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4 papers
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…
Lake- and Surface-Based Detectors for Forward Neutrino Physics
Nicholas W. Kamp, Carlos A. Argüelles, Albrecht Karle +2
We propose two medium-baseline, kiloton-scale neutrino experiments to study neutrinos from LHC proton-proton collisions: SINE, a surface-based scintillator panel detector observing…
Constraints and Sensitivities for Dipole-Portal Heavy Neutral Leptons from ND280 and its Upgrade
Ming-Shau Liu, Nicholas Kamp, Carlos A. Argüelles
We report new constraints and sensitivities to heavy neutral leptons (HNLs) with transition magnetic moments, also known as dipole-portal HNLs. This is accomplished using data from…
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…