RadiaSoft is rapidly expanding its products, partnerships, and personnel. We are excited to have Dr. Edelen’s talents in this position to provide ongoing leadership as we continue to grow and innovate.
RadiaSoft is happy to announce that Dr. Jonathan Edelen has been named President. An experienced member of the leadership team, Dr. Edelen will take over the duties of President effective immediately. Dr. David Bruhwiler, formerly President and CEO, will continue in the duties of CEO.
Dr. Edelen will be taking over day-to-day operations of the company. He will be primarily responsible for business development, as well as continuing to serve as Principal Investigator for multiple DOE grants focused on particle accelerator and Machine Learning technologies.
“My vision for the company is sustainable growth as we develop innovative products and build partnerships with organizations of all sizes,” Dr. Edelen said. “I look forward to expanding our technical expertise into new areas of science and new application spaces.”
“I’m confident that Jon is the right person for this position,” said Dr. Bruhwiler. “He’s been an integral part of RadiaSoft for five years and has shown himself to be an excellent leader and innovator. The Board of Directors was unanimous in their decision to make this move and we look forward to working with him in this new role, while I focus on my responsibilities as CEO.”
Dr. Edelen is both an accomplished scientist and an experienced businessperson, with a broad range of experience in accelerator physics, Machine Learning, and business development. He studied Applied & Computational Mathematics at Johns Hopkins University, was awarded Master’s and Doctoral degrees from Colorado State University, and was the Bardeen Fellow at Fermilab.
Meet Your Research Scientist: Dr. Christopher Hall
Meet the RadiaSoft team in this ongoing Q&A series, where we introduce you to our stellar employees. Learn about their work, their background, and some of the things that make them who they are. Today, Dr. Christopher Hall talks about his multi-faceted job at Radiasoft, a few of the interesting projects he’s worked on, and some things he wishes he more people knew about accelerators.
What do you do at RadiaSoft?
I am a research scientist, primarily focused on particle accelerator design and optimization.
What’s your educational and career background?
I have a B.S. from Hope College in Holland, Michigan. As an undergrad I worked on analysis of nuclear physics experiments to study high unstable isotopes (primarily Lithium-12 and Lithium-13) along the neutron drip line. I have a M.S. in physics from Michigan State University where I worked on design of the REA3 beamline. Finally my Ph.D. is from Colorado State University and my thesis work involved experimental analysis on the impact of coherent synchrotron radiation in the Jefferson Lab Energy Recovery Linac.
What’s the biggest misconception about your field and why?
I think one of the biggest misconceptions in the general public is about the relative prevalence of particle accelerators and their uses. The Large Hadron Collider consumes so much media attention that most people are not aware of other accelerators, like synchrotron light sources, that are used frequently in applied research.
Where did you grow up?
Until I left for college in Michigan I lived in a small town called Pataskala in central Ohio.
Before joining RadiaSoft, what’s the strangest or most interesting job you held?
When I was first starting my master’s degree at Michigan State University I did a short stint in a biophysics group. I got to work in a clean room which was interesting until I was starting to be trained on making and using piranha solution for cleaning experimental equipment. It was at that point I decided I needed to find a different research group, and ultimately ended up working for an accelerator physicist.
Who is your favorite scientist from history and why?
I don’t know if he is ‘from history’ yet but I like Andre Geim for being the only person to have, so far, won both Ig Nobel and Nobel prizes.
Tell us about one of your current projects.
I work on a wide variety of projects currently. One of the main main projects, that I am the Principal Investigator for, is focused on using a particular variety of neural network called a variational autoencoder to better analyze data from beam position monitors in particle accelerators. Beyond that I also help with a variety of projects with design optimization elements, and maintain a Python library for orchestrating optimization and data generation called rsopt.
What is a talent, secret superpower, or fun fact about yourself that people wouldn’t guess?
I really like Futurama and I find their slogan amusing.
What’s your favorite Slack emoji and why?
The Futurama logo! I have a poster of it hanging over my desk.
What’s something you wish people understood better about RadiaSoft or Sirepo?
Radiasoft’s Sirepo platform is a really great tool for using a lot of different scientific codes, many of which have complicated command line interfaces. You can get even more out of Sirepo if you are willing to switch back and forth, because both command line and GUI have their strengths. Recognizing this and learning to use each can make many tasks a lot easier than if you stick to just one or the other.
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Meet Your Senior Research Scientist: Dr. Jonathan Edelen
Meet the RadiaSoft team in this ongoing Q&A series, where we introduce you to our stellar employees. Learn about their work, their background, and some of the things that make them who they are. Today, Jon Edelen, talks about his multi-faceted job at Radiasoft, a few of the interesting projects he’s worked on, and some things he wishes he more people knew about accelerators.
What do you do at RadiaSoft?
All sorts of things. I work on everything from helping to manage the company to supporting experimental efforts at national labs and at our sister company RadiaBeam. On the technical side, I work on RF controls systems, signal processing, and machine learning for accelerator diagnostics and controls. With the latter, I focus on anomaly detection, which is what we call trying to find faulty behavior in the instrument. I also model particle accelerators and have a particular interest in electron sources, including thermionic cathode physics.
What’s your educational and career background?
I graduated from Rensselaer Polytechnic Institute in 2009 with a Bachelor’s degree in Electrical Engineering. I did two years of magnetic signatures modeling for submarines before heading to graduate school at Colorado State University. I worked on design and optimization of the CSU Free Electron Laser (FEL) and on the physics of electron back-bombardment in thermionic cathode RF guns. After graduate school I was selected for the Bardeen Fellowship at Fermilab, which got me into the RF controls group and into working on RF modeling and combined RF/beams simulations. I also participated in high-power commissioning of a new radio frequency quadrupole (RFQ) and medium-energy beam transport system. In 2017, I started at RadiaSoft where I started developing symplectic space-charge algorithms and modeling of field emission in thermionic energy converters.
What’s the biggest misconception about your field and why?
There are a lot more particle accelerators out there than people think. There are the big facilities like CERN or SLAC, but accelerators are used in all kinds of industry applications, from medical diagnostics to food safety. They come in all sizes, too, from kilometer-scale instruments to tabletop devices.
Where did you grow up?
I grew up in a small town in Connecticut. We had a tiny high school class of 60–Everyone knew everything about everyone else. Small town life, eh?
Before joining RadiaSoft, what’s the strangest or most interesting job you held?
When I worked for the Navy I used to participate in sea trials for submarines and surface ships. It was kind of a fun experience being part of the analysis team. Usually there was only a 2-3 day window to do everything we needed to with the ship / boat and get them on to the next thing, so the schedules were tight.
Who is your favorite scientist from history and why?
Lenhard Euler is a favorite. He’s known for the Euler Equation, which addresses complex numbers and is critically important for advanced physics and mathematics. We use this equation almost daily in anything that involves electromagnetic fields, which is most things in particle accelerator science. Georg Cantor is another honorable mention. Cantor proved that there are both countable and uncountable infinities, which is a splendid thing to think about.
Tell us about one of your current projects.
I am continuously working on a ton of different projects. I am leading the design and commissioning of a RF control system for a C-Band LINAC. I am the Primary Investigator for an SBIR-funded project for building machine learning tools for large accelerator facilities, with the focus split between anomaly detection and control systems. Some of my other projects include optimizing an electron LINAC for a high efficiency Free Electron Laser experiment, helping to integrate hysteresis models in a magnetostatics simulation code, and working on a currently-languishing paper on the variation in the work function on thermionic cathodes.
What is a talent, secret superpower, or fun fact about yourself that people wouldn’t guess?
I know the secrets of farm-stand produce fraud! When I was in high school, I worked on a farm during the summers. The farm had a produce stand that sold peaches from Georgia. I spent a lot of time peeling the “Georgia” stickers off the peaches so that we could sell them as “Connecticut peaches,” which are not a real thing.
What’s your favorite Slack emoji and why?
Scuttleberg!!!! Because who doesn’t love a dancing scientist crab?!
What’s something you wish people understood better about RadiaSoft?
We’re known largely for our software development and our expert consultants for particle accelerators, but we also have a unique expertise in plasma physics, vacuum nanoelectronics, FPGA’s, and machine learning. We do much, much more than GUI’s.
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Brightness in Undulators: Challenges & Opportunities
The Importance of Brightness in Undulators
X-ray light sources are a key instrument for scientific discovery in disciplines ranging from biology to materials science to organic chemistry. These light sources are powered by undulators and are used, among other purposes, to examine the ultrafine structures of samples that are under investigation. The ability to measure such fine structures necessitates a high signal-to-noise ratio in the X-ray beam. The beam’s brightness dominates this signal-to-noise ratio, making that brightness a fundamental figure of merit for the undulator. Brightness is such an important feature that the ability to accurately calculate it determines how far beamline designs can advance.
Current and Potential Brightness of Light Sources
The light sources currently in use show wide variation in brightness and photon energy, with significant improvements anticipated as devices and facilities are updated. Figure 1 (at the top of this article) shows that the brightness of beams produced should increase by two orders of magnitude in the near future. Reaching these hoped-for brightness levels will require overcoming serious difficulties that are rooted in both the beamlines and their sources.
Challenges to Increasing Brightness
One of the challenges to increasing brightness in the beamlines is that their complicated behaviors require high-level mathematics to predict, which in turn requires training and retaining people who can do that work. Brightness calculations require knowing the emitted radiation of the undulator, the photon beam size and divergence, the electron beam energy spread, the photon flux, etc. The math gets twisted pretty fast. Photon flux, for example, is represented by the formula in Figure 2.
Figure 2. Photon Flux Calculations
The components of the photon flux formula are themselves complicated. The formulas for , , , and and are demonstrated in Figure 3.
Figure 3. Additional Calculations
The shifting variables and nested complexity of these mathematical formulas make them difficult to work with or discuss in depth in a blog post. (The references listed at the end of this article provide details for further study). For the purposes of this piece, it’s enough to recognize this barrier to advancement in the field.
Moreover, light sources themselves are enormous and intricate devices; they can cover acres of ground and have thousands of delicate moving parts that require indescribably-minute adjustments in real time. These adjustments must be done by a phalanx of highly-trained engineers and support staff. Suffice to say that any experiment run in such devices is enormously difficult and expensive. Scientists will need to find pathways to lower barriers in both the beamline calculations and the light-source generation in order to continue advancing the field.
Opportunities for Lowering Barriers
The value of high-stakes scientific experiments is, of course, in their complexities. It is neither possible nor desirable to remove them. Lowering barriers to navigating them should be a focus among the community.
The obvious solution is to use software and computational power to handle the calculations and model the behavior of the beamlines and light-source devices. Modeling instead of executing “real” experiments reduces the expense and increases accuracy by orders of magnitude. The sticky bit is that these software programs can be challenging to learn, hard to use, and difficult to share. To truly lower these barriers, another boost is needed. Bringing the simulation codes into a browser or other GUI-based platform can be that boost.
Wrapping the legacy codes in a GUI makes them accessible to scientists and engineers at all stages of their careers. Removing the necessity of learning command-line operations means that any of the multiple codes suitable for X-ray brightness calculations can be used with equal ease. “Drag and drop” inputs make adjustments fast and cheap and multiple iterations easy. GUIs also provide greater visualization capabilities as well as ease of sharing between collaborators.
Using GUI technology to facilitate calculating and modeling tasks is not a silver bullet that removes all barriers. But it is a powerful tool that should be recognized and utilized as scientists seek to achieve the ever-brighter beams that are needed.
