CERN Accelerating science

Preprint
Report number	arXiv:2209.07559 ; FERMILAB-CONF-22-719-ND-PPD-QIS-SCD
Title	Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learning
Author(s)	Shanahan, Phiala (MIT) ; Terao, Kazuhiro (SLAC) ; Whiteson, Daniel (UC, Irvine) ; Aarts, Gert (Swansea U. ; ECT, Trento ; Fond. Bruno Kessler, Trento) ; Adelmann, Andreas (Northeastern U. ; PSI, Villigen) ; Akchurin, N. (Texas Tech.) ; Alexandru, Andrei (George Washington U. ; Maryland U.) ; Amram, Oz (Johns Hopkins U.) ; Andreassen, Anders (Google Inc.) ; Apresyan, Artur (Fermilab) ; Avestruz, Camille (Michigan U.) ; Bartoldus, Rainer (SLAC) ; Bechtol, Keith (Wisconsin U., Madison) ; Benkendorfer, Kees (LBL, Berkeley ; Reed Coll.) ; Benelli, Gabriele (Brown U.) ; Bernius, Catrin (SLAC) ; Bogatskiy, Alexander (New York U., CCPP) ; Bortolato, Blaz (Stefan Inst., Ljubljana) ; Boyda, Denis (Argonne (main) ; Harvard U. ; IAIFI, Cambridge) ; Brooijmans, Gustaaf (Nevis Labs, Columbia U.) ; Calafiura, Paolo (LBL, Berkeley) ; Calì, Salvatore (MIT ; Harvard U. ; IAIFI, Cambridge) ; Canelli, Florencia (Zurich U.) ; Chachamis, Grigorios (LIP) ; Chekanov, S.V. (Argonne (main)) ; Chen, Deming (Illinois U., Urbana) ; Chen, Thomas Y. (Columbia U.) ; Ćiprijanović, Aleksandra (Fermilab) ; Collins, Jack H. (SLAC) ; Connolly, J. Andrew (Washington U., Seattle) ; Coughlin, Michael (Minnesota U.) ; Dai, Biwei (UC, Berkeley ; LBL, Berkeley) ; Damgov, J. (Texas Tech.) ; DeZoort, Gage (Princeton U.) ; Diaz, Daniel (UC, San Diego) ; Dillon, Barry M. (Stefan Inst., Ljubljana ; Heidelberg U.) ; Dinu, Ioan-Mihail (Johns Hopkins U.) ; Dong, Zhongtian (Kansas U.) ; Donini, Julien (Clermont-Ferrand U.) ; Duarte, Javier (UC, San Diego) ; Dugad, S. (Tata Inst.) ; Dvorkin, Cora (Harvard U.) ; Faroughy, D.A. (Zurich U.) ; Feickert, Matthew (UC, San Diego) ; Feng, Yongbin (Fermilab) ; Fenton, Michael (UC, Irvine) ; Foreman, Sam (Argonne (main)) ; De Freitas, Felipe F. (Aveiro U.) ; Funcke, Lena (MIT ; Harvard U. ; IAIFI, Cambridge ; MIT, Cambridge, CTP ; Unlisted) ; C, P.g. (Texas Tech.) ; Gandrakota, Abhijith (Fermilab) ; Ganguly, Sanmay (Tokyo U., ICEPP) ; Garrison, Lehman H. (New York U., CCPP) ; Gessner, Spencer (SLAC) ; Ghosh, Aishik (UC, Irvine) ; Gonsk, Julia (Nevis Labs, Columbia U.) ; Graham, Matthew (Caltech) ; Gray, Lindsey (Fermilab) ; Grönroos, S. (CERN) ; Hackett, Daniel C. (MIT ; Harvard U. ; IAIFI, Cambridge) ; Harris, Philip (MIT) ; Hauck, Scott (Washington U., Seattle) ; Herwig, Christian (Fermilab) ; Holzman, Burt (Fermilab) ; Hopkins, Walter (Argonne (main)) ; Hsu, Shih-Chieh (Washington U., Seattle) ; Huang, Jin (Brookhaven) ; Huang, Yi (Brookhaven) ; Jin, Xiao-Yong (Argonne (main)) ; Kagan, Michael (SLAC) ; Kah, Alan (Nevis Labs, Columbia U.) ; Kamenik, Jernej F. (Stefan Inst., Ljubljana ; Ljubljana U.) ; Kansal, Raghav (UC, San Diego) ; Karagiorgi, Georgia (Columbia U.) ; Kasieczka, Gregor (Hamburg U.) ; Katsavounidis, Erik (MIT) ; Khoda, Elham E. (Washington U., Seattle) ; Khosa, Charanjit K. (Sussex U. ; INFN, Genoa) ; Kipf, Thomas (Technion) ; Komiske, Patrick (MIT) ; Komm, Matthias (CERN) ; Kondor, Risi (Chicago U.) ; Kourlitis, Evangelos (Argonne (main)) ; Krause, Claudius (Rutgers U., Piscataway) ; Lamichhane, K. (Texas Tech.) ; Le Pottier, Luc (LBL, Berkeley ; Michigan U.) ; Lin, Meifeng (Brookhaven) ; Lin, Yin (MIT ; Harvard U. ; IAIFI, Cambridge) ; Liu, Mia (Purdue U.) ; Lu, Nan (Caltech) ; Lucini, Biagio (Swansea U., Math. Dept. ; Swansea U.) ; Martinez, J. (Texas Tech.) ; Martín-Ramiro, Pablo (LBL, Berkeley ; Madrid, IFT) ; Matevc, Andrej (Stefan Inst., Ljubljana ; Ljubljana U.) ; McCormack, William Patrick (MIT) ; Metodiev, Eric (MIT) ; Mikuni, Vinicius (Zurich U.) ; Miller, David W. (Chicago U.) ; Mishra-Sharma, Siddharth (Harvard U. ; IAIFI, Cambridge ; Maryland U.) ; Mukherjee, Samadrita (Tata Inst.) ; Murnane, Daniel (LBL, Berkeley) ; Nachman, Benjamin (LBL, Berkeley ; UC, Berkeley) ; Narayan, Gautham (Illinois U., Urbana) ; Neubauer, Mark (Illinois U., Urbana) ; Ngadiuba, Jennifer (Fermilab) ; Norberg, Scarlet (Puerto Rico U., Mayaguez) ; Nord, Brian (Fermilab ; Texas Tech.) ; Ochoa, Inês (LIP, Lisbon) ; Offermann, Jan T. (Chicago U.) ; Park, Sang Eon (MIT) ; Pedro, Kevin (Fermilab) ; Peña, Cristían (Fermilab) ; Perloff, Alexx (Colorado U.) ; Pettee, Mariel (LBL, Berkeley) ; Pierini, Maurizio (CERN) ; Quast, T. (CERN) ; Rankin, Dylan (MIT) ; Ren, Yihui (Brookhaven) ; Rieger, Marcel (CERN) ; Vlimant, Jean-Roch (Caltech) ; Roy, Avik (Illinois U., Urbana) ; Sanz, Veronica (Sussex U. ; Valencia U., IFIC) ; Sarda, Nilai (MIT) ; Savard, Claire (Colorado U.) ; Scheinker, Alexander (Los Alamos) ; Uros ; Seljak (LBL, Berkeley ; UC, Berkeley) ; Sheldon, Brian (UC, San Diego) ; Shih, David (Rutgers U., Piscataway) ; Shimmin, Chase (Yale U.) ; Smolkovic, Aleks (Stefan Inst., Ljubljana) ; Stein, George (LBL, Berkeley ; UC, Berkeley) ; Mantilla Suarez, Cristina (Fermilab) ; Szewc, Manuel (ICAS, UNSAM, Buenos Aires) ; Thais, Savannah (Princeton U.) ; Thaler, Jesse (MIT) ; Torbunov, Dmitrii (Brookhaven) ; Tran, Nhan (Fermilab) ; Tsan, Steven (UC, San Diego) ; Udrescu, Silviu-Marian (MIT) ; Undleeb, S. (Texas Tech.) ; Vaslin, Louis (Clermont-Ferrand U.) ; Villaescusa-Navarro, Francisco (New York U., CCPP ; Princeton U.) ; Villar, V.Ashley (Penn State U.) ; Viren, Brett (Brookhaven) ; Vlimant, Jean-Roch (SLAC ; Caltech) ; Whitbeck, A. (Texas Tech.) ; Williams, Daniel (Nevis Labs, Columbia U.) ; Winklehner, Daniel (MIT) ; Xie, Si (Caltech) ; Yang, Tingjun (Fermilab) ; Yu, Haiwang (Brookhaven) ; Yunus, Mikaeel (MIT)
Imprint	2022-09-15
Note	Contribution to Snowmass 2021
Presented at	2021 Snowmass Summer Study, Seattle, WA, United States, 11 - 20 July 2021, pp.
Subject category	hep-th ; Particle Physics - Theory ; hep-lat ; Particle Physics - Lattice ; hep-ex ; Particle Physics - Experiment ; cs.AI ; Computing and Computers ; physics.comp-ph ; Other Fields of Physics
Abstract	The rapidly-developing intersection of machine learning (ML) with high-energy physics (HEP) presents both opportunities and challenges to our community. Far beyond applications of standard ML tools to HEP problems, genuinely new and potentially revolutionary approaches are being developed by a generation of talent literate in both fields. There is an urgent need to support the needs of the interdisciplinary community driving these developments, including funding dedicated research at the intersection of the two fields, investing in high-performance computing at universities and tailoring allocation policies to support this work, developing of community tools and standards, and providing education and career paths for young researchers attracted by the intellectual vitality of machine learning for high energy physics.
Other source	Inspire
Copyright/License	preprint: (License: CC BY 4.0)