Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learning

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 gener...

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Detalles Bibliográficos
Autores principales: Shanahan, Phiala, Terao, Kazuhiro, Whiteson, Daniel, Aarts, Gert, Adelmann, Andreas, Akchurin, N., Alexandru, Andrei, Amram, Oz, Andreassen, Anders, Apresyan, Artur, Avestruz, Camille, Bartoldus, Rainer, Bechtol, Keith, Benkendorfer, Kees, Benelli, Gabriele, Bernius, Catrin, Bogatskiy, Alexander, Bortolato, Blaz, Boyda, Denis, Brooijmans, Gustaaf, Calafiura, Paolo, Calì, Salvatore, Canelli, Florencia, Chachamis, Grigorios, Chekanov, S.V., Chen, Deming, Chen, Thomas Y., Ćiprijanović, Aleksandra, Collins, Jack H., Connolly, J. Andrew, Coughlin, Michael, Dai, Biwei, Damgov, J., DeZoort, Gage, Diaz, Daniel, Dillon, Barry M., Dinu, Ioan-Mihail, Dong, Zhongtian, Donini, Julien, Duarte, Javier, Dugad, S., Dvorkin, Cora, Faroughy, D.A., Feickert, Matthew, Feng, Yongbin, Fenton, Michael, Foreman, Sam, De Freitas, Felipe F., Funcke, Lena, C, P.g., Gandrakota, Abhijith, Ganguly, Sanmay, Garrison, Lehman H., Gessner, Spencer, Ghosh, Aishik, Gonsk, Julia, Graham, Matthew, Gray, Lindsey, Grönroos, S., Hackett, Daniel C., Harris, Philip, Hauck, Scott, Herwig, Christian, Holzman, Burt, Hopkins, Walter, Hsu, Shih-Chieh, Huang, Jin, Huang, Yi, Jin, Xiao-Yong, Kagan, Michael, Kah, Alan, Kamenik, Jernej F., Kansal, Raghav, Karagiorgi, Georgia, Kasieczka, Gregor, Katsavounidis, Erik, Khoda, Elham E., Khosa, Charanjit K., Kipf, Thomas, Komiske, Patrick, Komm, Matthias, Kondor, Risi, Kourlitis, Evangelos, Krause, Claudius, Lamichhane, K., Le Pottier, Luc, Lin, Meifeng, Lin, Yin, Liu, Mia, Lu, Nan, Lucini, Biagio, Martinez, J., Martín-Ramiro, Pablo, Matevc, Andrej, McCormack, William Patrick, Metodiev, Eric, Mikuni, Vinicius, Miller, David W., Mishra-Sharma, Siddharth, Mukherjee, Samadrita, Murnane, Daniel, Nachman, Benjamin, Narayan, Gautham, Neubauer, Mark, Ngadiuba, Jennifer, Norberg, Scarlet, Nord, Brian, Ochoa, Inês, Offermann, Jan T., Park, Sang Eon, Pedro, Kevin, Peña, Cristían, Perloff, Alexx, Pettee, Mariel, Pierini, Maurizio, Quast, T., Rankin, Dylan, Ren, Yihui, Rieger, Marcel, Vlimant, Jean-Roch, Roy, Avik, Sanz, Veronica, Sarda, Nilai, Savard, Claire, Scheinker, Alexander, Uros, Seljak, Sheldon, Brian, Shih, David, Shimmin, Chase, Smolkovic, Aleks, Stein, George, Mantilla Suarez, Cristina, Szewc, Manuel, Thais, Savannah, Thaler, Jesse, Torbunov, Dmitrii, Tran, Nhan, Tsan, Steven, Udrescu, Silviu-Marian, Undleeb, S., Vaslin, Louis, Villaescusa-Navarro, Francisco, Villar, V.Ashley, Viren, Brett, Whitbeck, A., Williams, Daniel, Winklehner, Daniel, Xie, Si, Yang, Tingjun, Yu, Haiwang, Yunus, Mikaeel
Lenguaje:eng
Publicado: 2022
Materias:
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
Acceso en línea:http://cds.cern.ch/record/2827613
Descripción
Sumario: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.

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