Conclusion
Synchrotron radiation production is a hugely important engine for scientific productivity, with applications ranging from basic science to medicine to industry. It is and will continue to be ever-more important to lower barriers both to accurate calculations of brightness and modeling of beamlines in order to advance the development of light sources. Browser-based or GUI technology can lower barriers dramatically and will be an ever-bigger part of the progress in the field of beamline generation. Improving our ability to efficiently quantify the performance of light sources will bring a bright new tomorrow to the light-source community.
References & Resources
The information in this article is drawn from several sources. Please see the references below for further information.
Nash, O.Chubar, N. Goldring, D.L. Bruhwiler, P. Moeller, R. Nagler and M. Rakitin, “Detailed X-ray Brightness Calculations in the Sirepo GUI for SRW,” AIP Conference Proceedings2054, 060080 (2019); https://doi.org/10.1063/1.5084711
Nash, O.Chubar, D.L. Bruhwiler, M. Rakitin, P. Moeller, R. Nagler, and N. Goldring “Undulator radiation brightness calculations in the Sirepo GUI for SRW,” Proc SPIE 11110, 111100L(2019); http://doi.org/10.1117/12.2530663
M.S. Rakitin, P. Moeller, R. Nagler, B. Nash, D.L. Bruhwiler, D. Smalyuk, M. Zhernenkov and O. Chubar, “Sirepo: an open-source cloud-based software interface for X-ray source and optics simulation,” Journal of Synchrotron Radiation 25, 1877 (2018); https://doi.org/10.1107/S1600577518010986
K.-J. Kim, “Brightness, coherence and propagation characteristics of synchrotron radiation,” NIM A 246, p71 (1986)
This work is supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, under Award #DE-SC0011237
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Meet the RadiaSoft team in this ongoing Q&A series, where we introduce you to our stellar employees. Learn about their work, their background, and some of the things that make them who they are.
Today, Evan Carlin, talks about software development at Radiasoft, some interesting projects he’s worked on, and a few of software problems that he’s solved.
What do you do at RadiaSoft?
I solve problems by writing code. Most of my time is spent working on the Sirepo framework. I have worked on projects like adding the ability to run simulations on the Cori supercomputer at NERSC and allowing users to dynamically compile the FLASH code through the Sirepo interface. I also help with some other software development such as deploying a server running NVIDIA IndeX and setting up MongoDB and sirepo-bluesky on our Sirepo Jupyter server.
Outside of programming, I’m involved in the Social Justice Committee where we try to make RadiaSoft and the larger physics community more inclusive.
What’s your educational and career background?
I went to college in Tacoma Washington at the University of Puget Sound. I wanted to study economics, but the first 15 minutes of my introductory economics class taught me that I did not enjoy economics. That semester I was also in a Computer Science 101 course and I was hooked from the first assignment: modifying a Java program to move a turtle around the screen. After college, I worked at a consulting company doing a variety of programming projects. I then worked at Google, trying to improve customer support experience and internal tools.
What’s the biggest misconception about your field and why?
That you need to be good at math. Computer science departments are sometimes found inside of math departments and many people think you need to be good at math to excel at programming. Both fields share problem-solving and abstract-thinking skills, but you do not need to know much about math for most programming jobs. I took one math class in college, and I only took it so I could take a calculus-based physics class. If you like solving puzzles and don’t mind staring at a screen for hours on end, then you may be a good programmer.
Where did you grow up?
I grew up in RadiaSoft’s hometown, Boulder, Colorado. I spent my entire childhood there except for when I lived in Padova, Italy when I was 12.
Before joining RadiaSoft, what’s the strangest or most interesting job you held?
In college I worked in a “keychain factory.” Really, it was the basement of a house near mine with a long wall of tables stacked with boxes of keyrings and car-logo medallions. It was a great college job because I could just show up and work a few hours whenever I had time. The downside was that making keychains is about as exciting as it sounds, and it really hurts your fingernails after a while.
Who is your favorite scientist from history and why?
They aren’t technically scientists, but Adam Savage and Jamie Hyneman. They’re the cohosts of the TV show MythBusters, and were my earliest science influencers. A lot of what they did on their show used the scientific method and taught me how to break a problem down to understand it. I would love to someday own a shop like theirs and spend my time tinkering.
Tell us about one of your current projects.
I am working on a project called NDVIZ. The goal is to use 3D-visualization software to interact with very large datasets. I’m getting our software deployed so our collaborators at Oak Ridge National Laboratory can try it out.
I spent more hours than I care to admit trying to create a working Docker image [a piece of software that acts like a template for building new applications] that could run the service and properly visualize the data using NVIDIA IndeX. I had the container running and IndeX was reachable, but the data that was visualized was completely black instead of lit up. I tried twiddling all sorts of parameters, using different GPU drivers, manually building different software, and even reaching out to folks at NVIDIA. Ultimately the answer, as it usually is, was in the code. There were some example Dockerfiles that showed how to properly build an image and once I adapted them everything worked. I learned a good lesson, I should’ve taken some time to explore the code I was given before jumping in and solving the problem how I thought it should be solved.
What is a talent, secret superpower, or fun fact about yourself that people wouldn’t guess?
This might be a talent, a superpower, or just completely useless depending on who you ask, but I can get anyone who is willing to try to enjoy country music. People have been scarred by the pop-country on the radio. Once you dive in and explore country music, you’ll realize that it is a vast genre. Recently I’ve been listening to a lot of James Hand. His voice and style of play are not the most accessible but his lyrics are haunting.
What’s your favorite Slack emoji and why?
My favorite is the man dancing. I like to use it instead of thumbs up when I’m happy or in agreement with something.
What’s something you wish people understood better about RadiaSoft?
I wish people knew that even though the main focus of RadiaSoft’s work is particle physics, we are solving many difficult software problems, too. For example, we have written a distributed job-management system that uses asynchronous Python. We learned many interesting bits about Python through that project that I would like to someday share with the larger software community.
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Meet the RadiaSoft team in this ongoing Q&A series, where we introduce you to our stellar employees. Learn about their work, their background, and some of the things that make them who they are.
Today, Rob Nagler talks about the strategy of software programming, some interesting projects he’s worked on, and a few of the many companies he’s founded.
What do you do at RadiaSoft?
I help people with software and hardware. The software might be accounting systems or how to use a new tool. I also try to eliminate bottlenecks for the rest of the software team. Programmers should be programming, not dealing with license or hardware issues.
What’s your educational and career background?
I took my first computer course when I was nine years old, back when computers filled up entire rooms. I taught myself how to build electronics and software programs until high school when I took my first Basic and Fortran classes. I got degrees at UCSD and Stanford in computer engineering.
Although my focus is primarily software, I have been involved with hardware throughout my career. I’ve worked for big and small companies, but mostly, I’ve worked at startups, many of which I founded.
What’s the biggest misconception about your field and why?
People think programming is difficult. In many ways it is, but it often comes down to solving simple problems in a structured and specific way. I think the “structured” and “specific” parts are what trips people up. Sometimes they give too explicit instructions, and other times the instructions are too vague. Getting it Goldilocks-right is about taking the time to find the simplest way of talking about a problem. When this happens, the software writes itself.
Where did you grow up?
I grew up in East Meadow, NY. It’s a small town on Long Island with many Levitt homes, where most people would commute by train to work.
Before joining RadiaSoft, what’s the strangest or most interesting job you’ve held?
Over the years I’ve started 15 or so companies. Many of these startups operated concurrently so at times I wore (and still wear) different hats. I’ve had to act as a fiduciary for one company while negotiating with another company I owned.
One of my more fun startups was a nonprofit designed to encourage kids to ride their bikes to school. At one time 50 schools were running the program. Technology was involved: the kids had RFID tags on their bike helmets, and a solar-powered RFID reader was installed at the schools to count them as they arrived.
Who is your favorite scientist from history and why?
While there are interesting historical computer scientists, I prefer thinking about the people I’ve worked with who are not famous such as David Cheriton, Tom Lyon, Paul Moeller, Roger Sumner, and Ion Yadigaroglu. These people are my favorites because of how they influenced me and shaped my career.
Tell us about one of your current projects.
I am working on improving our accounting at RadiaSoft. I like this project because I can solve a problem for people without a lot of complex software. The problem has a lot of moving parts, but the solution is relatively simple and eliminates most of the need for manual entry. Not only is manual entry tedious for people, it’s error prone. Now, we can take our data from our timekeeping system, generate some inputs to Quickbooks, Paychex, and Excel, and eliminate hours and hours of manual entry from one system to another. It makes me happy to solve a direct problem for someone.
What is a talent, secret superpower, or fun fact about yourself that people wouldn’t guess?
Fun fact: I lived in Switzerland for 12 years. I was not the type of person to travel after college. Rather, I just went to work. A friend of a friend needed some help running a software company in Zurich so I hopped on a plane after a couple of phone calls. I had never been to Switzerland before. I didn’t even have a visa, which resulted in a rather sticky situation with the Fremdenpolizei.
What’s your favorite Slack emoji and why?
I don’t like emojis. Bah humbug. I am old fashioned, and I use emoticons. 🙂
What’s something you wish people understood better about RadiaSoft?
We are a small company with many different projects, which can be quite complicated to manage. While our project deliverable is usually a research paper, we try to make sure we also add some features to our flagship product, Sirepo. Doing this benefits the larger scientific community because Sirepo is open source. In this way, we’ve been able to grow Sirepo from a simple application to the rich, multi-faceted scientific gateway it is today.
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Research Spotlight: Averaged Invariants in Storage Rings with Synchrotron Motion
When we design a storage ring particle accelerator, we start from certain basic assumptions that are relaxed as we make progress in the design.
We begin by looking at the betatron motion—the horizontal and vertical oscillations that arise from alternating gradient strong focusing. This motion will tell us whether we can stably store a beam. We also look at synchrotron motion—the longitudinal oscillations of the particles in the beam because of time-of-flight dependence on energy and the radiofrequency cavities that accelerate the particles. One of the assumptions we start with is that the betatron and synchrotron motion is independent, or that they are uncoupled.
The benefit of assuming that they are uncoupled is that it allows us to start with three one-dimensional problems instead of one three-dimensional problem. However, they aren’t actually uncoupled. Betatron motion can be linearly coupled in, for example, circular optics or modified with nonlinear dynamics, such as with nonlinear integrable optics. Chromaticity—the dependence of the betatron frequency on the particle’s energy—also can couple the synchrotron motion with the betatron motion, so-called synchro-betatron coupling. This is a coupled nonlinear system that can lead to chaotic dynamics, emittance growth, and other bad things affecting the quality and lifetime of the beam.
Averaged Hamiltonians and Toy Models
Spurred by RadiaSoft’s collaboration on the FAST/IOTA project at Fermilab, we looked into if some sort of general statement can be made about the synchro-betatron coupling.
When we study the long-term dynamics in a storage ring, like the IOTA ring, we want to study the single-turn map. The single-turn map tells us, given some initial position of a particle, what its final position will be after going through the ring once. It turns out, the single-turn map contains all the dynamics of the accelerator, and analyzing it will let us make long-term predictions about the behavior of a beam of particles.
The dynamics of particles around a storage ring are described by a Hamiltonian which generates the single-turn map. This can get deep into the world of symplectic maps and Lie algebras, but the big picture idea is that if we can understand the dynamics of that Hamiltonian, we can understand the dynamics of the particles in the ring, whether the trajectories are stable or not, and so on.
