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CERN Document Server 7 notices trouvées  La recherche a duré 0.67 secondes. 
1.
CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simulation / Krause, Claudius (ed.) (Vienna, OAW ; Heidelberg U.) ; Faucci Giannelli, Michele (ed.) (INFN, Rome2 ; Chalmers U. Tech.) ; Kasieczka, Gregor (ed.) (Hamburg U.) ; Nachman, Benjamin (ed.) (LBNL, Berkeley) ; Salamani, Dalila (ed.) (CERN) ; Shih, David (ed.) (Rutgers U., Piscataway) ; Zaborowska, Anna (ed.) (CERN) ; Amram, Oz (Fermilab) ; Borras, Kerstin (DESY ; Aachen, Tech. Hochsch.) ; Buckley, Matthew R. (Rutgers U., Piscataway) et al.
We present the results of the "Fast Calorimeter Simulation Challenge 2022" - the CaloChallenge. [...]
arXiv:2410.21611 ; HEPHY-ML-24-05 ; FERMILAB-PUB-24-0728-CMS ; TTK-24-43. - 204.
Fermilab Library Server - Fulltext - Fulltext
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2.
Normalizing Flows for High-Dimensional Detector Simulations / Ernst, Florian (U. Heidelberg, ITP ; CERN) ; Favaro, Luigi (U. Heidelberg, ITP) ; Krause, Claudius (U. Heidelberg, ITP ; Vienna, OAW) ; Plehn, Tilman (U. Heidelberg, ITP) ; Shih, David (Rutgers U., Piscataway)
Whenever invertible generative networks are needed for LHC physics, normalizing flows show excellent performance. [...]
arXiv:2312.09290. - 24 p.
Fulltext
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3.
Exploiting Differentiable Programming for the End-to-end Optimization of Detectors / MODE Collaboration
The coming of age of differentiable programming makes possible today to create complete computer models of experimental apparatus that include the stochastic data-generation processes, the full modeling of the reconstruction and inference procedures, and a suitably defined objective function, along with the cost of any given detector configuration, geometry and materials. [...]
2022. - 8 p.
Fulltext
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4.
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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5.
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New developments in fast simulation with machine learning / Krause, Claudius (speaker) (Rutgers University)
2022 - 1438. Conferences; 10th Edition of the Large Hadron Collider Physics Conference External links: Talk details; Event details In : 10th Edition of the Large Hadron Collider Physics Conference
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6.
Event Generators for High-Energy Physics Experiments / Campbell, J.M. (Fermilab) ; Diefenthaler, M. (Jefferson Lab) ; Hobbs, T.J. (Fermilab ; IIT, Chicago) ; Höche, Stefan (Fermilab) ; Isaacson, Joshua (Fermilab) ; Kling, Felix (DESY) ; Mrenna, Stephen (Fermilab) ; Reuter, J. (DESY) ; Alioli, S. (Milan Bicocca U. ; INFN, Milan Bicocca) ; Andersen, J.R. (Durham U., IPPP) et al.
We provide an overview of the status of Monte-Carlo event generators for high-energy particle physics. Guided by the experimental needs and requirements, we highlight areas of active development, and opportunities for future improvements. [...]
arXiv:2203.11110; CP3-22-12; DESY-22-042; FERMILAB-PUB-22-116-SCD-T; IPPP/21/51, JLAB-PHY-22-3576; KA-TP-04-2022; LA-UR-22-22126; LU-TP-22-12; MCNET-22-04, OUTP-22-03P; P3H-22-024; PITT-PACC 2207; UCI-TR-2022-02.- 2024-05-24 - 225 p. - Published in : 10.21468/SciPostPhys.16.5.130 Fulltext: jt - PDF; 2203.11110 - PDF; Fulltext from Publisher: PDF; External links: JLab Document Server; Fermilab Library Server; eConf
In : 2021 Snowmass Summer Study, Seattle, WA, United States, 11 - 20 July 2021, pp.
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7.
Toward the end-to-end optimization of particle physics instruments with differentiable programming / MODE Collaboration
The full optimization of the design and operation of instruments whose functioning relies on the interaction of radiation with matter is a super-human task, given the large dimensionality of the space of possible choices for geometry, detection technology, materials, data-acquisition, and information-extraction techniques, and the interdependence of the related parameters. On the other hand, massive potential gains in performance over standard, "experience-driven" layouts are in principle within our reach if an objective function fully aligned with the final goals of the instrument is maximized by means of a systematic search of the configuration space. [...]
arXiv:2203.13818.- 2023-05-25 - 56 p. - Published in : Rev. Phys. 10 (2023) 100085 Fulltext: 2203.13818 - PDF; Publication - PDF;
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Voir aussi: noms d'auteurs similaires
6 Krause, C
2 Krause, C.
6 Krause, Christian
2 Krause, Christina
1 Krause, Christina M
10 Krause, Christopher
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