A first application of machine and deep learning for background rejection in the ALPS II TES detector
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...
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| creator | Meyer, Manuel Isleif, Katharina Januschek, Friederike Lindner, Axel Othman, Gulden Jose Alejandro Rubiera Gimeno Schwemmbauer, Christina Schott, Matthias Shah, Rikhav the ALPS II collaboration |
| description | 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. |
| doi_str_mv | 10.48550/arxiv.2304.08406 |
| format | Article |
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| subjects | Algorithms Artificial neural networks Cold dark matter Deep learning Hypothetical particles Light sources Machine learning Photons Physics - High Energy Physics - Experiment Physics - Instrumentation and Detectors Rejection |
| title | A first application of machine and deep learning for background rejection in the ALPS II TES detector |
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