Physics Community Needs, Tools, and Resources for Machine Learning - INSPIRE

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Physics Community Needs, Tools, and Resources for Machine Learning

Mar 30, 2022

33 pages

Contribution to:

Snowmass 2021

e-Print:

2203.16255 [cs.LG]

Report number:

FERMILAB-FN-1166-PPD-SCD

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Abstract: (arXiv)

Machine learning (ML) is becoming an increasingly important component of cutting-edge physics research, but its computational requirements present significant challenges. In this white paper, we discuss the needs of the physics community regarding ML across latency and throughput regimes, the tools and resources that offer the possibility of addressing these needs, and how these can be best utilized and accessed in the coming years.

Note:

Contribution to Snowmass 2021, 33 pages, 5 figures

References(163)

Figures(5)

[1]

A3D3 Institute.

•
- https://a3d3.ai/

[2]

Operation of the ATLAS trigger system in Run 2

ATLAS

Collaboration

•

Georges Aad
(
- Marseille, CPPM
)

et al.

- JINST 15 (2020) 10, P10004
•
e-Print:
- 2007.12539
•
DOI:
- 10.1088/1748-0221/15/10/P10004

[3]

The Phase-2 Upgrade of the CMS Level-1 Trigger

CMS

Collaboration

•

Alexandre Zabi
(
- Ecole Polytechnique
)

et al.

[4]

The Phase-2 Upgrade of the CMS Data Acquisition and High Level Trigger, Technical Design Report

CMS collaboration

Collaboration

[5]

Technical Design Report for the Phase-II Upgrade of the ATLAS TDAQ System

ATLAS

Collaboration

DOI:
- 10.17181/CERN.2LBB.4IAL

[6]

GPU coprocessors as a service for deep learning inference in high energy physics

Jeffrey Krupa
(
- MIT
)
,
Kelvin Lin
(
- Washington U., Seattle
)
,
Maria Acosta Flechas
(
- Fermilab
)
,
Jack Dinsmore
(
- MIT
)
,
Javier Duarte
(
- UC, San Diego
)

et al.

- Mach.Learn.Sci.Tech. 2 (2021) 3, 035005
•
e-Print:
- 2007.10359
•
DOI:
- 10.1088/2632-2153/abec21

[7]

Design and performance of the LHCb trigger and full real-time reconstruction in Run 2 of the LHC

LHCb

Collaboration

•

Roel Aaij
(
- Nikhef, Amsterdam
)

et al.

- JINST 14 (2019) 04, P04013
•
e-Print:
- 1812.10790
•
DOI:
- 10.1088/1748-0221/14/04/P04013

[8]

Streaming readout for next generation electron scattering experiments

Fabrizio Ameli
(
- INFN, Rome
)
,
Marco Battaglieri
(
- Jefferson Lab and
- INFN, Genoa
)
,
Vladimir V. Berdnikov
(
- Unlisted, US
)
,
Mariangela Bondì
(
- INFN, Genoa
)
,
Sergey Boyarinov
(
- Jefferson Lab
)

et al.

- Eur.Phys.J.Plus 137 (2022) 8, 958
•
e-Print:
- 2202.03085
•
DOI:
- 10.1140/epjp/s13360-022-03146-z

[9]

Machine learning at the energy and intensity frontiers of particle physics

Alexander Radovic
(
- William-Mary Coll.
)
,
Mike Williams
(
- MIT
)
,
David Rousseau
(
- Orsay, LAL
)
,
Michael Kagan
(
- SLAC
)
,
Daniele Bonacorsi
(
- U. Bologna (main) and
- INFN, Bologna
)

et al.

- Nature 560 (2018) 7716, 41-48
•
DOI:
- 10.1038/s41586-018-0361-2

[10]

A Review on Machine Learning for Neutrino Experiments

- Int.J.Mod.Phys.A 35 (2020) 33, 2043005
•
e-Print:
- 2008.01242
•
DOI:
- 10.1142/S0217751X20430058

[11]

Machine Learning in the Search for New Fundamental Physics

Georgia Karagiorgi
(
- Columbia U.
)
,
Gregor Kasieczka
(
- Hamburg U.
)
,
Scott Kravitz
(
- LBNL, Berkeley
)
,
Benjamin Nachman
(
- LBNL, Berkeley and
- UC, Berkeley
)
,
David Shih
(
- Rutgers U., Piscataway
)

e-Print:
- 2112.03769

[12]

Accelerating Deep Neural Networks for Real-time Data Selection for High-resolution Imaging Particle Detectors

Yeon-Jae Jwa
(
- Columbia U.
)
,
Giuseppe Di Guglielmo
(
- Columbia U.
)
,
Luca P. Carloni
(
- Columbia U.
)
,
Georgia Karagiorgi
(
- Columbia U.
)

[13]

A deep-learning based raw waveform region-of-interest finder for the liquid argon time projection chamber

ArgoNeuT

Collaboration

•

R. Acciarri
(
- Fermilab
)

et al.

