Improving sensitivity of the ARIANNA detector by rejecting thermal noise with deep learning

@article{Anker2021ImprovingSO,
  title={Improving sensitivity of the ARIANNA detector by rejecting thermal noise with deep learning},
  author={A. Anker and P. Baldi and Steven W. Barwick and Jakob Beise and Dave Z. Besson and Sjoerd Bouma and Maddalena Cataldo and P. H. Chen and Geoffrey Gaswint and Christian Glaser and Allan Hallgren and Steffen Hallmann and Jordan C. Hanson and Spencer R. Klein and Stuart Kleinfelder and Robert Lahmann and J X Liu and Michael Magnuson and Stephen McAleer and Zachary S. Meyers and Jiwoo Nam and Anna Nelles and A. Novikov and Manuel P. Paul and Christopher Persichilli and Ilse Plaisier and Lilly Pyras and Ryan Rice-Smith and Joulien Tatar and S.-H. Wang and Christoph Welling and L. Zhao},
  journal={Journal of Instrumentation},
  year={2021},
  volume={17}
}
  • A. AnkerP. Baldi L. Zhao
  • Published 2 December 2021
  • Physics, Computer Science
  • Journal of Instrumentation
The ARIANNA experiment is an Askaryan detector designed to record radio signals induced by neutrino interactions in the Antarctic ice. Because of the low neutrino flux at high energies (E_ν> 10^16 eV), the physics output is limited by statistics. Hence, an increase in sensitivity significantly improves the interpretation of data and offers the ability to probe new parameter spaces. The amplitudes of the trigger threshold are limited by the rate of triggering on unavoidable thermal noise… 

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