2023
DOI: 10.1002/andp.202200545
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A First Application of Machine and Deep Learning for Background Rejection in the ALPS II TES Detector

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 fo… Show more

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Cited by 2 publications
(3 citation statements)
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“…Direct dark matter searches using ALPS II's TES detection system Christina Schwemmbauer Notably, our setup has already achieved very low intrinsic dark count rates (in an analysis optimized for 1.165 eV) as low as (2.16 ± 2.02) • 10 −6 Hz [16]. These dark counts include mainly electronic noise but also radioactivity or cosmic backgrounds.…”
Section: Pos(eps-hep2023)120mentioning
confidence: 96%
See 1 more Smart Citation
“…Direct dark matter searches using ALPS II's TES detection system Christina Schwemmbauer Notably, our setup has already achieved very low intrinsic dark count rates (in an analysis optimized for 1.165 eV) as low as (2.16 ± 2.02) • 10 −6 Hz [16]. These dark counts include mainly electronic noise but also radioactivity or cosmic backgrounds.…”
Section: Pos(eps-hep2023)120mentioning
confidence: 96%
“…An optical fiber will connect the ALPS II experiment directly to the TES detector inside a cryostat. In the interest of this measurement, the so-called intrinsic background was studied extensively, which are the dark counts of the sensor without the attached optical fiber [9,15,16]. Based on these studies, one can predict the performance of our TES detector for DM-scattering experiments (also with disconnected fiber) using the dielectric function of the target [17].…”
Section: Pos(eps-hep2023)120mentioning
confidence: 99%
“…Cuts imposed on the fitting parameters in an intrinsic sample allowed the rejection of almost all background events, reaching a rate of 6.9 +2.6 -1.5 • 10 -6 cps [10] while keeping more than 90 % efficiency for 1064 nm photons, reflecting that the intrinsics sample contains pulses with an amplitude and shape different than the one for photons. These results have been further improved using machine learning techniques [15].…”
Section: Pulse Shape Analysis In Frequency Domainmentioning
confidence: 99%