Organized by: Prof. Lu Lu

Events During the Week of February 26th through March 5th, 2023

Monday, February 27th, 2023

No events scheduled

Tuesday, February 28th, 2023

No events scheduled

Wednesday, March 1st, 2023

No events scheduled

Thursday, March 2nd, 2023

WIPAC-astro discussion

Where are Milky Way’s Hadronic PeVatrons?

Time: 2:30 pm - 3:30 pm

Place:

Speaker: Takahiro Sudo, Ohio State University

Abstract: Observations of the Milky Way at TeV–PeV energies reveal a bright diffuse flux of hadronic cosmic rays and also bright point sources of gamma rays. If the gamma-ray sources are hadronic cosmic-ray accelerators, then they must also be neutrino sources. However, no neutrino sources have been detected. Where are they? We introduce a new population-based approach to probe Milky Way hadronic PeVatrons, demanding consistency between diffuse and point-source PeV-range data on cosmic rays, gamma rays, and neutrinos. For the PeVatrons, two extreme scenarios are allowed: (1) the hadronic cosmic-ray accelerators and the gamma-ray sources are the same objects, so that bright neutrino sources exist and improved telescopes can detect them, versus (2) the hadronic cosmic-ray accelerators and the gamma-ray sources are distinct, so that there are no detectable neutrino sources. The latter case is possible if hadronic accelerators have sufficiently thin column densities. We quantify present constraints and future prospects, showing how to reveal the nature of the hadronic PeVatrons

Host: Lu Lu

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Friday, March 3rd, 2023

Machine Learning and its Applications in IceCube

Time: 2:00 pm - 3:00 pm

Place: CH4274/Join Zoom Meeting

Speaker: Claudio Kopper, Michigan State University/Friedrich-Alexander-Universität Erlangen-Nürnberg

Abstract: In this talk, I will be discussing the fascinating world of machine learning (ML) and its applications to the IceCube neutrino telescope. 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. I will present recent IceCube results that were made possible by machine learning techniques and highlight the challenges we face when applying ML to IceCube data. The key challenges to be solved in IceCube are background suppression, particle identification, and event reconstruction, all of which can benefit from the implementation of ML techniques. I will be showcasing the ways in which ML can help with these challenges, and how it has been widely adopted within IceCube, not only to tackle these issues but also in the development of analysis methodology. Overall, the talk will provide an overview of ML techniques, how they are applied in IceCube, and the exciting recent results based on ML.

Host: Albrecht Karle

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