A first application of machine and deep learning for background rejection in the ALPS II TES detector
| Autor: | Meyer, Manuel, Isleif, Katharina, Januschek, Friederike, Lindner, Axel, Othman, Gulden, Gimeno, Jose Alejandro Rubiera, Schwemmbauer, Christina, Schott, Matthias, Shah, Rikhav |
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| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | Annalen der Physik 2023, 2200545 |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1002/andp.202200545 |
| Popis: | 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. Comment: 11 pages, 5 figures, accepted for publication in Annals of Physics. Contribution to the Patras 2022 Workshop on Axions, WIMPs, and WISPs |
| Databáze: | arXiv |
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