Christopher M. Pierce

Accelerator Physicist, Stealth Startup

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The magnet from EO Lawrence's 27-inch cyclotron

(photo credit: WH McNeil)

Particle accelerators are an ideal playground to explore the potential of scientific machine learning. These systems generate large, complex, and physically-constrained data sets, which can be challenging to analyze using traditional methods. Machine learning techniques, however, have shown promise in uncovering complex nonlinear patterns and correlations in data and we may be able to use them to improve the way particle accelerators are designed and operated. In my work, I am integrating machine learning techniques with the physical laws that govern charged particle beams to improve the modeling, instrumentation, control, and optimization of particle accelerators.

As an example of the power of machine learning, the following figure represents the output of a neural network which has been trained to predict the shape of a particle beam in phase space as it traverses a toy particle accelerator. Nonlinear space charge forces degrade the beam’s “emittance” (in plain language, quality) over time. Space charge forces are usually expensive to model in physics-based simulation tools. However, after the neural network has been trained on sparse calculated results, it is able to query the properties of the beam as system parameters are varied in an efficient and differentiable manner.

A visualization of a particle beam's distribution function solved for using scientific machine learning

In addition to my work on scientific machine learning, my interests include photocathode physics for particle accelerator applications. Photocathodes are used to generate high-brightness electron beams which are necessary for some applications of particle accelerators, such as free electron lasers and ultrafast electron diffraction. The brightness of electron beams produced by the photocathode is highly dependent on the choice of photocathode material and in particular a material-dependent property called the mean transverse energy. Measuring the mean tranverse energy of photocathodes and devloping models that can predict this metric based on other material properties and understand how real-world problems can affect models are important goals in accelerator physics.

My work in the area of photocathode physics is related to exploiting the electronic band structure of semiconductors to limit the mean transverse energy of the photocathode and explore nonlinear photoemission processes via nanopatterned surfaces as a mechanism to shrink the photocathode’s source size (see image below). My past work has also shown how nonlinear processes can be enhanced using nanoscale patterning of metal surfaces to improve the efficiency of generating short pulses of electrons. Additionally, I created and maintain an open repository of published data related to photocathode materials. The Photocathode Database exists to connect researchers with published measurements on a variety of photocathode materials. Although it is currently small, I hope it will continue to grow in usefulness as I continue to digitize and add data to it. I am always looking for help to maintain this resources and interested parties should contact me for information on how to get started.

A scanning electron micrograph of a nanopatterned spiral photocathode proposed for source size reduction

news

Oct 28, 2024 Pleased to have contributed to “Harnessing Plasmonic Interference for Nanoscale Ultrafast Electron Sources” out now in Physical Review Letters. The work demonstrates a novel approach to creating nanoscale electron sources using plasmonic focusing, achieving sub-40 pm-rad normalized transverse emittance.
Aug 29, 2024 Excited to announce the release of my new VSCode extension for the Bmad accelerator simulation environment! This extension enhances the development experience with features like syntax highlighting. Check it out on GitHub!
Mar 3, 2024 On my way to Korea for the ICFA ML Applications for Particle Accelerators Mini Workshop. Come say hi to me there!
Dec 29, 2023 I have completed all of the problems in Advent of Code this Year! Check out my solutions on Github.
Nov 21, 2023 Our conference proceeding “Kernel Density Estimators for Axisymmetric Particle Beams” has been published! Read more about it at the publication’s website.
Sep 1, 2023 I am happy to share that I am starting a new position as an accelerator physicist at a stealth startup. Looking forward to when I can share more about the exciting work we are doing!
Jun 19, 2023 Excited to present my work on physics-informed surrogate modeling for accelerators at Physics and Applications of High Brightness Beams!
Mar 10, 2023 Check out my latest publication in Physical Review Applied: Experimental Characterization of Photoemission from Plasmonic Nanogroove Arrays
Jan 25, 2023 I just launched a website! The Photocathode Database will connect researchers with published measurements on photocathode properties.
Nov 2, 2022 Say hi to me at the ICFA Beam Dynamics Mini-Workshop on Machine Learning Applications for Particle Accelerators! I am there presenting some of my work on how surrogate models can be installed into genetic algorithms and greatly improve the efficiency of multi-objective optimizations of accelerators.