14-17 September 2021
America/Los_Angeles timezone

Photon detection probability predictionusing one-dimensional generative neural network

16 Sep 2021, 13:30
Signal reconstruction and identification (analysis methods, simulations) Signal Reconstruction (3C)


Wei Mu (Fermilab)


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 within the framework ofGeant4brings extreme challenge to computing resources with kilo-tonne-scale liquid argon detectors and GeV-level energy depositions. In this work, we propose a one-dimensional generative model which bypasses photon transport simulation and predicts the number of photons detected by particular photon detectors at the same level of detail asGeant4simulation.The application to photon detection systems in kilo-tonne-scale liquid argon detectors demonstrates this novel generative model is able to reproduceGeant4simulation with good accuracy and 20x-50xfaster. This generative model can be used to fast predict photon detection probability in huge liquid argon detectors like ProtoDUNE or DUNE.

Primary author

Wei Mu (Fermilab)


Alexander Himmel (Fermilab) Dr Bryan Ramson (Fermilab)

Presentation Materials