CERN Accelerating science

CERN Document Server Sök i 19 journaler efter:  1 - 10nästa  gå till journal: Sökningen tog 0.64 sekunder. 
1.
RD53 pixel chips for the ATLAS and CMS Phase-2 upgrades at HL-LHC / Loddo, F (INFN, Bari) ; Andreazza, A (Milan U. ; INFN, Milan) ; Arteche, F (Sao Paulo, Inst. Tech. Aeronautics) ; Barbero, M B (Marseille, CPPM) ; Barillon, P (Marseille, CPPM) ; Beccherle, R (INFN, Pisa) ; Bilei, G M (INFN, Perugia ; Perugia U.) ; Bjalas, W (CERN) ; Bonaldo, S (INFN, Padua ; Padua U.) ; Bortoletto, D (Oxford U.) et al.
The Phase-2 upgrades at the High-Luminosity LHC of ATLAS and CMS experiments at CERN will require a new tracker with readout electronics operating in extremely harsh radiation environment (1 Grad), high hit rate (3.5 GHz/cm2) and high data rate readout (5 Gb/s). The RD53 collaboration is a joint effort between the ATLAS and CMS to qualify the chosen 65 nm CMOS technology in high radiation environment and develop the pixel readout chips of both experiments. [...]
FERMILAB-PUB-24-0469-PPD.- 2024 - 5 p. - Published in : Nucl. Instrum. Methods Phys. Res., A 1067 (2024) 169682
In : PSD13: The 13th International Conference on Position Sensitive Detectors, Oxford, United Kingdom, 3 - 9 Sep 2023, pp.169682
2.
Timing Performance of the CMS High Granularity Calorimeter Prototype / CMS HGCAL Collaboration
This paper describes the experience with the calibration, reconstruction and evaluation of the timing capabilities of the CMS HGCAL prototype in the beam tests in 2018. The calibration procedure includes multiple steps and corrections ranging from tens of nanoseconds to a few hundred picoseconds. [...]
arXiv:2312.14622.- 2024-04-11 - 22 p. - Published in : JINST 19 (2024) P04015 Fulltext: PDF; Fulltext from Publisher: PDF;
3.
Neutron irradiation and electrical characterisation of the first 8” silicon pad sensor prototypes for the CMS calorimeter endcap upgrade / CMS HGCAL Collaboration
As part of its HL-LHC upgrade program, the CMS collaboration is replacing its existing endcap calorimeters with a high-granularity calorimeter (CE). The new calorimeter is a sampling calorimeter with unprecedented transverse and longitudinal readout for both electromagnetic and hadronic compartments. [...]
arXiv:2209.10159.- 2023-08-24 - 19 p.
- Published in : JINST Fulltext: 33df321261c1de65891ca8b290f4b7a2 - PDF; 2209.10159 - PDF; Fulltext from Publisher: PDF; External link: Fermilab Library Server
4.
Performance of the CMS High Granularity Calorimeter prototype to charged pion beams of 20–300 GeV/c / CALICE Collaboration
The upgrade of the CMS experiment for the high luminosity operation of the LHC comprises the replacement of the current endcap calorimeter by a high granularity sampling calorimeter (HGCAL). The electromagnetic section of the HGCAL is based on silicon sensors interspersed between lead and copper (or copper tungsten) absorbers. [...]
arXiv:2211.04740.- 2023-08-14 - 31 p. - Published in : JINST 18 (2023) P08014 Fulltext: 2211.04740 - PDF; FERMILAB-PUB-23-299-ETD-PPD - PDF; Fulltext from Publisher: PDF; External link: Fermilab Library Server
5.
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
6.
