4 papers
A Differentiable Neural Surrogate for Photon Propagation in Neutrino Telescopes
Felix J. Yu, Berthy T. Feng, Nicholas Kamp +1
Large-volume neutrino telescopes infer neutrino properties from Cherenkov light, but simulating the transport of billions of photons through highly scattering ice or water is compu…
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…
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…
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…