Machine learning techniques deep underwater in km3net

Circella M, Eberl T (2022)

Publication Language: English

Publication Type: Journal article, Original article

Subtype: other

Publication year: 2022

Journal

Europhysics News Springer Verlag (Germany) / EDP Sciences

Book Volume: 53

Pages Range: 26-29-29

Journal Issue: 2

DOI: 10.1051/epn/2022206

Abstract

Deep in the water of the Mediterranean Sea, the KM3NeT detectors aim at the exploration of the cosmos through the detection of neutrinos and to determine the neutrino mass ordering. Machine learning techniques are widely used to push the performance of the detectors to the limit.

Authors with CRIS profile

Thomas Eberl Lehrstuhl für Experimentelle Astroteilchenphysik

Involved external institutions

National Institute for Nuclear Physics / Istituto Nazionale di Fisica Nucleare (INFN) IT Italy (IT)

How to cite

APA:

Circella, M., & Eberl, T. (2022). Machine learning techniques deep underwater in km3net. Europhysics News, 53(2), 26-29-29. https://dx.doi.org/10.1051/epn/2022206

MLA:

Circella, Marco, and Thomas Eberl. "Machine learning techniques deep underwater in km3net." Europhysics News 53.2 (2022): 26-29-29.

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