6 papers
CDRL: Certification-Driven Reinforcement Learning for Neutrino Flavor Model Discovery
Piyush Jha, Jake Rudolph, Victoria Knapp-Pérez +3
Many scientific discovery problems require searching combinatorial hypothesis spaces under complex domain constraints. Reinforcement learning (RL) offers a promising approach, but…
Time-dependent signals of new physics at the LHC
Max H. Fieg, Patrick J. Fox, Jinbo Zhang +3
The Large Hadron Collider (LHC) is sensitive to signals of beyond the Standard Model physics through a variety of channels including missing energy and resonance searches. In most…
Towards AI-assisted Neutrino Flavor Theory Design
Jason Benjamin Baretz, Max Fieg, Vijay Ganesh +4
Particle physics theories, such as those which explain neutrino flavor mixing, arise from a vast landscape of model-building possibilities. A model's construction typically relies…
Symbolic Density Estimation: A Decompositional Approach
Angelo Rajendram, Xieting Chu, Vijay Ganesh +2
We introduce AI-Kolmogorov, a novel framework for Symbolic Density Estimation (SymDE). Symbolic regression (SR) has been effectively used to produce interpretable models in standar…
Learning to Reconstruct Quirky Tracks
Qiyu Sha, Daniel Murnane, Max Fieg +4
Analysis of data from particle physics experiments traditionally sacrifices some sensitivity to new particles for the sake of practical computability, effectively ignoring some pot…
Untangling New Physics in Single Resonant Top Quarks
Krish Wu, Brandon Sun, Nitish Polishetty +3
Collisions of particles at the energy frontier can reveal new particles and forces via localized excesses. However, the initial observation may be consistent with a large variety o…