A first application of machine and deep learning for background rejection in the ALPS II TES detector
Publication date
2023-05-05
Document type
Forschungsartikel
Author
Meyer, Manuel
Januschek, Friederike
Lindner, Axel
Schwemmbauer, Christina
Schott, Matthias
Shah, Rikhav
Organisational unit
Publisher
Wiley-VCH
Series or journal
Annalen der Physik
ISSN
Periodical volume
536
Periodical issue
1
Article ID
2200545
Peer-reviewed
✅
Part of the university bibliography
✅
Language
English
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 ≈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.
Description
This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/).
Version
Published version
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