To do this, RadiaSoft scientists first computed an analytic model to predict an averaged Hamiltonian for a particle accelerator ring. The theoretical calculation is high-level and generic, and in practice is probably tricky to calculate anything concrete for a real accelerator. But it does show that, while we might not be able to compute an averaged Hamiltonian in practice, it at least in principle exists. That existence is enough to suggest various stability properties of the accelerator.
What we found is that, outside of resonance issues that arise from the synchrotron tune being a rational number, we can compute a period-averaged Hamiltonian that tells us something about the average motion of the particles over many synchrotron oscillations. We lose some short-time wobbles and wiggles in the trajectory, but we can say broadly whether the dynamics will be well-behaved or not by looking at this averaged Hamiltonian.
To confirm our computation, we built a toy model of a nonlinear ring with synchrotron motion. This was straightforward to implement in a Jupyter notebook. We used a toy 1D single-turn map Hamiltonian that includes chromaticity, linear focusing, and an octupolar nonlinear term for the transverse dynamics, and a thin RF cavity and linear momentum compaction for the longitudinal dynamics.
So the model includes integrable nonlinear transverse dynamics (but no chaos since the octupole term is included as a constant-focusing term), as well as nonlinear synchrotron motion through a nonlinear RF cavity potential.
While the model may not be perfect for a particle accelerator, it lets us compute something with pen and paper and compare it to the simulations.
We found that our averaged Hamiltonian is very well-conserved in our toy model with linear synchrotron motion. In this case, we computed the averaged Hamiltonian analytically, because our simple model allowed that.
We then compared this averaged Hamiltonian, turn-by-turn, with the unaveraged perpendicular Hamiltonian—that is, the Hamiltonian that contains all the chromatic terms and transverse dynamics, but not the synchrotron motion. What we found is that the perpendicular Hamiltonian has a periodicity with the synchrotron motion, suggesting the existence of some underlying invariant. We also found that the averaged Hamiltonian is very close to invariant over the synchrotron period, suggesting it is that invariant.
Comparison of the conservation of the turn-by-turn transverse Hamiltonian, and the synchrotron period-averaged Hamiltonian.
We can also compare this near-conservation to the synchrotron period directly.
The conservation is in the so-called normal coordinates (see, e.g., É. Forest for a discussion of general linear normal forms) of the linear synchrotron motion, so when we extend to the nonlinear motion we don’t expect conservation since we have not computed the nonlinear normal forms.
In a perturbation theory sense, computing the linear normal forms transforms the linear oscillations to a constant phase advance, so when we look at added nonlinear effects we haven’t canceled out sideband effects and we expect to see oscillations at harmonics of the fundamental. When we add nonlinear synchrotron motion, to account for the curvature in the RF cavity fields, we find oscillations in the invariant commensurate with twice the synchrotron period. This gives us a tell-tale signature that some invariant exists, but we aren’t computing the full normal coordinates, which can be hard to impossible to do effectively for more realistic systems.
Nonlinear side-bands in the linear normalized coordinates at approximately double the frequency of the synchrotron motion, as we might expect.
Signs of a Hamiltonian in a Real System
Now that we have an intuition for what will happen if we try to apply this reasoning to a complex system, we looked at an integrable Rapid Cycling Synchrotron design developed by Jeff Eldred at Fermilab.
We used Synergia to track the single-particle dynamics, and analyzed the on-momentum invariants for the characteristics we expected from our toy model. Sure enough, we see oscillatory, but bounded, behavior in the invariants that oscillates with the synchrotron period. This bounded behavior suggests there’s an underlying period-averaged Hamiltonian like the one we computed for the toy model, and that the averaged trajectories are integrable, even with the synchrotron motion.
Comparison of the Danilov-Nagaitsev invariants to the synchrotron motion. The periodic behavior is similar to what we observed in our toy model.
We found a periodic bursting behavior in the non-conservation of the Hamiltonian and second invariant of the lattice as computed in the usual on-momentum linear Twiss coordinates we might be familiar with for computing Courant-Snyder invariants. The periodicity suggests the existence of an underlying invariant, as simply averaging H over the period shows that there is not some spurious drift. The bursting in non-conservation coincides with the particle going from positive to negative energy offset. This corresponds to going to a region where the vertical and horizontal chromaticities are approximately equal, which permits exactly integrable dynamics for off-momentum particles, and a region where the chromaticities are not equal, but this is a subject for another paper.
Conclusion
This computation has strong implications for future nonlinear integrable optics accelerators like the iRCS we studied in this paper. A big concern was what synchro-betatron coupling will do to the integrability of nonlinear integrable optics, and in this paper we offer a mathematical treatment to better understand that question.
Our initial studies suggest that the dynamics will remain regular and well-behaved over long times, and that while we should look out for what synchro-betatron coupling could do, it’s not an immediate show-stopper. It also suggests a path forward for better understanding synchro-betatron coupling in general.
Read the full paper on arXiv here or check out the JINST publication here.
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Curious about our team of software developers at RadiaSoft? You can meet them, one-on-one, in this ongoing Q&A series. Every month, we introduce you to another member of the RadiaSoft team and they tell you about their work, background, and some of the things that make them who they are.
Today, Paul Moeller talks about the advice he’d give to software developers, the new machine learning app he’s working on, and the thing he wants most from Sirepo users.
What do you do at RadiaSoft?
As a principal software developer, I work on Sirepo. That entails bringing various scientific codes to our browser-based framework and putting nice user interfaces on them. Almost all of them are open-source; some scientists created this software themselves or it passed through time with different people maintaining it. (For example, Zgoubi, a Fortran code, has been around since the ’70s and that’s one of the ones Sirepo supports.)
These scientific codes are out there for anyone to get, but it’s really hard for a regular person to actually run them. They’d have to get it to their computer, compile everything, and make it work. You might have to write a file and feed it into it, for example. We at RadiaSoft try to take all of that information out and build a schema for it, which is like making a map of all the inputs for a code and then arranging them to make logical sense and build a user interface for it. The nice thing about what we do, what Sirepo does, is make it so users don’t need to go through all that, they can just run these codes from their browsers.
What’s your educational and career background?
I always knew that I wanted to study computer science, even when I was in high school. I enjoyed working on software. So I went to a small undergrad called Clarke College in my hometown of Dubuque, Iowa. I also had a music minor in undergrad. Then I got my master’s degree at Loyola University in Chicago and got a job right after that in Chicago. I went to work for a company that wrote software for manufacturing, accounting, and logistics, similar to SAP and Oracle. I worked there for four years, then got married and moved to Boulder, Colorado.
I knew I wanted to live in Boulder because I had a sister who lived here for a while and always thought it was a great place. My wife went to CU then. I met Rob Nagler, our CTO. and joined him at Bivio Inc., and the company evolved into a consulting services business. We met David and that’s how we joined RadiaSoft. This is all over a 20 year span.
What’s some advice you’d give to other software developers?
In general, computer scientists and developers get caught in the language and the technology being used rather than the problem they are trying to solve. You can’t have one hammer that hits every nail. There’s a big advantage to using different technologies for different issues, but also understand that the technology you’re using today will be very different or even obsolete in a few years. Remember that clients often come to you with the technology they want, rather than the problem they have.
Before joining RadiaSoft, what’s the strangest or most interesting job you’ve held?
Back in 1990, when I was in undergrad, I spent the summer working for Central City Opera as a festival staff member. A friend of mine from college and I traveled out to Colorado from Iowa and mopped rehearsal floors, picked up garbage, manned phones, and ushered the performances—all for $25 a day. As an usher I saw around 10 performances of La Traviata, Cosi Fan Tutte, and The Merry Widow each.
If you could invite a pioneer from your field to dinner, who would it be and why?
I would invite a young Steve Jobs, when he was young and crazy, right after he left Apple and when he was doing stuff with Pixar and Next. That would be an interesting time in his life to have a conversation. I really like his approach to making products because they are superior to their competitors. I’d be very curious to figure out what it is about him and how he makes decisions that result in such amazing products. That’s really the million dollar question.
Tell us about one of your current projects.
One project I’m working on is called Webcon. One of our senior research scientists, Jon Edelen, is heading that one up. It’s making a web app that lets you classify or apply machine learning to a dataset. It is a general purpose app which is kind of neat, as opposed to a lot of the work we do which is very specific, related to a beamline or a particular machine.
In Activait, you can upload a bunch of data, select what parts of the data you’re interested in, visualize it, partition it, and classify it. You can discover information from the raw data through classification and correlations.
In general, Activait can be used with any sort of data. It’s not only accelerator-science specific. There was one dataset I was looking at which tried to figure out whether you have diabetes or not. There were lots of inputs, like glucose levels. You let the machine learning just crunch away at it and predict. Machine learning is new to me and it’s an exciting project. It’s a product that’s live, but that we’re all actively working on. So it’s going to get better and better.
What is a talent, secret superpower, or fun fact about yourself that people wouldn’t guess?
I run a lot. If I’m not working, I’m most likely running. On weekends, I run short races, virtual races, these days. In the summertime, I’ll run 50 miles a week, fewer in bad winter weather. It’s definitely something I enjoy, but even five years earlier I would have said it was soccer. But eventually I got old enough that I thought, it’s better just to run rather than put myself in harm’s way.
What’s your favorite Slack emoji and why?
This is not one I use a lot, but one I like is the Walking the Dog emoji. It comes up a lot on our software team Slack channel because many people are walking dogs at various times. It’s what we use when we’re unavailable. Otherwise, I just stick to the thumbs-up emoji.
What’s something you wish people understood better about Sirepo?
I wish people understood that we’re always developing Sirepo and continually improving it. If you’ve used Sirepo and you think it’s nice, but it doesn’t do X thing. Let us know! I would love more feedback from active Sirepo users. It’s not a typical customer-service black hole with Sirepo. One of us will get back to you and you might even change the program for the better. We are totally open to supporting someone’s wishlist. Sirepo’s target customers are such a small portion of the world, it is something we can do. We’re very focused on a specific area, so the more buy-in we can get from people in those areas the better we can make the product for them.
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Meet Your Research Scientist: Stephen Coleman, PhD
Curious about our team of stellar research scientists here at RadiaSoft? We’ve got you covered—meet the team, one-on-one, in this ongoing Q&A series. Every month, we introduce you to another of RadiaSoft’s scientists and they tell you about their work, background, and some of the things that make them who they are.
Today, Dr. Stephen Coleman talks about how he got an Erdős-Bacon number, his electron cooling modeling project, and the misconception of physics as a monolith.
What do you do at RadiaSoft?
I’m a research scientist. I’ve been at RadiaSoft for about a year, which means I don’t have any of my own projects yet. Instead, I’m a worker bee on a couple of other projects. I skew to the computational side of things. Right now I’m working on a plasma simulation project called Flashcap, modeling plasma and capillaries for wakefield accelerators.
I’m also working on another project using the JSPEC app on Sirepo, which is for modeling magnetized electron cooling in accelerators. The JSPEC project is aimed at informing the design for the Electron-Ion Collider (EIC) at Brookhaven National Lab. An upcoming project will have me working with CU, here in Boulder, to build a magnetic focusing horn.
What’s your educational and career background?
