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Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learning / 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) et al.
The rapidly-developing intersection of machine learning (ML) with high-energy physics (HEP) presents both opportunities and challenges to our community. [...]
arXiv:2209.07559 ; FERMILAB-CONF-22-719-ND-PPD-QIS-SCD. -
Fermilab Library Server - eConf - Fulltext - Fulltext

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The LHC Olympics 2020 a community challenge for anomaly detection in high energy physics / Kasieczka, Gregor (Hamburg U.) ; Nachman, Benjamin (LBL, Berkeley ; UC, Berkeley, Miller Inst.) ; Shih, David (Rutgers U., Piscataway) ; Amram, Oz (Johns Hopkins U.) ; Andreassen, Anders (Google Inc.) ; Benkendorfer, Kees (LBL, Berkeley ; Reed Coll.) ; Bortolato, Blaz (Stefan Inst., Ljubljana) ; Brooijmans, Gustaaf (Nevis Labs, Columbia U.) ; Canelli, Florencia (Zurich U.) ; Collins, Jack H. (SLAC) et al.
A new paradigm for data-driven, model-agnostic new physics searches at colliders is emerging, and aims to leverage recent breakthroughs in anomaly detection and machine learning. In order to develop and benchmark new anomaly detection methods within this framework, it is essential to have standard datasets. [...]
arXiv:2101.08320.- 2021-12-07 - 108 p. - Published in : Rep. Prog. Phys. 84 (2021) 124201 Fulltext: PDF;

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