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…
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…
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…
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…
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…