I went to the University of Colorado (CU) as an undergrad for physics, then grad school in physics at the College of William and Mary in Virginia. I worked on an experiment called MINOS, which was a neutrino oscillation experiment with a beamline originating at Fermilab aimed at a detector in an iron mine in Northern Minnesota.
After grad school, I got a postdoc at CU. So I came back to Colorado and worked with one of my old professors on a different neutrino oscillation experiment called T2K, in Japan. It’s based out of an accelerator on the east coast of Japan that shoots neutrinos to a detector on the west coast of Japan. I did that for a few years, then got an interesting opportunity to work on a DARPA-funded project also at CU, across the street from where I had been working, but as something kind of like a biostatistician. It was a big career change there for a little while. I did that for about five years.
What’s the biggest misconception about your field and why?
I’m a bit of a generalist, not sure if I’d say I have a specific subfield of physics. I do skew on the computational side of things, but even within that area you can talk to someone about plasma modeling, you can talk to someone else about particle physics modeling, and they’re completely different worlds, even though it’s all physics, all computational.
I guess the misconception would be that it’s monolithic. If you talked to the average person on the street, they don’t know what a physicist does, day in, day out. They might think that you spend your time writing on a chalkboard. But it’s such a rich and varied collection of subfields, some of which align more closely than others. For instance, I have friends at NIST, just down the street from RadiaSoft’s office, who work with table-top laser experiments all day, and we talk past each other when we chat about work. It’s like we’re speaking completely different languages.
Even within accelerator physics, people don’t realize accelerators have industrial and medical uses, that it is not strictly collider-focused. It’s more than CERN, and Fermilab, and SLAC. There are real-world applications like irradiating food for sterilization or irradiating barrels of seeds to get rid of pests.
Where did you grow up?
I grew up just outside of Richmond, Virginia, in a suburb called Chesterfield County, which is on the south side of the James River that goes through Richmond.
Here’s a little fun tidbit: I have an aunt who does genealogy and she’s traced our family back many generations. Among our ancestors there are French Huguenots who sailed to the United States in the early 1700s, and that landed…on the south side of the James River, in what later became Chesterfield County.
Before joining RadiaSoft, what’s the strangest or most interesting job you’ve held?
When I was young, I was a bit of a theater kid and did plays and commercials and movies. (When I was 11, I was in a movie called Major Payne, which is a Damon Wayans’ military academy movie.) I do have an IMDb page. This acting experience means I am one of the rare people who has an Erdős-Bacon number.
Paul Erdős was a brilliant mathematician, very prolific, and he authored tons and tons of different papers with different people. If you can connect yourself—you were an author with someone who was an author with someone who authored a paper with him, then you have an Erdős number of three.
It’s a big thing among mathematicians and physicists to brag about your Erdős number, but at the same time, there’s also your Kevin Bacon number, if you’re an actor. The Erdős-Bacon number is where you add them together. My Erdős number is four and my Bacon number is three, so I have the same Erdős-Bacon number (seven) as Natalie Portman. Then, if you really want to get into it, there’s another number called the Erdős-Bacon-Sabbath number, if you recorded with someone who recorded with Black Sabbath. Not a lot of people with that. I suppose there’s still time for me to get that one. The Erdős-Bacon is rare enough.
Who is your favorite scientist from history and why?
I’d go with John von Neumann. He was a brilliant scientist who published a lot in math and physics. He was part of the Manhattan Project and eventually landed at the Institute for Advanced Study at Princeton, where he was basically given the freedom to do whatever he wanted. It’s amazing how much of our modern computing structures came from him, from him toying around with ENIAC, the existing supercomputer at that time. We owe Monte Carlo simulation to his physics and computational insights. There’s some quote from Hans Bethe about how von Neumann was evidence that there was a higher species of human.
Tell us about one of your current projects.
I’m working on a project called MCool that uses JSPEC to model the cooling of ion beams in the EIC. In the EIC, we want to keep the emittance down as the ion bunches cycle and speed up. You can do this by co-propagating an electron bunch with the ion bunch in a strong magnetic field.
It’s a tough project because there are only a few times that this technique has even been tried and there’s not a lot of agreement, even in the theoretical community, about how you determine what the magnitude of the cooling will be. But being able to computationally model that with any of the friction force models that there are, and being able to tell a computer to optimize for a given set of conditions, is going to be crucial to designing the new cooler in the EIC. Essentially, I’m working on one of the tools that will hopefully let us do that.
What is a talent, secret superpower, or fun fact about yourself that people wouldn’t guess?
I’m a decent musician and play a few instruments. I was in the CU marching band, playing trumpet. I can also play guitar and used to play upright bass in a jazz band.
What’s your favorite Slack emoji and why?
I like the Homer Disappearing Into Bushes we have on our Slack. The company as a whole is really collegial, so there’s little to no workplace drama at all. I think it’s funny to drop Homer Disappearing Into Bushes sometimes to pretend there’s a big controversy that I don’t want to be a part of.
What’s something you wish people understood better about RadiaSoft?
Between all the scientists we have on the team, we have both a lot of areas of collaborative overlap and a whole lot of unique specialties. We are representative of how diverse the field of knowledge is in real life. This means that we can draw upon institutional knowledge for problem solving, and can come up with creative ways to try to solve a problem that draws on experience outside of a particular sub-field. Basically, any problem a client might have, we have the right people to figure it out.
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In high energy physics, accelerator physics, and other science disciplines, simulation codes are a vital tool in the research arsenal. The codes often run through the command line, use specialized input files, and sometimes have their own build system.
Learning how to use these codes is hard and can take weeks or months. This means that despite being very useful, many of these codes have only a handful of expert users.
Sirepo was built to bring that kind of scientific computing to the cloud. It’s a gateway for those science codes to be used on a browser, to be accessed via graphical user interface (GUI). By wrapping these codes in GUIs and maintaining them on the backend, we provide a place for scientists to focus on their physics over IT and CS issues.
But even if you’re familiar with codes for accelerators or X-ray beamlines, you might still be thinking, what exactly is Sirepo?
We’ve put together this bird’s-eye view of Sirepo to answer that. Think of it as your crash-course.
In it, we’ll cover how Sirepo relates to various science codes (elegant, WARP, SRW, etc.); how it can help make running simulations easier; how you can share your models with colleagues; and more. Feel free to skip around, or if you want to see Sirepo in action, check out the tutorial video at the bottom of this article.
Want to jump ahead? Here’s where we’re going
What is Sirepo?
Sirepo is a digital cloud-based framework for running simulation codes in your browser. It is the software that houses the simulation code, which is why we call it a scientific gateway.
Under the umbrella of Sirepo are a number of apps. These are the codes that Sirepo supports with graphical user interfaces you can access via web browser. These apps all share a similar visual language and interface, making it easy for users to switch between different codes.
Take the popular physics simulation code elegant. Sirepo/elegant is an app that runs elegant in your browser without you having to download, install, or keep the code up-to-date. Through Sirepo, elegant is prepackaged and ready to be used.
We at RadiaSoft believe that science is made better by the free flow of information and tools. That’s why we built Sirepo in the first place, to aid the accelerator community. Therefore, like the codes it supports, Sirepo is open-source.
Simulation codes running on Sirepo
The physics codes Sirepo currently supports fall into three families: codes for particle accelerators, codes for X-ray optics, and codes with electrostatic PIC capabilities.
The library of supported codes on Sirepo is always expanding. Below we’ll get into a few of the most popular codes and what they do to give you an idea of how versatile the Sirepo gateway can be.
This is a particle accelerator code for electron linacs, synchrotrons, and much more.
According to its user manual, “elegant stands for ‘ELEctron Generation ANd Tracking,’ a somewhat out-of-date description of a fully 6D accelerator program that now does much more than generate particle distributions and track them. elegant, written entirely in the C programming language, uses a variant of the MAD input format to describe accelerators, which may be either transport lines, circular machines, or a combination thereof. Program execution is driven by commands in a namelist format.”
Its full name is Synchrotron Radiation Workshop and it’s an X-ray optics code for synchrotron radiation and coherent X-ray beamlines, such as what you find at the ALS or NSLS-II.
“Frequency-domain near-field methods are used for the SR calculation, and the Fourier-optics based approach is generally used for the wavefront propagation simulation.
“The code enables both fully- and partially-coherent radiation propagation simulations in steady-state and in frequency-/time-dependent regimes. Besides the SR applications, the code can be efficiently used for various simulations involving conventional lasers and other sources. SRW versions interfaced to Python and to IGOR Pro (WaveMetrics), as well as cross-platform library with C API, are available,”according to its GitHub repo.
OPAL or “Object Oriented Parallel Accelerator Library” is a particle accelerator code for linacs and electron guns with 3D space charge. OPAL is open-source and you can learn more about it here.
Synergia
This is a particle accelerator code for single or multiple bunch rings with 3D PIC. It’s a hybrid Python/C++ package. Learn more about Synergia here.
Originally developed in the 1970s, Zgoubi is a particle accelerator code for electron and ion spin dynamics in rings. Learn more about Zgoubi here.
Imports/exports
Sirepo is designed to aid both the beginner and advanced coder. While a GUI interface is beneficial in many ways, there may be operations and tasks a researcher wishes to carry out in the command line.
This is why there are easy exports to other file formats, like a simple zipped file or Python source file. (Accessible from the top menu bar in your workspace, pictured here.)
Similarly, one can import full lattice files for whichever accelerator they wish to model.
Making collaboration easy
Sharing and collaboration is not so smooth when done in the command line. First, software versions must be the same across all instances, or unexpected input file bugs can appear. Second, the files must all reach their destination intact and in full.
This sounds easy, but is harder to do in practice. And any mistakes mean a wrinkle in results or make a simulation impossible to reproduce.
Great science thrives in collaboration. Unlike traditional command line codes, a simulation in Sirepo is easy to share via link sharing.
Every simulation you create in Sirepo has a unique sharing URL, available in the top menu. Simply copy it and send to whomever you wish, and they will have access to an exact copy. It’s as easy as sending an email or a Slack message.
One thing to note is that Sirepo URL sharing is not like Google Docs. The link you share provides the recipient with a separate copy, not access to the original simulation. This way, you don’t have to worry about anyone altering your simulation.
A walkthrough of Sirepo
It’s important to remember that Sirepo itself is just the container for the codes above and others. Its features change with the codes’ own capabilities. But no matter which one you use, the interface will stay the same.
That said, the best way to understand Sirepo is to see it for yourself.
Welcome to our ongoing Q&A series where we introduce you to RadiaSoft’s stellar team and they tell you about their work, background, and some of the things that make them who they are.
Today, Dr. Boaz Nash talks about which scientists he’d invite to a dinner party, his work on synchrotron light sources and X-ray optics, and his recent art show.
What do you do at RadiaSoft?
I am a research scientist. This means I manage scientific projects and work on other people’s technical projects, mostly related to software and modeling for particle accelerators and synchrotron light sources. I’d say I have most experience with light sources. The interplay between the electron beam side and the X-ray or synchrotron radiation side is where I’ve done most of my work.
What’s your educational and career background?
I went to Reed College and studied math and physics. I worked on classical mechanics. We had to do a thesis and mine was a strange one related to transporting systems—trying to mathematically define what it means to take a system and move it and what aspects change about it when you transport it. I was able to make progress on the problem by relating it to the theory of adiabatic invariants. My thesis advisors were Nicholas Wheeler in physics and Thomas Wieting. I learned a lot from both of them.
