IPAC2021 - List of Authors (Palmer, M.A.)

Paper	Title	Page
MOPAB286	Towards a Data Science Enabled MeV Ultrafast Electron Diffraction System	906
	M.A. Fazio, S. Biedron, M. Martínez-Ramón, D.J. Monk, S.I. Sosa Guitron UNM-ECE, Albuquerque, USA M. Babzien, K.A. Brown, M.G. Fedurin, J.J. Li, M.A. Palmer, J. Tao BNL, Upton, New York, USA S. Biedron, T. Talbott UNM-ME, Albuquerque, New Mexico, USA J. Chen, A.J. Hurd, N.A. Moody, R. Prasankumar, C. Sweeney LANL, Los Alamos, New Mexico, USA D. Martin, M.E. Papka ANL, Lemont, Illinois, USA
	Funding: US DOE, SC, BES, MSE, award DE-SC0021365 and DOE NNSA award 89233218CNA000001 through DOE’s EPSCoR program in Office of BES with resources of DOE SC User Facilities BNL’s ATF and ALCF. A MeV ultrafast electron diffraction (MUED) instrument is a unique characterization technique to study ultrafast processes in materials by a pump-probe technique. This relatively young technology can be advanced further into a turn-key instrument by using data science and artificial intelligence (AI) mechanisms in conjunctions with high-performance computing. This can facilitate automated operation, data acquisition and real time or near- real time processing. AI based system controls can provide real time feedback on the electron beam which is currently not possible due to the use of destructive diagnostics. Deep learning can be applied to the MUED diffraction patterns to recover valuable information on subtle lattice variations that can lead to a greater understanding of a wide range of material systems. A data science enabled MUED facility will also facilitate the application of this technique, expand its user base, and provide a fully automated state-of-the-art instrument. We will discuss the progress made on the MUED instrument in the Accelerator Test Facility of Brookhaven National Laboratory.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-MOPAB286
About •	paper received ※ 20 May 2021 paper accepted ※ 09 June 2021 issue date ※ 25 August 2021
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MOPAB314	Surrogate Modeling for MUED with Neural Networks	970
	D.J. Monk, S. Biedron, M.A. Fazio, M. Martínez-Ramón, S.I. Sosa Guitron UNM-ECE, Albuquerque, USA M. Babzien, K.A. Brown, M.A. Palmer, J. Tao BNL, Upton, New York, USA D. Martin, M.E. Papka ANL, Lemont, Illinois, USA T. Talbott UNM-ME, Albuquerque, New Mexico, USA
	Electron diffraction is among the most complex and influential inventions of the last century and contributes to research in many areas of physics and engineering. Not only does it aid in problems like materials and plasma research, electron diffraction systems like the MeV ultra-fast electron diffraction(MUED) instrument at the Brookhaven National Lab(BNL) also present opportunities to explore/implement surrogate modeling methods using artificial intelligence/machine learning/deep learning algorithms. Running the MUED system requires extended periods of uninterrupted runtime, skilled operators, and many varying parameters that depend on the desired output. These problems lend themselves to techniques based on neural networks(NNs), which are suited to modeling, system controls, and analysis of time-varying/multi-parameter systems. NNs can be deployed in model-based control areas and can be used simulate control designs, planned experiments, and to simulate employment of new components. Surrogate models based on NNs provide fast and accurate results, ideal for real-time control systems during continuous operation and may be used to identify irregular beam behavior as they develop.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-MOPAB314
About •	paper received ※ 20 May 2021 paper accepted ※ 07 June 2021 issue date ※ 15 August 2021
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THPAB011	Monte Carlo Driven MDI Optimization at a Muon Collider	3769
	C. Curatolo, D. Lucchesi Univ. degli Studi di Padova, Padova, Italy F. Collamati INFN-Roma1, Rome, Italy C. Curatolo, D. Lucchesi INFN- Sez. di Padova, Padova, Italy A. Mereghetti CERN, Meyrin, Switzerland A. Mereghetti CNAO Foundation, Pavia, Italy N.V. Mokhov Fermilab, Batavia, Illinois, USA M.A. Palmer BNL, Upton, New York, USA P.R. Sala INFN-Milano, Milano, Italy
	A Muon Collider represents a very interesting possibility for a future machine to explore the energy frontier in particle physics. However, to reach the needed luminosity, beam intensities of the order of 10⁹–10¹² muons per bunch are needed. In this context, the Beam-Induced Background must be taken into account for its effects on magnets and detector. Several mitigation strategies can however be conceived. In this view, it is of crucial importance to develop a flexible tool that allows to easily reconstruct the machine geometry in a Monte Carlo code, allowing to simulate in detail the interaction of muon decay products in the machine, while being able to change the machine optics itself to find the best configuration. In this contribution, a possible approach to such a purpose is presented, based on FLUKA for the Monte Carlo simulation and on LineBuilder for the geometry reconstruction. Results based on the 1.5 TeV machine optics developed by the MAP collaboration are discussed, as well as a first approach to possible mitigation strategies.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-THPAB011
About •	paper received ※ 19 May 2021 paper accepted ※ 13 July 2021 issue date ※ 01 September 2021
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THPAB062	Long-Wave IR Terawatt Laser Pulse Compression to Sub-Picoseconds	3893
	I. Pogorelsky, M. Babzien, M.A. Palmer, M.N. Polyanskiy BNL, Upton, New York, USA
	Funding: U.S. Department of Energy under contract DE-SC0012704 We report an experiment and simulations on post-compression of 2 ps, 0.15 TW CO₂ laser pulses to 480 fs, ~0.25 TW by means of a self-phase modulation accompanied by a negative group dispersion in KCl and BaF2 optical slabs. In addition, down to 130 fs fine pulse structure, but at lower conversion efficiency, has been observed through self-compression in a bulk NaCl crystal. The obtained results surpass by far previous achievements in the ultra-fast long-wave IR laser technology
	Poster THPAB062 [2.675 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-THPAB062
About •	paper received ※ 12 May 2021 paper accepted ※ 18 June 2021 issue date ※ 24 August 2021
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

