2 citations · 3 across the 5 of their papers we have counts for
5 papers
When CPT Violation Hides in Plain Sight: How CP Measurements Are Compromised and How to Fix Them
Miaochen Jin, Gabriela Barenboim, Carlos A. Argüelles +2
The extraction of the leptonic charge-parity (CP)-violating phase from long-baseline neutrino oscillation experiments rests on the assumption of charge-parity-time (CP…
Machine Learning on Heterogeneous, Edge, and Quantum Hardware for Particle Physics (ML-HEQUPP)
Julia Gonski, Jennifer Ott, Jenni Ott +129
The next generation of particle physics experiments will face a new era of challenges in data acquisition, due to unprecedented data rates and volumes along with extreme environmen…
Comparison of Geometrical Layouts for Next-Generation Large-volume Cherenkov Neutrino Telescopes
Tong Zhu, Miaochen Jin, Carlos A. Argüelles
Water-(Ice-) Cherenkov neutrino telescopes have played a pivotal role in the search and discovery of high-energy astrophysical neutrinos. Experimental collaborations are developing…
Boosting Neutrino Mass Ordering Sensitivity with Inelasticity for Atmospheric Neutrino Oscillation Measurement
Santiago Giner Olavarrieta, Miaochen Jin, Carlos A. Argüelles +2
In this letter, we study the potential of boosting the atmospheric neutrino experiments sensitivity to the neutrino mass ordering (NMO) sensitivity by incorporating inelasticity me…
Two Watts is All You Need: Enabling In-Detector Real-Time Machine Learning for Neutrino Telescopes Via Edge Computing
Miaochen Jin, Yushi Hu, Carlos A. Argüelles
The use of machine learning techniques has significantly increased the physics discovery potential of neutrino telescopes. In the upcoming years, we are expecting upgrade of curren…