After graduating, I spent a year programming computers in Portland, Oregon, for Richard Crandall at a consulting company called Perfectly Scientific. I did algorithm development and worked for Pixar on lossless image compression (using stills from A Bug’s Life) and solid noise generation, among other projects. It was there that I learned programming, mainly with C and Mathematica.
Then I got into graduate school at Stanford for physics. I didn’t know what I would focus on. However, at a physics open house event, I was introduced to Ron Ruth at SLAC and learned about accelerator physics. He introduced me to Alex Chao and I was asked to give a talk about my undergraduate thesis project. They liked my talk. Alex—who’s written a lot of textbooks in accelerator physics—is one of the people who has really helped to make particle accelerator science an academic discipline. I started working for him as a graduate student in 2000.
I liked that I could immediately make progress on different projects, whereas, if I did string theory or condensed matter physics, I felt like it might be a year or longer before I would be able to do useful work on problems. Accelerator physics had all of these really hard yet accessible problems, so there was stuff to be done on it. During my six and a half years doing my PhD, I worked on electron storage rings, a high energy electron beam, steered and controlled by specially designed magnets and RF cavities.
My work on electron storage rings led me to study synchrotron light sources and I took my first post-doc position at the NSLS-II at Brookhaven National Lab. After three years, I took a second postdoc at ESRF, another synchrotron light source, in Grenoble, France. After eight years at the ESRF, I returned to the U.S., and took my present position at RadiaSoft. In fact, I was originally hired at RadiaSoft to work on an X-ray optics grant, which was in collaboration with Brookhaven National Lab using the software SRW, working with the author, Oleg Chubar.
So, overall, there has been a certain continuity to my career. Through all of these transitions to different labs, I learned to use a number of different beam dynamics codes, such as Tracy, Elegant, Mad-X, and Accelerator Toolbox. I was always interested in creating community around these tools that are used at many labs internationally, and I helped develop a collaboration involving the Accelerator Toolbox code that is still active today.
What’s the biggest misconception about your field and why?
People tend to think that it’s a very applied field. It is, in certain ways, but it’s also very theoretical. If you are an accelerator physicist, you may work in an accelerator control room, but you may also spend a lot of time solving really hard physics problems. I think the misconception persists because of the way that accelerators are used.
In particle physics, for example, accelerators are just there to give you high-energy particles to “uncover the foundations of the universe, the structure of particles,” a very exciting notion. Other applications, like proton therapy for cancer or synchrotron light sources creating X-rays used for studying materials and biology, are also very visible to the public. This all means the actual accelerator science is often not seen because it gets a bit overshadowed by the applications and scientific results.
Where did you grow up?
I grew up in Santa Cruz, California.
Before joining RadiaSoft, what’s the strangest or most interesting job you’ve held?
Probably my programming job at Perfectly Scientific. It was in the basement of a house above Reed College called The Center for Advanced Computation.
Richard Crandall was definitely a character and a really smart guy. He partly owned a bar called the Lutz in Portland and was a Reed College professor. He passed away in 2012. At one point he had found the largest explicitly known prime number in existence (a Mersenne prime). At the time, you could buy a poster from Perfectly Scientific with all the digits of the prime number printed out in tiny black print, creating a gray mass of numbers, barely legible without a magnifying glass.
If you could invite any scientist, living or dead, to dinner, who would it be and why?
That’s a good question. I’d have to say the founders of quantum mechanics. I’d just love to be a part of some of those early debates on quantum mechanics with Einstein and Bohr. I’d love to talk with Paul Dirac (famously unsociable).
The people-side of scientists has always been really interesting to me and I’ve been slowly learning more about their lives. What has historically motived them? These scientists have created huge amounts of technical and mathematical work. How did they manage to do so much in their life?
Tell us about one of your current projects.
I recently had my beamline control project get funded, which I’m excited about. It’s the first project I came up with myself that got funding.
The way I see it, it’s a continuation of some of what I’ve learned about electron beams but applied to X-rays traveling down a beamline. The connection between the electrons and photons was a big theme of my work at ESRF. This project is creating something called an “online model” of an X-ray beamline to allow the scientists to have better control and understanding of their beamline in real time. The operators of particle accelerators would be lost without such a living, dynamic model. And I thought that some of the same tools could be applied to the photon beamline.
One difference between electrons and photons in light and radiation is the concept of coherence. Light coming from the sun or most light bulbs is incoherent. Lasers, however, are almost fully coherent. But synchrotron radiation is partially coherent. The new generation of light sources are increasing this coherence, but understanding partial coherence is still crucial. So, I’m working on how to describe that and understand the state of this beam passing down a beamline. This would allow scientists to know exactly what’s happening in their beamlines.
When beamline elements get misaligned, scientists can use realignment algorithms. That’s standard on the electron beam side but hasn’t really been applied to the beamlines. This project also involves application of machine learning, which has recently been very successful at improving control in particle accelerators.
Ultimately, I’d like to have software that allows beamline scientists to look at plots of what’s happening to their X-ray beamline through the mirrors, lenses, gratings, etc., and provide automated algorithms to better align things more quickly. It would save both time and manpower if we could detect problems or misalignments faster and fix them more automatically.
What is a talent, secret superpower, or fun fact about yourself that people wouldn’t guess?
I’m an abstract painter. I’ve been doing it since I was a kid. I’ve never been very professional about it, but I have a studio space and was in a show recently at the Avalon, which is like a dance studio with a big parking lot in Boulder.
It was a drive-thru art show. Art in the time of COVID! I had two parking spots: one for my car and one for my art. There were 60 other artists also showing—dancers, musicians, and lots of other stuff going on.
What’s something you wish people understood better about RadiaSoft?
That we’re filling a really important role in the particle physics / light source community with Sirepo. Some standard software in accelerator physics is incredibly hard to use. And the same software keeps being rewritten over and over again. So, when it gets beyond a certain complexity, you really need software engineers to work on it.
I think a lot of people recognize this issue with beam dynamics software, but most scientists with expertise in accelerators and light sources don’t have the time or resources to build software beyond a certain complexity. You can waste enormous amounts of time trying to set them up on your own and pass data between them and get reasonable plots out. Some of that is what you do for a PhD, but I think a lot of it is not that useful.
The Sirepo platform, which hosts nice interfaces for these codes, makes them easier to use, and being open source, allows for community development and collaboration across multiple institutions. I think it’s a really good service to the community.
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With all the news coming out of the varied worlds of accelerator and beam physics, and the research and industry applications they fuel, we’re putting together monthly news recaps to show you what you’ve missed.
As summer comes to a close, Fermilab has been chosen to lead one of the National Quantum Information Science Research Centers and SLAC scientists invent low-cost ventilators for COVID use, plus PPPL gains funding to advance diagnostics from DOE. Read on to get more on these stories and others.
“The U.S. Department of Energy’s Fermilab has been selected to lead one of five national centers to bring about transformational advances in quantum information science as a part of the U.S. National Quantum Initiative, announced the White House Office of Science and Technology Policy, the National Science Foundation and the U.S. Department of Energy today. . . .”
“Menlo Park, Calif. — Researchers at the Department of Energy’s SLAC National Accelerator Laboratory have invented an emergency ventilator that could help save the lives of patients suffering from COVID-19, the disease caused by novel coronavirus SARS-CoV-2. Using standard parts that cost less than $400, the ventilator could be an affordable option when more sophisticated technology is not available, in short supply or too expensive. . . .”
Next-Generation Electron Source Hits the Bullseye for Materials Studies from DOE Office of Science
“This novel lens design will enable the next generation of ultrafast electron sources. These sources will in turn enable new characterization of materials and molecules. They will fill the gap in spatial resolution between static and ultrafast sources. This ability will allow scientists to characterize chemical dynamics at nanoscale dimensions of billionths of a meter. . . .”
Controlling Light to Accelerate Electrons in Just Meters from DOE Office of Science
“Particle accelerators work by accelerating charged particles, such as protons or electrons, at speeds close to the speed of light. They then smash those particles into a target or other particles. High energy physicists study the particles and radiation released in these collisions. However, conventional radiofrequency accelerators are close to the limits of how much energy they can push to particles. Researchers are developing advanced accelerator concepts to reach new energy levels. The flying focus technology could transform accelerators. Because the new concept needs much less space, future accelerators could be many times smaller than the accelerators of today. . . .”
With all the news coming out of the varied worlds of accelerator and beam physics, and the research and industry applications they fuel, we’re putting together monthly news recaps to show you what you’ve missed.
In recent weeks, CMS scientists published their thousandth peer-reviewed paper, CERN makes a push to build the 100km High-Luminosity LHC, the RHIC reaches a 20-year milestone for colliding, and more.
“The discovery of the Higgs particle by the international CMS and ATLAS collaborations is the most famous discovery made to date at the Large Hadron Collider at CERN. The scientists made the announcement on July 4, 2012, and it was later recognized with a Nobel Prize: The theorists who predicted the Higgs mechanism received the award in 2013. . . .”
Running with the Speed of Science in the Race Against COVID-19 from DOE
“[S]cientists are studying three different types of problems: using simulations to understand the protein structure of the virus and how it attacks, utilizing artificial intelligence to identify effective countermeasures against the virus and accelerate the discovery of promising treatments, and working with policymakers to manage the course of the infection and deploy resources strategically. . . .”
“Even in a time before Instagram, physicists starting up the Relativistic Heavy Ion Collider (RHIC)—a particle collider at the U.S. Department of Energy’s Brookhaven National Laboratory—knew they needed a great picture to share their success. They and the rest of the world were not disappointed. Around 9 p.m. on June 12, 2000—20 years ago today—subatomic “fireworks” lit up display monitors in the control room of RHIC’s STAR detector . . .”
“CERN has taken a major step towards building a 100-kilometre circular supercollider to push the frontier of high-energy physics. The decision was unanimously endorsed by the CERN Council, the organization’s governing body, on 19 June, following the plan’s approval by an independent panel in March. Europe’s pre-eminent particle-physics organization will need global help to fund the project, which is expected to cost at least €21 billion (US$24 billion) and would be a follow-up to the lab’s famed Large Hadron Collider (LHC). . . .”
“Ever since 1960, when Theodore Maiman built the world’s first infrared laser, physicists dreamed of producing X-ray laser pulses that are capable of probing the ultrashort and ultrafast scales of atoms and molecules. This dream was finally realized in 2009, when the world’s first hard X-ray free-electron laser (XFEL), the Linac Coherent Light Source (LCLS) at the Department of Energy’s SLAC National Accelerator Laboratory, produced its first light. . . .”
Learn about RadiaSoft’s stellar team in our ongoing Q&A series. Today, we speak with Dr. Nathan Cook about his current research, the biggest misconception about his field, and more.
What do you do at RadiaSoft?
First and foremost, I am a research scientist. I spend a good amount of my time studying accelerator and plasma systems through grants provided by the Department of Energy and through customers at National Labs and universities.
Right now, my primary focus is on advanced accelerator concepts technology, which includes plasma accelerators. Those are technologies for accelerating particles over shorter distances to create more compact accelerators. I’m also actively engaged in research on small-scale devices for power generation. I do a lot of work with particle-in-cell and fluid codes to model these kinds of systems for a broad variety of applications.
I am also a group leader, which means I provide support, feedback, and guidance for a number of RadiaSoft scientists. Our group is colloquially known as the applied physics group.
What’s your educational and career background?
