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Torsten ÅKESSON

Torsten Åkesson

Professor

Torsten ÅKESSON

Alignment of the ATLAS Inner Detector in Run 2

Author

  • G Aad
  • Torsten Åkesson
  • Simona Bocchetta
  • Eric Edward Corrigan
  • Caterina Doglioni
  • Jannik Geisen
  • Eva Brottmann Hansen
  • Vincent Hedberg
  • Göran Jarlskog
  • Edgar Kellermann
  • Balazs Konya
  • Else Lytken
  • Katja Mankinen
  • Caterina Marcon
  • Ulf Mjörnmark
  • Geoffrey André Adrien Mullier
  • Ruth Pöttgen
  • Trine Poulsen
  • Eleni Skorda
  • Oxana Smirnova
  • L Zwalinski

Summary, in English

The performance of the ATLAS Inner Detector alignment has been studied using pp collision data at s=13TeV collected by the ATLAS experiment during Run 2 (2015–2018) of the Large Hadron Collider (LHC). The goal of the detector alignment is to determine the detector geometry as accurately as possible and correct for time-dependent movements. The Inner Detector alignment is based on the minimization of track-hit residuals in a sequence of hierarchical levels, from global mechanical assembly structures to local sensors. Subsequent levels have increasing numbers of degrees of freedom; in total there are almost 750,000. The alignment determines detector geometry on both short and long timescales, where short timescales describe movements within an LHC fill. The performance and possible track parameter biases originating from systematic detector deformations are evaluated. Momentum biases are studied using resonances decaying to muons or to electrons. The residual sagitta bias and momentum scale bias after alignment are reduced to less than ∼0.1TeV-1 and 0.9 × 10 - 3, respectively. Impact parameter biases are also evaluated using tracks within jets. © 2020, The Author(s).

Department/s

  • Particle and nuclear physics
  • eSSENCE: The e-Science Collaboration

Publishing year

2020

Language

English

Publication/Series

European Physical Journal C

Volume

80

Issue

12

Document type

Journal article

Publisher

Springer

Topic

  • Subatomic Physics

Status

Published

ISBN/ISSN/Other

  • ISSN: 1434-6044