Publications (4)
Learning Efficient Representations of Neutrino Telescope Events
Felix J. Yu, Nicholas Kamp, Carlos A. Argüelles
Neutrino telescopes detect rare interactions of particles produced in some of the most extreme environments in the Universe. This is accomplished by instrumenting a cubic-kilometer…
Reducing Simulation Dependence in Neutrino Telescopes with Masked Point Transformers
Felix J. Yu, Nicholas Kamp, Carlos A. Argüelles
Machine learning techniques in neutrino physics have traditionally relied on simulated data, which provides access to ground-truth labels. However, the accuracy of these simulation…
Trigger-Level Event Reconstruction for Neutrino Telescopes Using Sparse Submanifold Convolutional Neural Networks
Felix J. Yu, Jeffrey Lazar, Carlos A. Argüelles
Convolutional neural networks (CNNs) have seen extensive applications in scientific data analysis, including in neutrino telescopes. However, the data from these experiments presen…
Enhancing Events in Neutrino Telescopes through Deep Learning-Driven Super-Resolution
Felix J. Yu, Nicholas Kamp, Carlos A. Argüelles
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 pho…