- JINST 17 (2022) 01, P01018
•
e-Print:
- 2103.06391
•
DOI:
- 10.1088/1748-0221/17/01/P01018

[14]

Extracting low energy signals from raw LArTPC waveforms using deep learning techniques — A proof of concept

Lorenzo Uboldi
(
- CERN
)
,
David Ruth
(
- Unlisted, US, IL
)
,
Michael Andrews
(
- Carnegie Mellon U.
)
,
Michael H.L.S. Wang
(
- Fermilab
)
,
Hans Joachim Wenzel
(
- Fermilab
)

et al.

- Nucl.Instrum.Meth.A 1028 (2022) 166371
•
e-Print:
- 2106.09911
•
DOI:
- 10.1016/j.nima.2022.166371

[15]

A novel trigger based on neural networks for radio neutrino detectors

ARIANNA

Collaboration

•

Astrid Anker
(
- UC, Irvine
)

et al.

- PoS ICRC2021 (2021) 1074
•
DOI:
- 10.22323/1.395.1074

[16]

A Convolutional Neural Network Neutrino Event Classifier

et al.

- JINST 11 (2016) 09, P09001
•
e-Print:
- 1604.01444
•
DOI:
- 10.1088/1748-0221/11/09/P09001

[17]

Search for Active-Sterile Antineutrino Mixing Using Neutral-Current Interactions with the NOvA Experiment

NOvA

Collaboration

•

M.A. Acero
(
- U. Atlantico, Barranquilla
)

et al.

- Phys.Rev.Lett. 127 (2021) 20, 201801
•
e-Print:
- 2106.04673
•
DOI:
- 10.1103/PhysRevLett.127.201801

[18]

Search for an anomalous excess of charged-current quasielastic $ν_{e}$ interactions with the MicroBooNE experiment using Deep-Learning-based reconstruction

MicroBooNE

Collaboration

•

P. Abratenko
(
- Tufts U.
)

et al.

- Phys.Rev.D 105 (2022) 11, 112003
•
e-Print:
- 2110.14080
•
DOI:
- 10.1103/PhysRevD.105.112003

[19]

Improved Energy Reconstruction in NOvA with Regression Convolutional Neural Networks

- Phys.Rev.D 99 (2019) 1, 012011
•
e-Print:
- 1811.04557
•
DOI:
- 10.1103/PhysRevD.99.012011

[20]

Wire-cell 3D pattern recognition techniques for neutrino event reconstruction in large LArTPCs: algorithm description and quantitative evaluation with MicroBooNE simulation

MicroBooNE

Collaboration

•

P. Abratenko
(
- Tufts U.
)

et al.

- JINST 17 (2022) 01, P01037
•
e-Print:
- 2110.13961
•
DOI:
- 10.1088/1748-0221/17/01/P01037

[21]

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

KM3NeT

Collaboration

•

S. Aiello
(
- INFN, Catania
)

et al.

- JINST 15 (2020) 10, P10005
•
e-Print:
- 2004.08254
•
DOI:
- 10.1088/1748-0221/15/10/P10005

[22]

Deep Underground Neutrino Experiment (DUNE), Far Detector Technical Design Report, Volume I Introduction to DUNE

DUNE

Collaboration

•

Babak Abi
(
- Oxford U.
)

et al.

- JINST 15 (2020) 08, T08008
•
e-Print:
- 2002.02967
•
DOI:
- 10.1088/1748-0221/15/08/T08008

[23]

Design and Construction of the MicroBooNE Detector

MicroBooNE

Collaboration

•

R. Acciarri
(
- Fermilab
)

et al.

- JINST 12 (2017) 02, P02017
•
e-Print:
- 1612.05824
•
DOI:
- 10.1088/1748-0221/12/02/P02017

[24]

A Proposal for a Three Detector Short-Baseline Neutrino Oscillation Program in the Fermilab Booster Neutrino Beam

Collaborations

•

R. Acciarri
(
- Fermilab
)

et al.

e-Print:
- 1503.01520

[25]

Neutrino interaction classification with a convolutional neural network in the DUNE far detector

DUNE

Collaboration

•

B. Abi
(
- Oxford U.
)

et al.

- Phys.Rev.D 102 (2020) 9, 092003
•
e-Print:
- 2006.15052
•
DOI:
- 10.1103/PhysRevD.102.092003

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