Response of a CMS HGCAL silicon-pad electromagnetic calorimeter prototype to 20–300 GeV positrons / CMS HGCAL Collaboration
The Compact Muon Solenoid Collaboration is designing a new high-granularity endcap calorimeter, HGCAL, to be installed later this decade. As part of this development work, a prototype system was built, with an electromagnetic section consisting of 14 double-sided structures, providing 28 sampling layers. [...]
arXiv:2111.06855; FERMILAB-PUB-22-427-PPD.- 2022-05-12 - 29 p. - Published in : JINST 17 (2022) P05022 Fulltext: 2111.06855 - PDF; document - PDF; cbd097f03712745131ad048fd9099ac7 - PDF; Fulltext from Publisher: PDF; External link: Fermilab Library Server
7.
A reconfigurable neural network ASIC for detector front-end data compression at the HL-LHC / Di Guglielmo, Giuseppe (Columbia U.) ; Fahim, Farah (Fermilab ; Northwestern U.) ; Herwig, Christian (Fermilab) ; Valentin, Manuel Blanco (Northwestern U.) ; Duarte, Javier (UC, San Diego) ; Gingu, Cristian (Fermilab) ; Harris, Philip (MIT) ; Hirschauer, James (Fermilab) ; Kwok, Martin (Brown U.) ; Loncar, Vladimir (CERN ; Belgrade, Inst. Phys.) et al.
Despite advances in the programmable logic capabilities of modern trigger systems, a significant bottleneck remains in the amount of data to be transported from the detector to off-detector logic where trigger decisions are made. We demonstrate that a neural network autoencoder model can be implemented in a radiation tolerant ASIC to perform lossy data compression alleviating the data transmission problem while preserving critical information of the detector energy profile. [...]
arXiv:2105.01683; FERMILAB-PUB-21-217-CMS-E-SCD.- 2021-06-07 - 9 p. - Published in : IEEE Trans. Nucl. Sci. 68 (2021) 2179 Fulltext: 5a82c8a6b63c02fc015568642085785c - PDF; 2105.01683 - PDF; fermilab-pub-21-217-cms-e-scd - PDF; External link: Fermilab Accepted Manuscript
8.
The DAQ system of the 12,000 Channel CMS High Granularity Calorimeter Prototype / CMS HGCAL Collaboration
The CMS experiment at the CERN LHC will be upgraded to accommodate the 5-fold increase in the instantaneous luminosity expected at the High-Luminosity LHC (HL-LHC). Concomitant with this increase will be an increase in the number of interactions in each bunch crossing and a significant increase in the total ionising dose and fluence. [...]
arXiv:2012.03876.- 2021-04-15 - 20 p. - Published in : JINST 16 (2021) T04001 Fulltext: fermilab-pub-21-599-cms - PDF; 2012.03876 - PDF; Fulltext from publisher: PDF; External link: Preprint
9.
hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices / Fahim, Farah (Northwestern U. ; Fermilab) ; Hawks, Benjamin (Fermilab) ; Herwig, Christian (Fermilab) ; Hirschauer, James (Fermilab) ; Jindariani, Sergo (Fermilab) ; Tran, Nhan (Fermilab) ; Carloni, Luca P. (Columbia U.) ; Di Guglielmo, Giuseppe (Columbia U.) ; Harris, Philip (MIT) ; Krupa, Jeffrey (MIT) et al.
Accessible machine learning algorithms, software, and diagnostic tools for energy-efficient devices and systems are extremely valuable across a broad range of application domains. [...]
arXiv:2103.05579 ; FERMILAB-CONF-21-080-SCD.
- 10 p.
Fermilab Library Server - Fulltext - Fulltext
10.
Construction and commissioning of CMS CE prototype silicon modules / CMS CE Collaboration
As part of its HL-LHC upgrade program, the CMS Collaboration is developing a High Granularity Calorimeter (CE) to replace the existing endcap calorimeters. The CE is a sampling calorimeter with unprecedented transverse and longitudinal readout for both electromagnetic (CE-E) and hadronic (CE-H) compartments. [...]
arXiv:2012.06336.- 2021-04-20 - 39 p. - Published in : JINST 16 (2021) T04002 Fulltext: fermilab-pub-21-430-cms - PDF; 2012.06336 - PDF; Fulltext from publisher: PDF; External link: Fermilab Accepted Manuscript

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