Machine learning in the search for new fundamental physics

G Karagiorgi, G Kasieczka, S Kravitz… - Nature Reviews …, 2022 - nature.com
Compelling experimental evidence suggests the existence of new physics beyond the well-
established and tested standard model of particle physics. Various current and upcoming …

Deep learning with photosensor timing information as a background rejection method for the Cherenkov Telescope Array

S Spencer, T Armstrong, J Watson, S Mangano… - Astroparticle …, 2021 - Elsevier
New deep learning techniques present promising new analysis methods for Imaging
Atmospheric Cherenkov Telescopes (IACTs) such as the upcoming Cherenkov Telescope …

Application of graph networks to background rejection in Imaging Air Cherenkov Telescopes

J Glombitza, V Joshi, B Bruno… - Journal of Cosmology and …, 2023 - iopscience.iop.org
Abstract Imaging Air Cherenkov Telescopes (IACTs) are essential to ground-based
observations of gamma rays in the GeV to TeV regime. One particular challenge of ground …

Enhancing Events in Neutrino Telescopes through Deep Learning-Driven Super-Resolution

FJ Yu, N Kamp, CA Argüelles - arXiv preprint arXiv:2408.08474, 2024 - arxiv.org
Recent discoveries by neutrino telescopes, such as the IceCube Neutrino Observatory,
relied extensively on machine learning (ML) tools to infer physical quantities from the raw …

End-to-End Analyses Using Image Classification

A Aurisano, LH Whitehead - Artificial Intelligence For High Energy …, 2022 - World Scientific
End-to-end analyses of data from high-energy physics experiments using machine and
deep learning techniques have emerged in recent years. These analyses use deep learning …

Machine Learning Application for Particle Physics: Mexico's Involvement in the Hyper-Kamiokande Observatory

S Cuen-Rochin, E de la Fuente… - … in Machine Learning …, 2021 - Springer
Abstract The Hyper-Kamiokande (Hyper-K) observatory, the successor to the Super-
Kamiokande (Super-K) experiment, will be the largest underground water Cherenkov …