A First Application of Machine and Deep Learning for Background Rejection in the ALPS II TES Detector

Axions and axion-like particles are hypothetical predicted in extensions of the standard model promising cold dark matter candidates. The Any Light Particle Search (ALPS II) experiment is a light-shining-through-the-wall that aims to produce these from strong light source magnetic field subsequently detect them through reconversion into photons. With an expected rate ≈1 photon per day, sensitive detection scheme needs be employed characterized. One foreseen detector based on transition edge sensor (TES). Here, machine deep learning algorithms for rejection background events recorded with TES investigated. A first application convolutional neural networks classify time series data measured also presented.