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CERN Document Server	4 のレコードが見つかりました。	検索にかかった時間： 0.67 秒

Review of particle physics / Particle Data Group Collaboration
The Review summarizes much of particle physics and cosmology. Using data from previous editions, plus 2,717 new measurements from 869 papers, we list, evaluate, and average measured properties of gauge bosons and the recently discovered Higgs boson, leptons, quarks, mesons, and baryons [...]
2024 - 2382 p. - Published in : Phys. Rev. D 110 (2024) 030001 Fulltext: PDF;

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Workshop on a future muon program at FNAL / Corrodi, S. (Argonne) ; Oksuzian, Y. (Argonne) ; Edmonds, A. (Boston U.) ; Miller, J. (Boston U.) ; Tran, H.N. (Boston U.) ; Bonventre, R. (LBL, Berkeley) ; Brown, D.N. (LBL, Berkeley) ; Méot, F. (Brookhaven) ; Singh, V. (UC, Berkeley) ; Kolomensky, Y. (LBNL, Berkeley) et al.
The Snowmass report on rare processes and precision measurements recommended Mu2e-II and a next generation muon facility at Fermilab (Advanced Muon Facility) as priorities for the frontier. [...]
arXiv:2309.05933 ; FERMILAB-CONF-23-464-PPD ; CALT-TH-2023-036. - 68.
Fermilab Library Server - Fulltext - Fulltext

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Review of Particle Physics : 2022 / Workman, R.L.
The Review summarizes much of particle physics and cosmology [...]
Oxford : Oxford University Press, 2022 - 2270.

10.1093/ptep/ptac097

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Applications and Techniques for Fast Machine Learning in Science / Deiana, Allison McCarn (Southern Methodist U.) ; Tran, Nhan (Fermilab ; Northwestern U. (main)) ; Agar, Joshua (Lehigh U. (main)) ; Blott, Michaela (Xilinx, Dublin) ; Di Guglielmo, Giuseppe (Columbia U. (main)) ; Duarte, Javier (UC, San Diego) ; Harris, Philip (MIT) ; Hauck, Scott (George Washington U. (main)) ; Liu, Mia (Purdue U.) ; Neubauer, Mark S. (Illinois U., Urbana) et al.
In this community review report, we discuss applications and techniques for fast machine learning (ML) in science -- the concept of integrating power ML methods into the real-time experimental data processing loop to accelerate scientific discovery. The material for the report builds on two workshops held by the Fast ML for Science community and covers three main areas: applications for fast ML across a number of scientific domains; techniques for training and implementing performant and resource-efficient ML algorithms; and computing architectures, platforms, and technologies for deploying these algorithms. [...]
arXiv:2110.13041; FERMILAB-PUB-21-502-AD-E-SCD.- 2022-04-12 - 56 p. - Published in : Front. Big Data 5 (2022) 787421 Fulltext: 2110.13041 - PDF; fermilab-pub-21-502-ad-e-scd - PDF; Fulltext from Publisher: PDF; External link: Fermilab Library Server

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似た著者名：
3	Bonventre, R
1	Bonventre, Richard J.

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