Improving sensitivity of the ARIANNA detector by rejecting thermal noise with deep learning
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 > 10^{16} $), the physics output is limited by statistics. Hence, an increase in sensitivity significantly i...
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ftdatacite:10.48550/arxiv.2112.01031 2023-05-15T14:02:29+02:00 Improving sensitivity of the ARIANNA detector by rejecting thermal noise with deep learning ARIANNA Collaboration Anker, A. Baldi, P. Barwick, S. W. Beise, J. Besson, D. Z. Bouma, S. Cataldo, M. Chen, P. Gaswint, G. Glaser, C. Hallgren, A. Hallmann, S. Hanson, J. C. Klein, S. R. Kleinfelder, S. A. Lahmann, R. Liu, J. Magnuson, M. McAleer, S. Meyers, Z. M. Nam, J. Nelles, A. Novikov, A. Paul, M. P. Persichilli, C. Plaisier, I. Pyras, L. Rice-Smith, R. Tatar, J. Wang, S. -H Welling, C. Zhao, L. 2021 https://dx.doi.org/10.48550/arxiv.2112.01031 https://arxiv.org/abs/2112.01031 unknown arXiv https://dx.doi.org/10.1088/1748-0221/17/03/p03007 arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ Instrumentation and Methods for Astrophysics astro-ph.IM FOS Physical sciences article-journal Article ScholarlyArticle Text 2021 ftdatacite https://doi.org/10.48550/arxiv.2112.01031 https://doi.org/10.1088/1748-0221/17/03/p03007 2022-04-01T12:55:33Z The ARIANNA experiment is an Askaryan detector designed to record radio signals induced by neutrino interactions in the Antarctic ice. Because of the low neutrino flux at high energies ($E > 10^{16} $), 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 fluctuations. We present a real-time thermal noise rejection algorithm that enables the trigger thresholds to be lowered, which increases the sensitivity to neutrinos by up to a factor of two (depending on energy) compared to the current ARIANNA capabilities. A deep learning discriminator, based on a Convolutional Neural Network (CNN), is implemented to identify and remove thermal events in real time. We describe a CNN trained on MC data that runs on the current ARIANNA microcomputer and retains 95 percent of the neutrino signal at a thermal noise rejection factor of $10^5$, compared to a template matching procedure which reaches only $10^2$ for the same signal efficiency. Then the results are verified in a lab measurement by feeding in generated neutrino-like signal pulses and thermal noise directly into the ARIANNA data acquisition system. Lastly, the same CNN is used to classify cosmic-rays events to make sure they are not rejected. The network classified 102 out of 104 cosmic-ray events as signal. : 23 pages, 11 figures, 1 table Text Antarc* Antarctic DataCite Metadata Store (German National Library of Science and Technology) Antarctic The Antarctic |
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DataCite Metadata Store (German National Library of Science and Technology) |
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Instrumentation and Methods for Astrophysics astro-ph.IM FOS Physical sciences |
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Instrumentation and Methods for Astrophysics astro-ph.IM FOS Physical sciences ARIANNA Collaboration Anker, A. Baldi, P. Barwick, S. W. Beise, J. Besson, D. Z. Bouma, S. Cataldo, M. Chen, P. Gaswint, G. Glaser, C. Hallgren, A. Hallmann, S. Hanson, J. C. Klein, S. R. Kleinfelder, S. A. Lahmann, R. Liu, J. Magnuson, M. McAleer, S. Meyers, Z. M. Nam, J. Nelles, A. Novikov, A. Paul, M. P. Persichilli, C. Plaisier, I. Pyras, L. Rice-Smith, R. Tatar, J. Wang, S. -H Welling, C. Zhao, L. Improving sensitivity of the ARIANNA detector by rejecting thermal noise with deep learning |
topic_facet |
Instrumentation and Methods for Astrophysics astro-ph.IM FOS Physical sciences |
description |
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 > 10^{16} $), 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 fluctuations. We present a real-time thermal noise rejection algorithm that enables the trigger thresholds to be lowered, which increases the sensitivity to neutrinos by up to a factor of two (depending on energy) compared to the current ARIANNA capabilities. A deep learning discriminator, based on a Convolutional Neural Network (CNN), is implemented to identify and remove thermal events in real time. We describe a CNN trained on MC data that runs on the current ARIANNA microcomputer and retains 95 percent of the neutrino signal at a thermal noise rejection factor of $10^5$, compared to a template matching procedure which reaches only $10^2$ for the same signal efficiency. Then the results are verified in a lab measurement by feeding in generated neutrino-like signal pulses and thermal noise directly into the ARIANNA data acquisition system. Lastly, the same CNN is used to classify cosmic-rays events to make sure they are not rejected. The network classified 102 out of 104 cosmic-ray events as signal. : 23 pages, 11 figures, 1 table |
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Text |
author |
ARIANNA Collaboration Anker, A. Baldi, P. Barwick, S. W. Beise, J. Besson, D. Z. Bouma, S. Cataldo, M. Chen, P. Gaswint, G. Glaser, C. Hallgren, A. Hallmann, S. Hanson, J. C. Klein, S. R. Kleinfelder, S. A. Lahmann, R. Liu, J. Magnuson, M. McAleer, S. Meyers, Z. M. Nam, J. Nelles, A. Novikov, A. Paul, M. P. Persichilli, C. Plaisier, I. Pyras, L. Rice-Smith, R. Tatar, J. Wang, S. -H Welling, C. Zhao, L. |
author_facet |
ARIANNA Collaboration Anker, A. Baldi, P. Barwick, S. W. Beise, J. Besson, D. Z. Bouma, S. Cataldo, M. Chen, P. Gaswint, G. Glaser, C. Hallgren, A. Hallmann, S. Hanson, J. C. Klein, S. R. Kleinfelder, S. A. Lahmann, R. Liu, J. Magnuson, M. McAleer, S. Meyers, Z. M. Nam, J. Nelles, A. Novikov, A. Paul, M. P. Persichilli, C. Plaisier, I. Pyras, L. Rice-Smith, R. Tatar, J. Wang, S. -H Welling, C. Zhao, L. |
author_sort |
ARIANNA Collaboration |
title |
Improving sensitivity of the ARIANNA detector by rejecting thermal noise with deep learning |
title_short |
Improving sensitivity of the ARIANNA detector by rejecting thermal noise with deep learning |
title_full |
Improving sensitivity of the ARIANNA detector by rejecting thermal noise with deep learning |
title_fullStr |
Improving sensitivity of the ARIANNA detector by rejecting thermal noise with deep learning |
title_full_unstemmed |
Improving sensitivity of the ARIANNA detector by rejecting thermal noise with deep learning |
title_sort |
improving sensitivity of the arianna detector by rejecting thermal noise with deep learning |
publisher |
arXiv |
publishDate |
2021 |
url |
https://dx.doi.org/10.48550/arxiv.2112.01031 https://arxiv.org/abs/2112.01031 |
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Antarctic The Antarctic |
geographic_facet |
Antarctic The Antarctic |
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Antarc* Antarctic |
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Antarc* Antarctic |
op_relation |
https://dx.doi.org/10.1088/1748-0221/17/03/p03007 |
op_rights |
arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
op_doi |
https://doi.org/10.48550/arxiv.2112.01031 https://doi.org/10.1088/1748-0221/17/03/p03007 |
_version_ |
1766272751145844736 |