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A first application of machine and deep learning for background rejection in the ALPS II TES detector
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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.
Forward citations
Cited by 2 Pith papers
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Simulation and measurement of Black Body Radiation background in a Transition Edge Sensor
A simulation framework for black-body radiation propagation to a transition-edge sensor reproduces the measured background shape of the ALPS II detector and quantifies how improved energy resolution reduces the 1064 n...
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First direct search for light dark matter interactions in a transition-edge sensor
A 489-hour run of a tungsten transition-edge sensor, used as both target and readout, sets first-generation limits on sub-MeV dark matter scattering with electrons and nucleons and on dark photon absorption.
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