Photon detection probability prediction using one-dimensional generative neural network

Journal Article · 18 March 2022 · Machine Learning: Science and Technology

DOI: https://doi.org/10.1088/2632-2153/ac58e2 · OSTI ID:1855622

Mu, Wei ; Himmel, Alexander I. ; Ramson, Bryan

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 the Geant4 simulation 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 $\mathrm{O}\mathrm{u}\mathrm{t}\mathrm{e}\mathrm{r}\mathrm{P}\mathrm{r}\mathrm{o}\mathrm{d}\mathrm{u}\mathrm{c}\mathrm{t}$ -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 the Geant4 simulation. The application to simulating photon detection systems in kilo-tonne-scale liquid argon detectors demonstrates this novel generative model is able to reproduce Geant4 simulation 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.

• Sponsoring Organization: USDOE
• OSTI ID: 1855622
• Journal Information: Machine Learning: Science and Technology, Journal Name: Machine Learning: Science and Technology Journal Issue: 1 Vol. 3; ISSN 2632-2153
• Publisher: IOP PublishingCopyright Statement
• Country of Publication: United Kingdom
• Language: English