A first application of machine and deep learning for background rejection in the ALPS II TES detector
Abstract
Axions and axion-like particles are hypothetical particles predicted in extensions of the standard model and are promising cold dark matter candidates. The Any Light Particle Search (ALPS II) experiment is a light-shining-through-the-wall experiment that aims to produce these particles from a strong light source and magnetic field and subsequently detect them through a reconversion into photons. With an expected rate 1 photon per day, a sensitive detection scheme needs to be employed and characterized. One foreseen detector is based on a transition edge sensor (TES). Here, we investigate machine and deep learning algorithms for the rejection of background events recorded with the TES. We also present a first application of convolutional neural networks to classify time series data measured with the TES.
Cite
@article{arxiv.2304.08406,
title = {A first application of machine and deep learning for background rejection in the ALPS II TES detector},
author = {Manuel Meyer and Katharina Isleif and Friederike Januschek and Axel Lindner and Gulden Othman and Jose Alejandro Rubiera Gimeno and Christina Schwemmbauer and Matthias Schott and Rikhav Shah},
journal= {arXiv preprint arXiv:2304.08406},
year = {2023}
}
Comments
11 pages, 5 figures, accepted for publication in Annals of Physics. Contribution to the Patras 2022 Workshop on Axions, WIMPs, and WISPs