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HEP Software Foundation Community White Paper Working Group - Data and Software Preservation to Enable Reuse
/ Hildreth, M.D. (Notre Dame U.) ; Boehnlein, A. (Jefferson Lab) ; Cranmer, K. (New York U.) ; Dallmeier-Tiessen, S. (CERN) ; Gardner, R. (Chicago U.) ; Hacker, T. (Purdue U.) ; Heinrich, L. (New York U.) ; Jimenez, I. (UC, Santa Cruz) ; Kane, M. (Unlisted, DE) ; Katz, D.S. (NCSA, Urbana) et al.
In this chapter of the High Energy Physics Software Foundation Community Whitepaper, we discuss the current state of infrastructure, best practices, and ongoing developments in the area of data and software preservation in high energy physics. [...]
arXiv:1810.01191 ; HSF-CWP-2017-06 ; FERMILAB-FN-1060-CD.
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2018.
Fermilab Library Server (fulltext available) - Fulltext - Fulltext
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The present and future of QCD
/ Achenbach, P. (Jefferson Lab) ; Adhikari, D. (Virginia Tech.) ; Afanasev, A. (George Washington U. ; Jefferson Lab) ; Afzal, F. (Bonn U., HISKP ; Bonn U.) ; Aidala, C.A. (Michigan U.) ; Al-bataineh, A. (Jordan U. Sci. Tech. ; Yarmouk U. ; Kansas U.) ; Almaalol, D.K. (Illinois U., Urbana ; Illinois U., Urbana (main)) ; Amaryan, M. (Old Dominion U. ; Old Dominion U. (main)) ; Androić, D. (Zagreb U.) ; Armstrong, W.R. (Argonne ; Argonne, PHY) et al.
This White Paper presents the community inputs and scientific conclusions from the Hot and Cold QCD Town Meeting that took place September 23-25, 2022 at MIT, as part of the Nuclear Science Advisory Committee (NSAC) 2023 Long Range Planning process. A total of 424 physicists registered for the meeting. [...]
arXiv:2303.02579; JLAB-PHY-23-3808.-
2024-04-15 - 111 p.
- Published in : Nucl. Phys. A 1047 (2024) 122874
Fulltext: 2303.02579 - PDF; Publication - PDF; External link: JLab Document Server
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Snowmass 2021 Computational Frontier CompF4 Topical Group Report: Storage and Processing Resource Access
/ Bhimji, W. (LBL, Berkeley) ; Bhimij, W. (LBL, Berkeley) ; Carder, D. (UC, Berkeley) ; Dart, E. (UC, Berkeley) ; Duarte, J. (UC, San Diego) ; Fisk, I. (Flatiron Inst., New York) ; Gardner, R. (Chicago U.) ; Guok, C. (UC, Berkeley) ; Jayatilaka, B. (Fermilab) ; Lehman, T. (UC, Berkeley) et al.
Computing plays a significant role in all areas of high energy physics. The Snowmass 2021 CompF4 topical group's scope is facilities R&D;, where we consider "facilities" as the computing hardware and software infrastructure inside the data centers plus the networking between data centers, irrespective of who owns them, and what policies are applied for using them. [...]
arXiv:2209.08868; FERMILAB-PUB-22-715-SCD.-
2023-04-26 - 52 p.
- Published in : Comput. Softw. Big Sci. 7 (2023) 5
Fulltext: ca5207741f54c3b335f8db28cb95fffa - PDF; 2209.08868 - PDF; Fulltext from Publisher: PDF; External link: Fermilab Library Server
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Software Training in HEP
/ Malik, Sudhir (Puerto Rico U., Mayaguez) ; Meehan, Samuel (CERN) ; Lieret, Kilian (LMU Munich (main)) ; Evans, Meirin Oan (Sussex U.) ; Villanueva, Michel H. (Mississippi U.) ; Katz, Daniel S. (Illinois U., Urbana) ; Stewart, Graeme A. (CERN) ; Elmer, Peter (Princeton U.) ; Aziz, Sizar (IJCLab, Orsay) ; Bellis, Matthew (Siena Coll., Loudonville) et al.
Long term sustainability of the high energy physics (HEP) research software ecosystem is essential for the field. With upgrades and new facilities coming online throughout the 2020s this will only become increasingly relevant throughout this decade [...]
arXiv:2103.00659.-
2021-10-08 - 7 p.
