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The Machine Learning landscape of top taggers

https://doi.org/10.21468/SciPostPhys.7.1.014

Kasieczka, Gregor ; Plehn, Tilman ; Butter, Anja ; Cranmer, Kyle ; Debnath, Dipsikha ; Dillon, Barry M. ; Fairbairn, Malcolm ; Faroughy, Darius A. ; Fedorko, Wojtek ; Gay, Christophe ; et al ( January 2019 , SciPost Physics)

Based on the established task of identifying boosted, hadronicallydecaying top quarks, we compare a wide range of modern machine learningapproaches. Unlike most established methods they rely on low-levelinput, for instance calorimeter output. While their networkarchitectures are vastly different, their performance is comparativelysimilar. In general, we find that these new approaches are extremelypowerful and great fun.
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Operation and performance of the ATLAS semiconductor tracker in LHC Run 2

https://doi.org/10.1088/1748-0221/17/01/P01013

Aad, Georges ; Abbott, Brad ; Abbott, Dale Charles ; Abed Abud, Adam ; Abeling, Kira ; Abhayasinghe, Deshan Kavishka ; Abidi, Syed Haider ; Aboulhorma, Asmaa ; Abramowicz, Halina ; Abreu, Henso ; et al ( January 2022 , Journal of Instrumentation)

Abstract The semiconductor tracker (SCT) is one of the tracking systems for charged particles in the ATLAS detector. It consists of 4088 silicon strip sensor modules.During Run 2 (2015–2018) the Large Hadron Collider delivered an integrated luminosity of 156 fb -1 to the ATLAS experiment at a centre-of-mass proton-proton collision energy of 13 TeV. The instantaneous luminosity and pile-up conditions were far in excess of those assumed in the original design of the SCT detector.Due to improvements to the data acquisition system, the SCT operated stably throughout Run 2.It was available for 99.9% of the integrated luminosity and achieved a data-quality efficiency of 99.85%.Detailed studies have been made of the leakage current in SCT modules and the evolution of the full depletion voltage, which are used to study the impact of radiation damage to the modules.
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