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A first application of machine and deep learning for background rejection in the ALPS II TES detector
- Source :
- Annalen der Physik 2023, 2200545
- Publication Year :
- 2023
-
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.<br />Comment: 11 pages, 5 figures, accepted for publication in Annals of Physics. Contribution to the Patras 2022 Workshop on Axions, WIMPs, and WISPs
- Subjects :
- High Energy Physics - Experiment
Physics - Instrumentation and Detectors
Subjects
Details
- Database :
- arXiv
- Journal :
- Annalen der Physik 2023, 2200545
- Publication Type :
- Report
- Accession number :
- edsarx.2304.08406
- Document Type :
- Working Paper
- Full Text :
- https://doi.org/10.1002/andp.202200545