- Published in : Comput. Softw. Big Sci.: 5 (2021) , no. 1, pp. 22
Fulltext: document - PDF; 2103.00659 - PDF;
In : 25th International Conference on Computing in High-Energy and Nuclear Physics (CHEP), Online, Online, 17 - 21 May 2021, pp.22
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Muon Collider Forum Report
/ Black, K.M. (U. Wisconsin, Madison (main)) ; Jindariani, S. (Fermilab) ; Li, D. (LBNL, Berkeley) ; Maltoni, F. (Louvain U., CP3 ; INFN, Bologna ; U. Bologna, DIFA) ; Meade, P. (YITP, Stony Brook) ; Stratakis, D. (Fermilab) ; Acosta, D. (Rice U.) ; Agarwal, R. (LBNL, Berkeley) ; Agashe, K. (Maryland U.) ; Aimè, C. (Pavia U.) et al.
A multi-TeV muon collider offers a spectacular opportunity in the direct exploration of the energy frontier. Offering a combination of unprecedented energy collisions in a comparatively clean leptonic environment, a high energy muon collider has the unique potential to provide both precision measurements and the highest energy reach in one machine that cannot be paralleled by any currently available technology. [...]
arXiv:2209.01318; FERMILAB-FN-1194.-
2024-02-23 - 94 p.
- Published in : JINST 19 (2024) T02015
Fulltext: 2209.01318 - PDF; f9a96f4855e018cb1a1bb8d4d6ebbc35 - PDF; Fulltext from Publisher: PDF; External link: Fermilab Library Server
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Distributed statistical inference with pyhf enabled through funcX
/ Feickert, Matthew (Illinois U., Urbana) ; Heinrich, Lukas (CERN) ; Stark, Giordon (UC, Santa Cruz, Inst. Part. Phys.) ; Galewsky, Ben (NCSA, Urbana)
In High Energy Physics facilities that provide High Performance Computing environments provide an opportunity to efficiently perform the statistical inference required for analysis of data from the Large Hadron Collider, but can pose problems with orchestration and efficient scheduling. The compute architectures at these facilities do not easily support the Python compute model, and the configuration scheduling of batch jobs for physics often requires expertise in multiple job scheduling services. [...]
arXiv:2103.02182.-
2021 - 10 p.
- Published in : EPJ Web Conf.: 251 (2021) , pp. 02070
Fulltext: 2103.02182 - PDF; document - PDF;
In : 25th International Conference on Computing in High-Energy and Nuclear Physics (CHEP), Online, Online, 17 - 21 May 2021, pp.02070
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Learning from the Pandemic: the Future of Meetings in HEP and Beyond
/ Neubauer, Mark S. (Illinois U., Urbana (main)) ; Adams, Todd (Florida State U.) ; Adelman-McCarthy, Jennifer (Fermilab) ; Benelli, Gabriele (Brown U.) ; Bose, Tulika (Wisconsin U., Madison) ; Britton, David (Glasgow U.) ; Burchat, Pat (Stanford U., ITP) ; Butler, Joel (Fermilab) ; Cartwright, Timothy A. (Wisconsin U., Madison) ; Davídek, Tomáš (Charles U.) et al.
The COVID-19 pandemic has by-and-large prevented in-person meetings since March 2020. [...]
arXiv:2106.15783 ; FERMILAB-PUB-21-308-ND-PPD-SCD.
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55 p.
Fermilab Library Server - Fulltext - Fulltext
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Calorimetry with Deep Learning: Particle Simulation and Reconstruction for Collider Physics
/ Belayneh, Dawit (U. Chicago (main)) ; Carminati, Federico (CERN) ; Farbin, Amir (Texas U., Arlington (main)) ; Hooberman, Benjamin (Illinois U., Urbana (main)) ; Khattak, Gulrukh (CERN ; Unlisted, PK) ; Liu, Miaoyuan (Fermilab) ; Liu, Junze (Illinois U., Urbana (main)) ; Olivito, Dominick (UC, San Diego (main)) ; Barin Pacela, Vitória (U. Helsinki (main)) ; Pierini, Maurizio (CERN) et al.
Using detailed simulations of calorimeter showers as training data, we investigate the use of deep learning algorithms for the simulation and reconstruction of particles produced in high-energy physics collisions. We train neural networks on shower data at the calorimeter-cell level, and show significant improvements for simulation and reconstruction when using these networks compared to methods which rely on currently-used state-of-the-art algorithms. [...]
arXiv:1912.06794; FERMILAB-PUB-20-448-CMS.-
2020-07-31 - 31 p.
- Published in : Eur. Phys. J. C 80 (2020) 688
Article from SCOAP3: scoap3-fulltext - PDF; scoap - PDF; Fulltext: fermilab-pub-20-448-cms - PDF; 1912.06794 - PDF; Fulltext from Publisher: PDF;
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