%0 Conference Paper
%A Meyer, Manuel
%A Isleif, Katharina-Sophie
%A Januschek, Friederike
%A Lindner, Axel
%A Othman, Gulden
%A Rubiera Gimeno, José Alejandro
%A Schwemmbauer, Christina
%A Schott, Matthias
%A Shah, Rikhav
%T A first application of machine and deep learning for background rejection in the ALPS II TES detector
%J Annalen der Physik
%V 536
%N 1
%@ 0003-3804
%C Berlin
%I Wiley-VCH
%M PUBDB-2022-06682
%P 2200545
%D 2023
%Z Published as open access
%X 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, 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.
%B 17th Patras Workshop on Axions, WIMPs and WISPs
%C 8 Aug 2022 - 12 Aug 2022, Mainz (Germany)
Y2 8 Aug 2022 - 12 Aug 2022
M2 Mainz, Germany
%F PUB:(DE-HGF)16 ; PUB:(DE-HGF)8
%9 Journal ArticleContribution to a conference proceedings
%U <Go to ISI:>//WOS:000981825400001
%R 10.1002/andp.202200545
%U https://bib-pubdb1.desy.de/record/485527