Towards a Data Science Enabled MeV Ultrafast Electron Diffraction System

906

M.A. Fazio, S. Biedron, M. Martínez-Ramón, D.J. Monk, S.I. Sosa Guitron
UNM-ECE, Albuquerque, USA
M. Babzien, K.A. Brown, M.G. Fedurin, J.J. Li, M.A. Palmer, J. Tao
BNL, Upton, New York, USA
S. Biedron, T. Talbott
UNM-ME, Albuquerque, New Mexico, USA
J. Chen, A.J. Hurd, N.A. Moody, R. Prasankumar, C. Sweeney
LANL, Los Alamos, New Mexico, USA
D. Martin, M.E. Papka
ANL, Lemont, Illinois, USA

Funding: US DOE, SC, BES, MSE, award DE-SC0021365 and DOE NNSA award 89233218CNA000001 through DOE’s EPSCoR program in Office of BES with resources of DOE SC User Facilities BNL’s ATF and ALCF.
A MeV ultrafast electron diffraction (MUED) instrument is a unique characterization technique to study ultrafast processes in materials by a pump-probe technique. This relatively young technology can be advanced further into a turn-key instrument by using data science and artificial intelligence (AI) mechanisms in conjunctions with high-performance computing. This can facilitate automated operation, data acquisition and real time or near- real time processing. AI based system controls can provide real time feedback on the electron beam which is currently not possible due to the use of destructive diagnostics. Deep learning can be applied to the MUED diffraction patterns to recover valuable information on subtle lattice variations that can lead to a greater understanding of a wide range of material systems. A data science enabled MUED facility will also facilitate the application of this technique, expand its user base, and provide a fully automated state-of-the-art instrument. We will discuss the progress made on the MUED instrument in the Accelerator Test Facility of Brookhaven National Laboratory.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-MOPAB286

About •

paper received ※ 20 May 2021 paper accepted ※ 09 June 2021 issue date ※ 25 August 2021

