TY  - CONF
AU  - Meyer, Manuel
AU  - Isleif, Katharina-Sophie
AU  - Januschek, Friederike
AU  - Lindner, Axel
AU  - Othman, Gulden
AU  - Rubiera Gimeno, José Alejandro
AU  - Schwemmbauer, Christina
AU  - Schott, Matthias
AU  - Shah, Rikhav
TI  - A first application of machine and deep learning for background rejection in the ALPS II TES detector
JO  - Annalen der Physik
VL  - 536
IS  - 1
SN  - 0003-3804
CY  - Berlin
PB  - Wiley-VCH
M1  - PUBDB-2022-06682
SP  - 2200545
PY  - 2023
N1  - Published as open access
AB  - 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.
T2  - 17th Patras Workshop on Axions, WIMPs and WISPs
CY  - 8 Aug 2022 - 12 Aug 2022, Mainz (Germany)
Y2  - 8 Aug 2022 - 12 Aug 2022
M2  - Mainz, Germany
LB  - PUB:(DE-HGF)16 ; PUB:(DE-HGF)8
UR  - <Go to ISI:>//WOS:000981825400001
DO  - DOI:10.1002/andp.202200545
UR  - https://bib-pubdb1.desy.de/record/485527
ER  -