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Dugad, S.

Publications and source records attributed to Dugad, S..

Timing Performance of the CMS High Granularity Calorimeter Prototype

This paper describes the experience with the calibration,reconstruction and evaluation of the timing capabilities of the CMSHGCAL prototype in the beam tests in 2018. The calibrationprocedure includes multiple steps and corrections ranging from tensof nanoseconds to a few hundred picoseconds. The timing performanceis studied using signals from positron beam particles with energiesbetween 20 GeV and 300 GeV. The performance is studied as afunction of particle energy against an external timing reference aswell as standalone by comparing the two different halves of theprototype. The timing resolution is found to be 60 ps forsingle-channel measurements and better than 20 ps for full showersat the highest energies, setting excellent perspectives for theHGCAL calorimeter performance at the HL-LHC.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Neutron irradiation and electrical characterisation of the first 8” silicon pad sensor prototypes for the CMS calorimeter endcap upgrade

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. Due to its compactness, intrinsic time resolution, and radiation hardness, silicon has been chosen as active material for the regions exposed to higher radiation levels. The silicon sensors are fabricated as 20 cm (8”) wide hexagonal wafers and are segmented into several hundred pads which are read out individually. As part of the sensor qualification strategy, 8” sensor irradiation with neutrons has been conducted at the Rhode Island Nuclear Science Center (RINSC) and followed by their electrical characterisation in 2020-21. The completion of this important milestone in the CE's R&D program is documented in this paper and it provides detailed account of the associated infrastructure and procedures.The results on the electrical properties of the irradiated CE silicon sensors are presented.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learning

The rapidly-developing intersection of machine learning (ML) with high-energy physics (HEP) presents both opportunities and challenges to our community. Far beyond applications of standard ML tools to HEP problems, genuinely new and potentially revolutionary approaches are being developed by a generation of talent literate in both fields. There is an urgent need to support the needs of the interdisciplinary community driving these developments, including funding dedicated research at the intersection of the two fields, investing in high-performance computing at universities and tailoring allocation policies to support this work, developing of community tools and standards, and providing education and career paths for young researchers attracted by the intellectual vitality of machine learning for high energy physics.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