Photon detection probability prediction using one-dimensional generative neural network

Abstract

Photon detection is important for liquid argon detectors for direct dark matter searches or neutrino property measurements. Precise simulation of photon transport is widely used to understand the probability of photon detection in liquid argon detectors. Traditional photon transport simulation, which tracks every photon using theGeant4simulation toolkit, is a major computational challenge for kilo-tonne-scale liquid argon detectors and GeV-level energy depositions. In this work, we propose a one-dimensional generative model which efficiently generates features using an OuterProduct-layer. This model bypasses photon transport simulation and predicts the number of photons detected by particular photon detectors at the same level of detail as theGeant4simulation. The application to simulating photon detection systems in kilo-tonne-scale liquid argon detectors demonstrates this novel generative model is able to reproduceGeant4simulation with good accuracy and 20 to 50 times faster. This generative model can be used to quickly predict photon detection probability in huge liquid argon detectors like ProtoDUNE or DUNE.

References in corpus (5)

• Conditional Generative Adversarial Nets
• Generative Adversarial Networks
• First results from a Dark Matter search with liquid Argon at 87 K in the Gran Sasso Underground Laboratory
• First results on ProtoDUNE-SP liquid argon time projection chamber performance from a beam test at the CERN Neutrino Platform
• The ArDM Liquid Argon Time Projection Chamber at the Canfranc Underground Laboratory: a ton-scale detector for Dark Matter Searches

Cited by in corpus (2)

• Deep Generative Models for Detector Signature Simulation: A Taxonomic Review
• Performance of an Optical TPC Geant4 Simulation with Opticks GPU-Accelerated Photon Propagation