Machine Learning: Science and Technology (Jan 2024)

Differentiable simulation of a liquid argon time projection chamber

  • Sean Gasiorowski,
  • Yifan Chen,
  • Youssef Nashed,
  • Pierre Granger,
  • Camelia Mironov,
  • Ka Vang Tsang,
  • Daniel Ratner,
  • Kazuhiro Terao

DOI
https://doi.org/10.1088/2632-2153/ad2cf0
Journal volume & issue
Vol. 5, no. 2
p. 025012

Abstract

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Liquid argon time projection chambers (LArTPCs) are widely used in particle detection for their tracking and calorimetric capabilities. The particle physics community actively builds and improves high-quality simulators for such detectors in order to develop physics analyses in a realistic setting. The ability of these simulators to mimic real, measured data is limited by the modeling of the physical detectors used for data collection. This modeling can be improved by performing dedicated calibration measurements. Conventional approaches calibrate individual detector parameters or processes one at a time. However, the impact of detector processes is entangled, making this a poor description of the underlying physics. We introduce a differentiable simulator that enables a gradient-based optimization, allowing for the first time a simultaneous calibration of all detector parameters. We describe the procedure of making a differentiable simulator, highlighting the challenges of retaining the physics quality of the standard, non-differentiable version while providing meaningful gradient information. We further discuss the advantages and drawbacks of using our differentiable simulator for calibration. Finally, we provide a starting point for extensions to our approach, including applications of the differentiable simulator to physics analysis pipelines.

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