I received my undergraduate degree in math and physics from Williams College, then my PhD in physics from Stony Brook University in 2014. There I studied accelerator physics and worked at Brookhaven National Lab. That’s where I was first introduced to accelerators.
After graduating, I was anticipating moving to California, where my wife lived, but I was contacted by Stephen Webb, who’s another RadiaSoft employee, as well as a Stony Brook alum and friend. He encouraged me to apply to RadiaSoft. I did and joined in 2015.
What’s the biggest misconception about your field and why?
There is a tendency to think that accelerator or plasma physicists are only working on large collaborative international projects. That there’s only the LHC, or there’s only some large-scale project to study, and that most of the time we’re working to further this singular project aim. Certainly, that’s not been my experience, nor is it the case at RadiaSoft.
The reality is that there are myriad applications of accelerators, ranging from very very small-scale devices to produce electricity (thermionic converters, for example, are essentially reliant on accelerator technology) to small-scale light sources and detectors. In fact, a variety of applications across industries rely on small teams to produce small devices.
Where did you grow up?
I grew up in Columbia, Maryland, a suburb between Baltimore and Washington. It’s known for being one of the original planned communities.
Before joining RadiaSoft, what’s the strangest or most interesting job you’ve held?
The most interesting job I had was a part-time one in undergrad working for the Williams College website where I wrote about stuff going on around campus. Throughout my high school and early college education I was very interested in journalism—to the point that I considered becoming a journalist in lieu of pursuing science. I did a lot with my high school newspaper; I was editor for two years and did many other programs.
Williams is an interesting place because even though it was a small liberal arts school with a few thousand people in the middle of Massachusetts, they bring in all these artists, performers, and festivals. The cultural and community events were much more diverse and interesting than you’d expect.
Having the opportunity to write about different people, projects, and events helped me learn more about the community at a time that I was feeling very much out of my element.
Who is your favorite scientist from history and why?
This one’s really tough. I’d have to say Tycho Brahe. Partially because I was very interested in astronomy when I first started studying physics. Among astronomers he has a certain reputation because he was such a strange character with a larger-than-life personality.
Brahe was prominent in the late 1500s. This was a time when people still were resistant to the idea of planets orbiting the sun, but they were also starting to observe the motions of heavenly bodies and saw things that flew in the face of preconceived ideas. The Danish government gave Brahe access to an island off the coast of Denmark, which now is part of Sweden, I think. He built a castle, laboratory, and observatory and started doing science with a few other people, including Johannes Kepler. He also threw huge parties and got into a lot of trouble. I mean, he is famous for having lost his nose in a duel.
Yet Brahe still was producing some of the most accurate observations and measurements of the motions of bodies that had ever existed. His work was instrumental in enabling Kepler to form the three laws of planetary motion, which were essentially the foundation for Newtonian gravity. I find it fascinating that this person, who didn’t seem like your standard scientist, contributed so powerfully to the foundational elements of astronomy and basic physics principles in such a weird, tumultuous era.
I named my cat after him, and didn’t realize until later that Cat Tycho has a white nose, so it looks like he’s also wearing a false nose like the real Tycho.
Tell us about one of your current projects.
I’m working on a project called Flashcap, which is intending to model plasma systems for advanced accelerators. It’s very important to the community because it’s been recently demonstrated that by generating a controlled plasma density profile you can improve the quality of electron beams that are interacting with these plasmas by several orders of magnitude, potentially. (Several world records for energy and beam quality have been set by using specific types of structures called capillary discharge plasmas.)
I’m really excited because prior to this work there were very few tools out there to model these systems. It’s an opportunity for us to have a broader impact on the accelerator community by supplying them with a tool that addresses the needs of a really exciting technology. It’s already garnered positive feedback in the international community and from collaborators like Berkeley Lab, DESY, and scientists at the University of Strathclyde.
What is a talent, secret superpower, or fun fact about yourself that people wouldn’t guess?
I won a Kelloggs-sponsored cereal-eating contest in college. Anyone on campus could go to the dining hall and participate. I won. The prize was an Xbox, which was awesome to an undergrad.
The cereal was terrible, though. It was a promotional event, so they were doing market research on new cereals or something. I can best describe it as a cross between Cinnamon Toast Crunch and Frosted Flakes. It was a pain because not only was it overwhelming to eat that kind of cereal in large quantities but you really needed a good amount of milk for it not to hurt your mouth. It made for an extra layer of challenge.
What’s your favorite Slack emoji and why?
My favorite is a custom one called Coffee Slayer. It’s based on a character from a Japanese anime called Goblin Slayer. The character is a great mercenary who just goes around killing goblins because he has a lifelong vendetta against them. He does so with utter tenacity and focus to the point that it’s cliche, almost a meme. He never takes his helmet off, which is another meme.
So, you take this absurd character and put a little espresso cup in his hand and imagine that instead of spending all his time slaying goblins he’s just sitting in a café drinking coffee, which is hilarious. I also love coffee, so I like using it as a status for grinding away at your work while drinking coffee, which is something I think all scientists can appreciate.
What’s something you wish people understood better about RadiaSoft?
What people don’t understand is that we’re in a unique position at RadiaSoft where we can work with so many different people in the field and so many sub-disciplines within the accelerator, beam physics, and energy and nuclear physics communities. It’s very rare for us to work on a single project or a single aim for five years, but it’s commonplace for academia or national labs with long-term R&D.
We have to be a lot more agile and come up to speed on things and make a contribution and then move in a one- to three-year time scale. That, I think, is an underappreciated challenge and a really great and fun aspect of working at RadiaSoft.
Want to learn more about the RadiaSoft team? Visit our team page for full bios.
Synchrotron Radiation 101: How Light Sources Work and Their Applications
When a beam of electrons traveling close to light speed is bent away from a straight trajectory, it gives off a special kind of light is called synchrotron radiation.
Created by particle accelerators called synchrotrons, this kind of electromagnetic radiation has proved to be an incredible scientific tool for investigating matter, the universe, and so much more.
In this post, we’re exploring what synchrotron light is, how it’s produced, what it’s used for, and more.
How synchrotrons became light sources
First built in the 1940s, synchrotrons were not originally made to produce synchrotron light. As a type of circular particle accelerator, they were intended to study particle collisions and interactions. But it didn’t take scientists long to notice the synchrotron’s byproduct, an extremely bright light.
The first synchrotron to use the “racetrack” design with straight sections, a 300 MeV electron synchrotron at University of Michigan.
Called synchrotron radiation or synchrotron light, it can cover the full electromagnetic spectrum. It’s characterized “by high brightness—many orders of magnitude brighter than conventional sources—and [is highly polarized], tunable, collimated (consisting of almost parallel rays) and concentrated over a small area,” according to IOP.
More specifically, the power radiated from this beam is equal to(q2a2/c3), where a is the acceleration and q is the charge. This equation is called the Larmour formula, and it applies to radiation produced from both bending magnets with a circular trajectory and to undulators/wigglers, where the electrons oscillate back and forth.
(Natural sources of synchrotron radiation also exist in the universe. The Crab Nebula is one. But as you can imagine, it’s not useful for laboratory experiments. Though, it does tell us a lot about the plasma environment of space.)
It took more a decade after that first synchrotron was built, in 1956, for the first dedicated light source experiment to be carried out: an X-ray spectroscopy study by American scientists Diran Tomboulian and Paul Hartman. This happened at Cornell University’s accelerator when synchrotron light was directed off the accelerator ring towards an experimental station.
For more than 20 years, scientists utilized this synchrotron “byproduct” for their own work, while the machines themselves were primarily used for high-energy particle collisions. It was not until 1980 that the first dedicated light source facility was built in the United Kingdom at Daresbury. Today, there are dozens in use around the world.
Synchrotron Radiation Source (SRS), Daresbury Laboratory.
Light source setup
Particle accelerators are what create artificial synchrotron light, but we’re not going to cover those in detail here. (For the full rundown, see our previous post.) Instead, we will give a brief primer on some components unique to dedicated synchrotron radiation facilities.
EPSIM 3D/JF Santarelli, Synchrotron Soleil
Storage rings
These specialized facilities use storage rings to produce synchrotron radiation. Storage rings are exactly what they sound like: circular accelerators where electron beams can be stored and kept moving for many hours.
In fact, storage rings are synchrotrons. Unlike traditional ones that accelerate particles from low to high energies using radiofrequency (RF) cavities, however, these maintain beam energies and the RF cavities only replace energy lost during circulation.
Beamlines
As the electrons are circulating at the desired energy, they give off light. The direction this light moves depends on your frame of reference. In the laboratory frame, it travels in the same direction as the beam (with an opening angle of 1/ γ, where gamma is the relativistic time dilation factor); in the electron beam frame, it travels perpendicularly to the beam’s path.
No matter the frame, that light is allowed to escape the storage ring through ports leading to straight beamlines, which then end in experimental stations (pictured above). The available spectrum of this light depends on two things: (1) the beam’s energy and (2) the properties of the magnetic source (i.e. whether a bending magnet or an undulator or wiggler is being used). In practice, this means higher energy electrons allow for shorter wavelengths.
Typically, only a specific wavelength is desired for experimental use. In order to isolate it, a few mechanisms are used to condition the beam as it heads toward an experiment station.
One such tool is the monochromator. It “selects” a single wavelength of electromagnetic radiation with a narrow bandwidth.
For picking out X-rays, a crystal monochromator is used; for UV light, a grating monochromator is used. Slits control the physical width of the beam and the angular spread. Mirrors and lenses are used as focusing elements.
After the light is filtered and focused while traveling down the beamline, it strikes a sample.
Samples
“Sample” is a generic way of referring to the thing a scientist is researching using synchrotron light. It could be a crystal whose structure we wish to better understand. It could be some material or object that we want to image in high resolution. The possibilities are numerous, and we’ll explore some research topics further down.
Using the light source, experimental samples are subjected to varying temperatures, pressures, etc. Detectors nearby record the data from the sample-light interaction and send it to computers for collection and analysis.
Types of synchrotron light experiments
What kind of experiments can be done using synchrotron light? Experiments themselves are too numerous to list, but the methods are a little easier to pin down by category. Here are three of the most common.
1. Diffraction
This happens when synchrotron light is diffracted by the sample itself. Waves (in our case, light waves) are spread out as they pass an object or go through an aperture. After interacting with the sample, that light creates an image, called a diffraction pattern.
One can learn more about the sample’s nature by studying this image, e.g. an X-ray image.
2. Spectroscopy
This happens when light is sent through a sample and measurements are taken on the other side to see which wavelengths are absorbed and/or emitted according to the sample’s characteristics.
Spectroscopy gives us a look into the sample’s electrical states or chemical bonds, in addition to its composition.
3. Imaging
Using X-rays as our wavelength in this example, the light penetrates the sample and emerges on the other side, creating a contrast image of the sample’s interior. It’s similar to how doctors view a broken bone inside your arm with a hospital X-ray machine, except much more powerfully and with a much higher spatial resolution thanks to better focusing.
Industry and research applications
From investigating the structure of crystals and proteins to monitoring air pollution, synchrotron radiation is capable of shedding light on the molecular and atomic worlds. Here are a few ways both industry and research use it in practice.
Medical and pharmaceutical research
Perhaps one of the best-known applications of synchrotron light is in medical and pharmaceutical research. The high intensity of this light allows for the study of disease mechanisms, high-resolution imaging, and advances in microbiology and cancer radiation therapy.
