44 citations · 90 across the 20 of their papers we have counts for
7 papers · 1 filter
Anomaly detection for multijet scenarios
Gregor Kasieczka, Sung Hak Lim, Louis Moureaux +3
Signals of physics beyond the Standard Model continue to resist discovery at the LHC. Recent years have seen the proliferation of new anomaly detection techniques, promising discov…
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 dit…
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…