12 papers
Hiding in the Shadow of the Upsilon: Ditaus from a Light Pseudoscalar
Matthew R Buckley, David Shih, Isaac R Wang
The CMS collaboration has reported a measurement of decays to ditaus using of scouting data. If interpreted as the decay of , the measured d…
Neural Scaling Laws for Jet Generation
Oz Amram, Darius A. Faroughy, Tjarko Gerdes +5
Recently observed empirical scaling laws describe the performance of foundation-type models as three independent key quantities -- dataset size, compute, and model parameters -- ar…
Collider-Bench: Benchmarking AI Agents with Particle Physics Analysis Reproduction
Darius A. Faroughy, Sofia Palacios Schweitzer, Ian Pang +2
Autonomous language-model agents are increasingly evaluated on long-horizon tool-use tasks, but existing benchmarks rarely capture the complexity and nuance of real scientific work…
Kitchen Sink Anomaly Detection
Ranit Das, Marie Hein, Gregor Kasieczka +6
An enormous amount of R&D effort has resulted in many new resonant anomaly detection methods being proposed in recent years. However, the vast majority of previous R&D studies have…
Learning to Unscramble: Simplifying Symbolic Expressions via Self-Supervised Oracle Trajectories
David Shih
We present a new self-supervised machine learning approach for symbolic simplification of complex mathematical expressions. Training data is generated by scrambling simple expressi…
Learning to Unscramble Feynman Loop Integrals with SAILIR
David Shih
Integration-by-parts (IBP) reduction of Feynman integrals to master integrals is a key computational bottleneck in precision calculations in high-energy physics. Traditional approa…