Materials research
One of the advantages of synchrotron light for materials research is its high tunability. Particularly in the X-ray range, scientists can pick and choose exactly what kind of light they want to use in their experiment. This allows for high precision and time-dependent measurements that would be impossible under other circumstances.
Synchrotron radiation reflecting from a terbium crystal at the Daresbury Synchrotron Radiation Source, 1990
In short, synchrotron light sources “reveal the structure, chemical composition, electronic properties, and other features of specimens critical to materials science,” among other disciplines, according to Chemical & Engineering News.
Bio and environmental sciences
The same mechanisms that permit a look inside materials also allow researchers to examine macromolecules, proteins, and other structures. Crystallography—the science of crystal structures and properties—is an important application of synchrotron radiation.
But it’s in atmospheric research and clean combustion technologies where applications may be more visible to the public in the years to come.
For example, a 2018 study on auto-oxygenation using synchrotron radiation from Berkeley’s Advanced Light Source provided insight into atmosphere pollution because the chemistry between that and fuel combustion inside an engine is similar. The findings enabled more accurate fuel combustion simulations, but could, ultimately, help improve simulations predicting air pollution and global temperature, according to Phys.org.
Synchrotron radiation discoveries and successes
This is just scratching the surface of light source uses. Entire books have been dedicated to the subject. So instead of diving further into the research applications and theory, we’ll look at a couple far-reaching discoveries that apply to the masses.
Giant magnetoresistance
One of the most far-reaching discoveries to come out of synchrotron radiation research is giant magnetoresistance.
This type of quantum mechanical process is observed as a big change in electrical resistance depending on the alignment of ferromagnetic layers in a structure. The main application of this phenomena is in magnetic field sensors, like those used in hard drives and other computer parts.
According to Ennen et al. in a 2016 paper, applications of this technology are “impressively broad, ranging from applications in the air- and space or automotive industry, non-destructive material testing, or the compass functionality in mobile phones to biomedical techniques, like biometric measurements of eyes and biosensors.”
Put simply, the modern technological world as we know it would not be possible without the discovery of giant magnetoresistance.
Tamiflu
Another wide-reaching discovery is the anti-flu drug Tamiflu. The structure of one of its active ingredients was determined using synchrotron radiation.
The drug is widely used today and was a go-to treatment for the 2005 H5N1 flu outbreak in Southeast Asia.
Synchrotron Nobel Prizes
There have been several Nobel Prizes awarded that depended on synchrotron radiation. One was the 2009 Nobel Prize in Chemistry given to Venkatraman Ramakrishnan, Thomas A. Steitz, and Ada E. Yonath for their work in figuring out the structure of the ribosome.
This research used, in part, X-ray crystallography, a method made possible by synchrotron light. “Venkatraman Ramakrishnan and other researchers were able to collaborate to map the structure of ribosomes, made up of hundreds of thousands of atoms,” according to the Nobel Prize website. “Among other applications, this has been important in the production of antibiotics.”
The Argonne National Laboratory synchrotron and the Stanford Synchrotron Radiation Lightsource helped win the 2012 Nobel Prize in Chemistry for “the structure and functioning of a protein complex on the surface of human cells, called a G-protein-coupled receptor, that receives signals from the cell’s environment and is a key target for drug development,” said Stanford National Accelerator Lab.
The future of synchrotron radiation: Free-electron lasers
First-generation synchrotrons were built in the mid-1900s and often couldn’t be iteratively upgraded. Second-generation machines, however, were constructed with the idea of continuous improvement. Many have been improved with successive add-ons over time, boosting their power and refining their capabilities.
Such piecemeal improvement can only take us so far. The third generation of these accelerators and beyond will likely involve entirely new technology and new construction.
The free-electron laser FELIX Radboud University, Netherlands.
One of the possibilities for third-generation machines is the use of free-electron lasers (FELs) as the beam source itself.
Regular lasers make light by jiggling electrons bound inside atoms. FELs make light by using magnets to agitate electrons that are unbound from atoms, a.k.a. “free” (hence the name).
The FELs use the same undulators as those in storage ring-based light sources, but thanks to an electron clumping effect called “micro-bunching,” the resulting synchrotron radiation is orders of magnitude more coherent and intense.
Another important feature is their ability to generate shorter—femtosecond, in this case—pulses with the same intensity in each peak that current synchrotron sources emit in one second. Such pulses can produce X-rays millions of times brighter than today’s most powerful light sources.
This kind of speed, precision, and power would allow researchers to probe matter in new ways.
While that might sound innocuous on its face, it is in fact remarkable because scientists will be able to scrutinize almost unimaginably small, complex structures. With next-generation synchrotron light sources, we have a microscope capable of peering into the very chemical and atomic processes that make up life and the world around us.
See a synchrotron light source simulation with Sirepo.
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Learn about RadiaSoft’s world-class team in our new Q&A series. Today, we speak with Dr. Stephen Webb, a senior research scientist, about his job, current research, favorite scientist from history, and more.
What do you do at RadiaSoft?
I spend a lot of my time working on R&D projects for the Department of Energy (DOE). Right now, I’m focused on a project to model a novel ultra-high-efficiency free-electron laser (FEL) configuration for an experiment that we’ll be conducting with RadiaBeam, UCLA, and Argonne National Laboratory. In the past, I’ve done a lot of beam dynamics with people at Fermilab and algorithm development and modeling of complex accelerator systems.
Aside from the R&D, I’ve been doing some mentorship for new scientists. I work with them to get up to speed with how we do things and try to learn new skills from them at the same time.
What’s your educational and career background?
I have an undergraduate degree in physics from Georgia Tech. After that, I enrolled at Stony Brook University. While my original background was in condensed matter physics, I bounced around until I landed in the accelerator group at Brookhaven National Laboratory and, ultimately, did my PhD on free-electron laser theory.
Shortly after finishing that program, I moved out to Colorado to take a job at a small R&D company. Then when David, our CEO, started up RadiaSoft, he hired me away as the first full-time employee. I’ve been working at RadiaSoft ever since.
Where did you grow up?
I grew up in suburban Atlanta, not too far from where Donald Glover, the comedian and musician, grew up. He’s got a couple stories about a Home Depot and I know that Home Depot!
Before joining RadiaSoft, what is the strangest or most interesting job you’ve held?
I worked at a used bookstore in Atlanta during summers in college. I’ve got so many stories about that place. One day, a man came in without a shirt or shoes holding a garbage bag full of romance novels to sell. Me and a coworker had to go through a full bag of mildewy paperback romances. We only took eight of them.
We had another regular who’d we actually close the store down for. He was a therapist at the local prison, with a fascinating background, and he’d stock his library with a few grand worth of books on slow Sunday evenings.
Who is your favorite scientific figure from history and why?
The first person that comes to mind is the guy who invented group theory, Évariste Galois. In 1832, when he was 20, he ended up in a duel with someone over a woman and he knew he was going to die because he wasn’t much of a fighter.
He spent the last night before the duel writing down everything, all the math that was in his head. He was shot and later died. Those papers became the foundation for group theory, which is fundamental for math and physics.
Tell us about one of your current or future projects.
I’m pitching a project to DOE on how we can use machine learning for accelerator controls. One of the things with autonomous accelerators, i.e. where the machine does most of the deciding, is that it has to figure out when its diagnostic measurements are faulty.
A human can easily look at a diagnostic measurement and go, yeah, that’s bad. But figuring out how to do that purely with the diagnostic information is a challenging problem. It’s also absolutely necessary for autonomous accelerators. If you don’t have that, it can’t be truly autonomous.
Right now, the approach that’s used for automatic tuning of accelerators is very binary. For example, Diagnostic A is behaving within some tolerance and then it very abruptly goes outside of that. When that happens, the machine drops the diagnostic completely.
What I am working on is a way to allow diagnostics to gracefully fail. That way, you can see that it’s starting to fail, but is still useful right now. It lets you know when to put in a purchase order, while you continue to extract info until you replace it, or it fails.
What is a talent, secret superpower, or fun fact about yourself that people wouldn’t guess?
I’ve been doing aikido for 14 years and am a second-degree black belt. I’ve also been to 20 countries, but never south of the equator.
What’s your favorite Slack emoji and why?
It’s one I made for our Slack, called jetbarf, which I hand drew. It’s a smiley face barfing the jet colormap. We use it to describe a bad plot.
No one should use jet in their figures. It’s overwhelming and there’s all this color-perception theory about why jet is terrible.
What’s something you wish people understood better about RadiaSoft?
RadiaSoft has operated in a lot of different worlds, and because those worlds don’t talk to each other, many people see the company as only Sirepo or RadiaSoft Scientific Consulting.
It’s really all of it. We’re a small enough team that it’s very easy to take disparate disciplines and put them in one room to solve a hard problem. We have a staff with a broad background of talent. Most of our scientists are experts in two or three different fields. We’re an interdisciplinary group that’s very good at porting our skills to new problems. Basically, if you bring us a problem, we might not have solved it exactly, but we probably have the skillset required to attack it.
Want to learn more about the RadiaSoft team? Visit our team page for full bios.
How Particle Accelerators Work: From Linac to Synchrotron
Accelerating particles is a simple concept: an electric field moves a charged particle from one location to another. Since electric fields don’t act on neutral particles (like neutrons), only electrons, protons, ions, and various antiparticles can be accelerated like this.
How much the particle speeds up depends on the strength of the electric field. Over the last century, accelerator design has become more sophisticated to achieve higher-energies, but basic principles remain constant. Where things get really fascinating, however, are the new applications for particle accelerators and beams.
To understand the full picture of accelerator science, we’ll explore not only how modern machines work, but also how they’re used in everyday life.
Contents
Why we accelerate particles
More than 30,000 accelerators are in use around the world. The most famous is the Large Hadron Collider (LHC) at CERN. While it’s the world’s largest and most powerful particle accelerator, it’s an exception.
Yes, they’re most well-known for helping scientists explore the foundational building blocks of the universe, but accelerators are also used for cancer therapy, food packaging, materials research, imaging broken bones, archeology, art, and more. They’ve even improved the taste of chocolate and helped to make baby diapers more absorbent.
Before diving into the mechanics and physics of accelerators, let’s explore some of these everyday applications in greater detail.
Particle accelerators applications
Cancer therapy
One of the most practical and impactful uses for particle accelerators is in cancer treatment. Conceived in the 1950s, proton therapy, where a tumor is bombarded with a beam of protons, remains one of the most cutting-edge treatment for cancerous tumors. These charged particles damage the DNA of cancer cells, killing them.
Early model of the linear accelerator developed to treat cancer.
Early model of the linear accelerator developed to treat cancer.
There are advantages to treating with protons over traditional radiation therapies, which attack the tumor with X-rays. Radiation biologist Kathryn Held explained it best in a presentation at the 2014 AAAS meeting: “We could cure a very high percentage of tumors if we could give sufficiently high doses of radiation, but we can’t because of the damage to healthy tissue. That’s the advantage of particles. We can tailor the dose to the tumor and limit the amount of damage in the critical surrounding normal tissues.” Proton beams have a very handy property called the Bragg peak, which means the particles can precisely target tumors and deposit most of their cancer-killing energy in the tumor itself, rather than flowing out through the body and damaging healthy tissue.
Food packaging
Have you ever wondered how a refrigerated Thanksgiving turkey can be so perfectly plastic wrapped? The short answer is, accelerators.
It works like this: shrink wrap is often made of polyethylene plastic. Polymers, a.k.a. the molecules, are strung together like beads on a necklace. The plastic is comprised of many of these necklaces.
