# Photon detection probability prediction using one-dimensional generative neural network

arXiv: https://arxiv.org/abs/2109.07277 · HTML: https://mlsanity.com/paper/2109.07277/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 26 references that are in the map.
- First posted on arXiv 11 Sep 2021.

## Builds on (most cited first, 5 of 5)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [How (not) to Train your Generative Model: Scheduled Sampling, Likelihood, Adversary?](https://mlsanity.com/paper/1511.05101/) (2015)
- [Image-based model parameter optimization using Model-Assisted Generative Adversarial Networks](https://mlsanity.com/paper/1812.00879/) (2018)
- [Liquified protein vibrations, classification and cross-paradigm de novo image generation using deep neural networks](https://mlsanity.com/paper/2004.07603/) (2020)

## Built on by (most cited first, 2 of 2)

- [Deep Generative Models for Detector Signature Simulation: A Taxonomic Review](https://mlsanity.com/paper/2312.09597/) (2023)
- [New directions for surrogate models and differentiable programming for High Energy Physics detector simulation](https://mlsanity.com/paper/2203.08806/) (2022)

## Abstract (arXiv)

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.