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arXiv · 2304.08406

A first application of machine and deep learning for background rejection in the ALPS II TES detector

Manuel Meyer·Katharina Isleif·Friederike Januschek·Axel Lindner·Gulden Othman·Jose Alejandro Rubiera Gimeno·Christina Schwemmbauer·Matthias Schott·Rikhav Shah
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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 $\sim$ 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.

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Manuel Meyer, Katharina Isleif, Friederike Januschek, Axel Lindner, Gulden Othman, Jose Alejandro Rubiera Gimeno, Christina Schwemmbauer, Matthias Schott, Rikhav Shah. 2023-04-17. A first application of machine and deep learning for background rejection in the ALPS II TES detector. https://doi.org/10.1002/andp.202200545

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