Photon detection probability prediction using one-dimensional generative neural network

TL;DR

This paper introduces a one-dimensional generative neural network that accurately and significantly faster predicts photon detection probabilities in large liquid argon detectors, bypassing computationally intensive traditional simulations.

Contribution

The paper presents a novel generative model that efficiently predicts photon detection in large detectors, reducing simulation time by 20 to 50 times compared to Geant4.

Findings

Reproduces Geant4 simulation results with good accuracy

Achieves 20 to 50 times faster prediction

Applicable to large-scale liquid argon detectors like DUNE

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.