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DTSTAMP:20230831T095745Z
LOCATION:Davos
DTSTART;TZID=Europe/Stockholm:20230626T112000
DTEND;TZID=Europe/Stockholm:20230626T115000
UID:submissions.pasc-conference.org_PASC23_sess104_pos141@linklings.com
SUMMARY:P16 - Denoising Electronic Signals from Particle Detectors Using a
  Flexible Deep Convolutional Autoencoder
DESCRIPTION:Poster\n\nMark Anderson, Noah Rowe, and Tianai Ye (Queen's Uni
 versity)\n\nIn this work, we present the use of a deep convolutional autoe
 ncoder to denoise signals from particle detectors. The study of rare parti
 cle interactions is crucial in advancing our understanding of the Universe
 . However, the presence of electronic noise makes signal events difficult 
 to distinguish from backgrounds, especially due to the infrequent nature o
 f the interactions we are searching for. We begin with our recently publis
 hed results on germanium detectors demonstrating that deep learning is mor
 e effective at removing noise than traditional approaches, while still pre
 serving the underlying pulse shape well. We show that our approach also ha
 s practical implications on data storage and processing efficiency. To ext
 end on our published work, we explored additional deep learning-based meth
 ods for signal denoising and modeling. We then present our simulations on 
 implementing these algorithms at the hardware level directly for real-time
  denoising and data selection. Finally, we demonstrate that our approach i
 s broadly applicable to other detector technologies and one-dimensional el
 ectronic signals.\n\nSession Chair: Elaine M. Raybourn (Sandia National La
 boratories)
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