Name: Workshop on Machine Learning for Cosmic-Ray Air Showers
Start: 2022-01-31T17:00:00-05:00
End: 2022-02-03T17:30:00-05:00
Location: Embassy Suites by Hilton Newark Wilmington South

Workshop on Machine Learning for Cosmic-Ray Air Showers

from Monday 31 January 2022 (17:00) to Thursday 3 February 2022 (17:30)

Monday 31 January 2022

17:00

17:00 - 17:30

17:30

17:30 - 19:00
Tuesday 1 February 2022

09:00 Welcome
Welcome
09:00 - 09:15
09:15
09:15 - 10:30
Contributions

09:15 Deep learning in astroparticle physics - Jonas Glombitza (RWTH AACHEN UNIVERSITY)

10:00 Machine Learning for High-Energy Physics Reconstruction and Analysis - Sergei Gleyzer
10:30 Coffee Break
Coffee Break
10:30 - 11:00
11:00
11:00 - 12:30
Contributions

11:00 Exploitation of Symmetries and Domain Knowledge in Deep Learning Architectures - Mirco Huennefeld (Universität Dortmund)

11:45 Composition Analysis of cosmic-rays at IceCube Observatory, using Graph Neural Networks - Paras Koundal (Karlsruhe Institute of Technology)
12:30 Lunch
Lunch
12:30 - 14:00
14:00 Tuesday
Tuesday
14:00 - 15:30
Contributions

14:00 Measurement of the high-energy muon multiplicity in cosmic-ray air showers with IceTop and IceCube using neural networks - Stef Verpoest (University of Gent)

14:30 Air shower reconstruction using a Graph Neural Network for the IceAct telescopes - Larissa Paul (Marquette University)

15:00 Cosmic ray mass composition study using a Random Forest applied to data from the IceAct telescopes - Larissa Paul (Marquette University)

15:15 Pattern Recognition for Multiple Interactions in a Neutron Monitor - P.-S. Mangeard (University of Delaware)
15:30 Coffee Break
Coffee Break
15:30 - 16:00
16:00 Tuesday
Tuesday
16:00 - 17:30
Contributions

16:00 Composition of 100 TeV - 100 PeV Cosmic Rays with IceCube and IceTop using Boosted Decision Trees - Julian Saffer (Karlsruhe Institute of Technology)

16:30 Cosmic rays primary energy estimation using Machine Learning and combined reconstruction - Diana Leon Silverio (South Dakota School of Mines and Technology)

17:00 Energy Reconstruction with Convolutional Neural Networks in IceTop - Frank McNally (Mercer University)
Wednesday 2 February 2022
09:00
09:00 - 10:30
Contributions

09:00 Machine learning based event reconstruction in Telescope Array surface detector - Oleg Kalashev (INR RAS Moscow)

09:45 State-of-art deep learning technologies and their application to air-shower reconstruction - Vladimir Sotnikov (JetBrains Research)
10:30 Coffee Break
Coffee Break
10:30 - 11:00
11:00
11:00 - 12:30
Contributions

11:00 Deep Learning for Air Shower Reconstruction at the Pierre Auger Observatory - Jonas Glombitza (RWTH AACHEN UNIVERSITY) for the Pierre Auger Collaboration

11:30 Extraction of the Muon Signals Recorded with the Surface Detector of the Pierre Auger Observatory Using Recurrent Neural Networks - Juan Miguel Carceller (University College London)

12:00 Neural Network Approaches for Event Classification Onboard EUSO-SPB2 - George Filippatos (Colorado School of Mines)
12:30 Lunch
Lunch
12:30 - 14:00
14:00
14:00 - 15:30
Contributions

14:00 CORSIKA and CONEX for air shower simulations - Tanguy Pierog (Karlsruhe Institute of Technology (KIT), IAP)

14:45 CORSIKA 8: A modern framework for high-energy cascade simulations - Remy Prechelt (University of Hawai'i)
15:30 Coffee Break
Coffee Break
15:30 - 16:00
16:00 Tutorial
Tutorial
16:00 - 17:30
Contributions

16:00 Machine Learning and Artificial Intelligence in Physics: Overview and Applications - Gregory Dobler (University of Delaware)
Thursday 3 February 2022
09:00
09:00 - 10:30
Contributions

09:00 Machine learning in Baikal-GVD - Ivan Kharuk (Institute for Nuclear Research RAS)

09:35 Towards mass composition study with KASCADE using deep learning - Daniil Reutsky (Moscow Institute of Physics and Technology)

09:55 IACT event reconstruction with deep learning: some progress, lessons learned, and outlook from CTLearn - Daniel Nieto (Instituto de Física de Partículas y del Cosmos and Departamento de EMFTEL, Universidad Complutense de Madrid)
10:30 Coffee Break
Coffee Break
10:30 - 11:00
11:00
11:00 - 12:30
Contributions

11:00 Search for optimal deep neural network architecture for gamma detection at KASCADE - Margarita Tsobenko (Higher School of Economics University - St. Petersburg)

11:20 Photon flux calculation using Deep Learning - Jigar Bhanderi

11:55 Improving the gamma-hadron separation for air showers at the IceCube Neutrino Observatory - Federico Bontempo (Karlsruhe Institute of Technology)
12:30 Lunch
Lunch
12:30 - 14:00
14:00
14:00 - 15:30
Contributions

14:00 Open questions in deep learning techniques for the radio detection - Dmitriy Kostunin (DESY)

14:45 Deep Learning for Classification and Denoising of Cosmic-Ray Radio Signals - Abdul Rehman (University of Delaware)

15:15 Training Neural Networks to Classify and Denoise Cosmic-Ray Radio Signals Using Background Measured at the South Pole - Dana Kullgren (University of Delaware)
15:30 Coffee Break
Coffee Break
15:30 - 16:00
16:00
16:00 - 17:30
Contributions

16:00 Crowdsourcing your training labels with Zooniverse - Lucy Fortson (University of Minnesota)

16:30 What slow down cosmic ray analysis and what can we do about them? - Xinhua Bai (South Dakota School of Mines and Technology)

16:55 Workshop on Machine learning for Cosmic-Ray Air Showers - Summary & Outlook - Matthias Plum (Marquette University)

17:20 Good Bye - Frank Schroeder (University of Delaware / Karlsruhe Institute of Technology)

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