When a beam of particles (usually electrons) from an accelerator hits the polymer, it ionizes the material. Ionization allows these necklaces to form new molecular bonds with each other in a phenomenon called “crosslinking.”
Image via U.S. Packaging & Wrapping LLC
This process makes the plastic much stronger because the molecular structure is now interconnected, like a net.
When crosslinked plastic is heated, it doesn’t melt. This is key because “[w]hen cooled to room temperature, the [crosslinked] plastic retains its expanded shape. Place something inside it, such as a Butterball turkey, and apply heat, and the plastic shrinks back down to its original size, resulting in an air-tight wrapping,” explains Elizabeth Clements in a Symmetry article.
Art and archeology
Priceless works of art pose a unique challenge in identifying their constituent materials, divining their provenance, and understanding how they’re made. Plus, their often delicate and irreplaceable nature requires a nondestructive and, if possible, nonsampling solution.
Enter, accelerators.
For decades, the Centre for Research and Restoration of the Museums of France has used ion beam analysis (IBA) to analyze works of cultural heritage. First introduced in 1957, IBA has become the go-to solution for historians to gain insight into works of art and archeological artifacts.
With their accelerator, AGLAE, the Centre has identified the materials used in drawings of Italian Renaissance artist Pisanello, the pigments on an Egyptian Book of the Dead, and much more.
How particles are accelerated
How can one machine do all of the above? It comes down to electricity and magnetism. While accelerators come in three main types (explained further down), they all require some basic parts to function.
A source of charged particles
Electric fields to accelerate those particles
Magnetic fields to control the particles’ paths
A vacuum chamber through which the particles travel
Detectors for measuring particle attributes
Charged particle source
Sources of charged particles can be a gas or even a solid material, like metal. To get the particles themselves, the donor gas or metal is excited and particles are stripped off.
Charged particles are then shot through a “gun” into the accelerator itself, where electric fields accelerate them, increasing their energy.
The amount of energy a particle acquires—measured in electronvolts (eV)—as it moves through an electric field is determined by the difference in electric potential between where it enters and exits the field. Higher potential means higher particle energy.
Magnetic fields focus and steer the particle beam. If it’s a circular accelerator, they also bend the beam’s path into a complete circle.
Image copyright: Creative Commons. Red particles emerging from a source box, S, and accelerating as they pass through four open-ended cylindrical metal electrodes (C1–C4) connected to an oscillator, G.
To get an idea of what this looks like, the above GIF illustrates a linear setup with an alternating electric field accelerating red particles. The particle gun is represented by S, the electric field by E, and its charge by red and blue +/-.
To accelerate particles, both cyclic and linear accelerators typically use alternating electric fields generated by electromagnetic waves. These can range from radio- to microwaves. The field in adjacent accelerating cavities are out of phase with each other, so that the field ramps back up right as the particles transition from one cavity to the other.
This means the particle “feels” a constant acceleration every time it passes through a cavity. These individual “pushes” add up over time as the particles move through more fields, resulting in a big net “push,” accelerating them to high energies.
Vacuum chambers
All of this action happens inside vacuum chambers to avoid contact with the atmosphere. This is vital because charged particles are so small that they can be easily bumped off course or lose energy through collisions with the air.
Particle detectors
These accelerated particles can be smashed into targets or into each other (if there are two beams accelerating in opposite directions).
That’s where detectors come in. They can be as tailor-made as the interactions researchers want to observe. We won’t go into all the details here, but check out this short summary of how particle detectors work.
What are the different types of accelerators?
There are two main accelerator families: linear and circular. Within those, there are many designs. The three most common types of accelerators are linear accelerators, cyclotrons, and synchrotrons.
Linear accelerators
Linear accelerators (or linacs) are so named because of their shape. In a linac, particles are accelerated through a sequence of electric fields in a straight line, gaining energy the further they travel.
Like cars drag racing down a highway, they only go in one direction, accelerating all the while. The more fields they pass through, the more they accelerate, and the more fields, the longer the linac.
Before the advent of flatscreen TVs, many people had accelerators sitting in their living rooms. That’s because cathode ray tubes, the devices used to generate images on screen, are a kind of linac.
Today, the largest linac is the Stanford Linear Accelerator at SLAC National Accelerator Laboratory, which measures 2 mi (3.3 km) long. It can accelerate particles up to 50 gigaelectronvolts (GeV).
Image via energy.gov: SLAC National Accelerator Laboratory
Circular accelerators
In the circular family of accelerators, there are two main types: cyclotrons and synchrotrons.
Cyclotrons
In a cyclotron, particle beams are steered through relatively weak electric fields many times, gaining energy while traveling outward in a spiral towards a target.
Invented around 1930, the first cyclotron was only 4.5 inches in diameter—small enough to hold in your hand. The largest ever built is 59 ft (18 m) in diameter. Called TRIUMF, it’s located in British Columbia, Canada.
In this kind of accelerator, charged particles are injected into a vacuum chamber between two hollow D-shaped metal electrodes, called dees, in the cyclotron’s center.
Electrodes provide an alternating radio-frequency voltage that switches between the two dees. Precise timing accelerates the particles and increases their path’s diameter, changing it into the spiral indicated in dashed lines above.
A large magnet provides a constant magnetic field which bends the particles’ path so they stay within the cyclotron and keep accelerating, gaining more energy in each revolution.
Synchrotrons
Synchrotrons are a type of circular accelerator that can reach very high energies. They do this by keeping the electric and magnetic fields synchronized with the particle beam as it gains energy. Hence, the name.
Unlike the spiral motion of a cyclotron, particles move around a circle inside a synchrotron. (Think NASCAR races on a circular track.) As the particles accelerate, the electromagnetic field in the ring increases to keep pace.
A synchrotron beam isn’t continuous. Instead, particles are clustered into “bunches.” Each bunch is shaped like a small, ultrathin noodle. The bunch could be a few centimeters long, but only a tenth of a millimeter wide.
These bunches contain something like 1012 particles, a density that still falls far short of the number of atoms that would be in an actual noodle of that size.
The Next Frontier: Higher Luminosity and Smaller Machines
From CERN’s massive and complex LHC, which holds the record for largest machine ever built, to comparatively run-of-the-mill linacs in hospital X-ray rooms, we’ve become very good at building particle accelerators.
So, where do we go from here? Have we reached the limit on what we can build or what accelerators can do? The answer is a resounding no.
There are many roads for advancing accelerator physics. Two with far-reaching potential are increasing beam luminosity and making accelerators very, very tiny.
Why luminosity matters
One indicator of accelerator performance is luminosity. It provides a metric for how many interactions you can see and how much data you can produce, which means more potential for discoveries of new physics.
That potential is the focus of the High Luminosity LHC (HL-LHC) project. According to CERN, the project “will allow physicists to study [known] mechanisms in greater detail, such as the Higgs boson, and observe rare new phenomena that might reveal themselves.”
Scheduled to start operation in 2027, it aims to increase luminosity by a factor of 10 over the original LHC’s design value. Experts estimate the upgrade will produce 15 million Higgs bosons annually. That’s up from the three million the LHC made in 2017. Increasing this number is important for scientists at CERN, as detectors can only clearly observe a small fraction of the Higgs bosons that are produced.
Making many more bosons could lead to observations that expand on the Standard Model of particle physics, changing our understanding of the most basic building blocks of matter.
Miniaturizing Accelerators
Researchers are also making accelerators smaller than ever. One example is the accelerator-on-a-chip—a nanoscale particle accelerator made by Stanford University researchers. Presently in the proof-of-concept stage, it demonstrates that accelerators can be made cheaper and smaller than behemoths like the LHC.
“The largest accelerators are like powerful telescopes. There are only a few in the world and scientists must come to places like SLAC to use them,” electrical engineer and team lead on this project Jelena Vuckovic said in a Sci Tech Daily article. “We want to miniaturize accelerator technology in a way that makes it a more accessible research tool.”
Along with that accessibility, making accelerators more compact has manifold possibilities in other applications. Today, X-ray machines take up whole rooms, perhaps with technology like this they could be made portable. Perhaps cancer therapies could be made cheaper with easier-to-manufacture equipment.
One thing is certain, from the largest to the smallest, the future of accelerators is one of vast possibility for both fundamental science and industry application.
Three Reasons Why You Should Wrap Legacy Codes in GUIs
Scientific research, no matter the subject, depends on specialized codes to model complex systems. Scientists are constantly developing tools for their own research that could benefit others, but these codes, scripts, simulations, and programs are rarely shared. Not because they aren’t useful, but because sharing them can be challenging.
Most often, scientists develop code that runs locally on the command line. Meaning, a user interacts with the computer via commands given in text form, rather than using menus or graphics.
Command-line interfaces (CLIs) can be difficult to learn for novices. There are no visual cues; one must recall what the system can do and the commands to make it happen. To get them up and running, confusing text input files are also needed.
One solution to making knowledge transfer easier, onboarding processes faster, and collaboration less cumbersome? Wrap your command line code in a graphical user interface (GUI). GUIs have the advantage of being easy to use and simple to learn, which opens a code up to a wider group of users who get up to speed faster.
In this post, we cover three ways GUIs improve productivity and business outcomes.
1. Better succession planning
Frequently, mission-critical scientific codes have one expert—the code author. But what happens when that person moves on to another project, retires, or otherwise becomes unavailable?
Companies and labs can be left scrambling.
This is where good succession planning can make a big difference. If you have a simple, quick way to transfer legacy code resources and knowledge, everyone can get working faster. One way to do that is to wrap that important code in a GUI.
GUIs are simple to learn and don’t require advanced or specific coding knowledge. This means that learning and using legacy code with GUIs can happen much faster than traditional command-line interfaces.
2. Shorter onboarding processes
Another complication of single-person authorship is an arduous onboarding process. Teaching a new employee how to use a legacy code can mean weeks or more of learning before they can work independently.
If a project is time-sensitive, that onboarding could run longer than the project itself. This also adds cost because two people are working (teacher and student) when one trained person would suffice.
For example, say a summer intern is assigned to work on a project requiring the use of a legacy code. Even if they are familiar with coding, it can take weeks to get them up to speed on a particular legacy code. That means one or more people teaching that intern the ropes, adding cost. By the time they can work confidently, the internship could be over.
GUIs flatten the learning curve when compared to command-line interfaces, reducing onboarding time from months to days.
This is largely thanks to their visual nature. You don’t need to memorize commands for GUIs. You don’t need to be fluent in coding language for GUIs. Graphical interfaces allow complex systems and/or processes to be reduced to simple-to-execute actions. (This feature is especially useful since legacy codes often lack proper documentation.)
3. Ready-made plots
Command line codes do not automatically produce graphical plots or other visuals. This means that after the code is run, the user must take multiple additional steps to produce a plot, increasing overall task time.
GUIs can be programmed to automatically produce plots in the same program. Reducing the time spent running ancillary plotting programs, and improving overall efficiency.
GUIs open legacy codes up to a broader community
Simply wrapping a legacy code in a GUI can solve major problems impacting your business right now by making people productive faster. It makes getting consistent visualizations straightforward, which can aid marketing efforts and improve ease of use. It also future-proofs your company, ensuring legacy code resources aren’t lost with staff transitions.
Bottom line, if you want to better your business in any of the above ways, it’s time to start thinking about wrapping your legacy code in a GUI.
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