IPAC2022 - List of Authors (Einstein-Curtis, J.A.)

Paper	Title	Page
MOPOTK038	BPM Analysis with Variational Autoencoders	543
	C.C. Hall, J.P. Edelen, J.A. Einstein-Curtis, M.C. Kilpatrick RadiaSoft LLC, Boulder, Colorado, USA
	Funding: This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of High Energy Physics under Award Number DE-SC0021699. In particle accelerators, beam position monitors (BPMs) are used extensively as a non-intercepting diagnostic. Significant information about beam dynamics can often be extracted from BPM measurements and used to actively tune the accelerator. However, common measurement tools, such as measurements of kicked beams, may become more difficult when very strong nonlinearities are present or when data is very noisy. In this work, we examine the use of variational autoencoders (VAEs) as a technique to extract measurements of the beam from simulated turn-by-turn BPM data. In particular, we show that VAEs may have the possibility to outperform other dimensionality reduction techniques that have historically been used to analyze such data. When the data collection period is limited, or the data is noisy, VAEs may offer significant advantages.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-MOPOTK038
About •	Received ※ 09 June 2022 — Revised ※ 13 June 2022 — Accepted ※ 15 June 2022 — Issue date ※ 10 July 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUPOST009	Online Correction of Laser Focal Position Using FPGA-Based ML Models	857
	J.A. Einstein-Curtis, S.J. Coleman, N.M. Cook, J.P. Edelen RadiaSoft LLC, Boulder, Colorado, USA S.K. Barber, C.E. Berger, J. van Tilborg LBNL, Berkeley, California, USA
	Funding: This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of High Energy Physics under Award Numbers DE-SC 00259037 and DE-AC02-05CH11231. High repetition-rate, ultrafast laser systems play a critical role in a host of modern scientific and industrial applications. We present a prototype diagnostic and correction scheme for controlling and determining laser focal position at 10 s of Hz rate by utilizing fast wavefront sensor measurements from multiple positions to train a focal position predictor. This predictor is used to determine corrections for actuators along the beamline to provide the desired correction to the focal position on millisecond timescales. Our initial proof-of-principle demonstrations leverage pre-compiled data and pre-trained networks operating ex-situ from the laser system. We then discuss the application of a high-level synthesis framework for generating a low-level hardware description of ML-based correction algorithms on FPGA hardware coupled directly to the beamline. Lastly, we consider the use of remote computing resources, such as the Sirepo scientific framework* , to actively update these correction schemes and deploy models to a production environment. * M.S. Rakitin et al., "Sirepo: an open-source cloud-based software interface for X-ray source and optics simulations", Journal of Synchrotron Radiation 25, 1877-1892 (Nov 2018).
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-TUPOST009
About •	Received ※ 20 May 2022 — Revised ※ 14 June 2022 — Accepted ※ 15 June 2022 — Issue date ※ 23 June 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

BPM Analysis with Variational Autoencoders

543

C.C. Hall, J.P. Edelen, J.A. Einstein-Curtis, M.C. Kilpatrick
RadiaSoft LLC, Boulder, Colorado, USA

Funding: This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of High Energy Physics under Award Number DE-SC0021699.
In particle accelerators, beam position monitors (BPMs) are used extensively as a non-intercepting diagnostic. Significant information about beam dynamics can often be extracted from BPM measurements and used to actively tune the accelerator. However, common measurement tools, such as measurements of kicked beams, may become more difficult when very strong nonlinearities are present or when data is very noisy. In this work, we examine the use of variational autoencoders (VAEs) as a technique to extract measurements of the beam from simulated turn-by-turn BPM data. In particular, we show that VAEs may have the possibility to outperform other dimensionality reduction techniques that have historically been used to analyze such data. When the data collection period is limited, or the data is noisy, VAEs may offer significant advantages.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-MOPOTK038

About •

Received ※ 09 June 2022 — Revised ※ 13 June 2022 — Accepted ※ 15 June 2022 — Issue date ※ 10 July 2022

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUPOST009

Online Correction of Laser Focal Position Using FPGA-Based ML Models

857

J.A. Einstein-Curtis, S.J. Coleman, N.M. Cook, J.P. Edelen
RadiaSoft LLC, Boulder, Colorado, USA
S.K. Barber, C.E. Berger, J. van Tilborg
LBNL, Berkeley, California, USA

Funding: This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of High Energy Physics under Award Numbers DE-SC 00259037 and DE-AC02-05CH11231.
High repetition-rate, ultrafast laser systems play a critical role in a host of modern scientific and industrial applications. We present a prototype diagnostic and correction scheme for controlling and determining laser focal position at 10 s of Hz rate by utilizing fast wavefront sensor measurements from multiple positions to train a focal position predictor. This predictor is used to determine corrections for actuators along the beamline to provide the desired correction to the focal position on millisecond timescales. Our initial proof-of-principle demonstrations leverage pre-compiled data and pre-trained networks operating ex-situ from the laser system. We then discuss the application of a high-level synthesis framework for generating a low-level hardware description of ML-based correction algorithms on FPGA hardware coupled directly to the beamline. Lastly, we consider the use of remote computing resources, such as the Sirepo scientific framework* , to actively update these correction schemes and deploy models to a production environment.
* M.S. Rakitin et al., "Sirepo: an open-source cloud-based software interface for X-ray source and optics simulations", Journal of Synchrotron Radiation 25, 1877-1892 (Nov 2018).

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-TUPOST009

About •

Received ※ 20 May 2022 — Revised ※ 14 June 2022 — Accepted ※ 15 June 2022 — Issue date ※ 23 June 2022

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)