Export •

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

MOPAB314

Surrogate Modeling for MUED with Neural Networks

970

D.J. Monk, S. Biedron, M.A. Fazio, M. Martínez-Ramón, S.I. Sosa Guitron
UNM-ECE, Albuquerque, USA
M. Babzien, K.A. Brown, M.A. Palmer, J. Tao
BNL, Upton, New York, USA
D. Martin, M.E. Papka
ANL, Lemont, Illinois, USA
T. Talbott
UNM-ME, Albuquerque, New Mexico, USA

Electron diffraction is among the most complex and influential inventions of the last century and contributes to research in many areas of physics and engineering. Not only does it aid in problems like materials and plasma research, electron diffraction systems like the MeV ultra-fast electron diffraction(MUED) instrument at the Brookhaven National Lab(BNL) also present opportunities to explore/implement surrogate modeling methods using artificial intelligence/machine learning/deep learning algorithms. Running the MUED system requires extended periods of uninterrupted runtime, skilled operators, and many varying parameters that depend on the desired output. These problems lend themselves to techniques based on neural networks(NNs), which are suited to modeling, system controls, and analysis of time-varying/multi-parameter systems. NNs can be deployed in model-based control areas and can be used simulate control designs, planned experiments, and to simulate employment of new components. Surrogate models based on NNs provide fast and accurate results, ideal for real-time control systems during continuous operation and may be used to identify irregular beam behavior as they develop.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-MOPAB314

About •

paper received ※ 20 May 2021 paper accepted ※ 07 June 2021 issue date ※ 15 August 2021

Export •

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

THPAB011

Monte Carlo Driven MDI Optimization at a Muon Collider

3769

C. Curatolo, D. Lucchesi
Univ. degli Studi di Padova, Padova, Italy
F. Collamati
INFN-Roma1, Rome, Italy
C. Curatolo, D. Lucchesi
INFN- Sez. di Padova, Padova, Italy
A. Mereghetti
CERN, Meyrin, Switzerland
A. Mereghetti
CNAO Foundation, Pavia, Italy
N.V. Mokhov
Fermilab, Batavia, Illinois, USA
M.A. Palmer
BNL, Upton, New York, USA
P.R. Sala
INFN-Milano, Milano, Italy

A Muon Collider represents a very interesting possibility for a future machine to explore the energy frontier in particle physics. However, to reach the needed luminosity, beam intensities of the order of 10⁹–10¹² muons per bunch are needed. In this context, the Beam-Induced Background must be taken into account for its effects on magnets and detector. Several mitigation strategies can however be conceived. In this view, it is of crucial importance to develop a flexible tool that allows to easily reconstruct the machine geometry in a Monte Carlo code, allowing to simulate in detail the interaction of muon decay products in the machine, while being able to change the machine optics itself to find the best configuration. In this contribution, a possible approach to such a purpose is presented, based on FLUKA for the Monte Carlo simulation and on LineBuilder for the geometry reconstruction. Results based on the 1.5 TeV machine optics developed by the MAP collaboration are discussed, as well as a first approach to possible mitigation strategies.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-THPAB011

About •

paper received ※ 19 May 2021 paper accepted ※ 13 July 2021 issue date ※ 01 September 2021

Export •

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

THPAB062

Long-Wave IR Terawatt Laser Pulse Compression to Sub-Picoseconds

3893

I. Pogorelsky, M. Babzien, M.A. Palmer, M.N. Polyanskiy
BNL, Upton, New York, USA

Funding: U.S. Department of Energy under contract DE-SC0012704
We report an experiment and simulations on post-compression of 2 ps, 0.15 TW CO₂ laser pulses to 480 fs, ~0.25 TW by means of a self-phase modulation accompanied by a negative group dispersion in KCl and BaF2 optical slabs. In addition, down to 130 fs fine pulse structure, but at lower conversion efficiency, has been observed through self-compression in a bulk NaCl crystal. The obtained results surpass by far previous achievements in the ultra-fast long-wave IR laser technology

Poster THPAB062 [2.675 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-THPAB062

About •

paper received ※ 12 May 2021 paper accepted ※ 18 June 2021 issue date ※ 24 August 2021

Export •

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