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

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, the machine and deep learning algorithms for the rejection of background events recorded with the TES are investigated. A first application of convolutional neural networks to classify time series data measured with the TES is also presented. Hypothetical axions and axion‐like particles are promising cold dark matter candidates that will be searched for in the Any Light Particle Search (ALPS II) experiment. In this article, machine and deep learning algorithms are investigated to improve the rejection of background events recorded with one foreseen detector of ALPS II.