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)