Neutrino Physics and Machine Learning (NPML)

Session

Day 5 Afternoon

24 Jul 2020, 13:00

Conveners

Day 5 Afternoon

• Patrick de Perio (TRIUMF)
• Nick Prouse (TRIUMF)

15. Machine Learning Research and Applications in IceCube

Mirco Huennefeld (TU Dortmund)

24/07/2020, 13:00

A collaboration/project summary talk

The field of machine learning has become increasingly important over the last years and now constitutes a vital contribution to the physics output of experiments such as IceCube. IceCube is a neutrino telescope situated at the geographic South Pole, instrumenting a cubic kilometer of glacial ice. Atmospheric and astrophysical neutrinos are indirectly measured via Cherenkov radiation of charged...

12. Deep Learning Classifier for Low-Energy Events in IceCube

Maria Prado Rodriguez (University of Wisconsin-Madison)

24/07/2020, 13:40

Individual talk

Tau appearance from neutrino oscillations of atmospheric muon neutrinos is studied by the DeepCore subarray, the densely-instrumented region of IceCube, an ice-Cherenkov neutrino detector 1.5 kilometers below the surface of the South Pole. These studies probe the unitarity of the PMNS matrix. Distinguishable event signatures in this region include track-like and shower-like events. Because the...

13. Optimizing a CNN to Reconstruct Low Energy IceCube Neutrino Events

Jessie Micallef (Michigan State University)

24/07/2020, 14:05

Individual talk

The IceCube Neutrino Observatory, located at the South Pole, instruments a cubic kilometer of ice with 5160 optical modules that are used to detect astrophysical and atmospheric neutrinos. Near the lowest energies that IceCube can resolve, at the 10s of GeV-scale, these events leave a Cherenkov signature that only a few optical modules will record. Thus, these events are difficult to...

43. White paper discussion

Adam Aurisano (University of Cincinnati)

24/07/2020, 14:30

44. Closing

Kazuhiro Terao (SLAC)

24/07/2020, 14:50