611 citations · 3k across the 87 of their papers we have counts for
6 papers · 1 filter
Strong CWoLa: Binary Classification Without Background Simulation
Samuel Klein, Matthew Leigh, Stephen Mulligan +1
Supervised deep learning methods have been successful in the field of high energy physics, and the trend within the field is to move away from high level reconstructed variables to…
End-to-End Optimal Detector Design with Mutual Information Surrogates
Kinga Anna Wozniak, Stephen Mulligan, Jan Kieseler +3
We introduce a novel approach for end-to-end black-box optimization of high energy physics (HEP) detectors using local deep learning (DL) surrogates. These surrogates approximate a…
Strategic White Paper on AI Infrastructure for Particle, Nuclear, and Astroparticle Physics: Insights from JENA and EuCAIF
Sascha Caron, Andreas Ipp, Gert Aarts +16
Artificial intelligence (AI) is transforming scientific research, with deep learning methods playing a central role in data analysis, simulations, and signal detection across parti…
TRANSIT your events into a new mass: Fast background interpolation for weakly-supervised anomaly searches
Ivan Oleksiyuk, Svyatoslav Voloshynovskiy, Tobias Golling
We introduce a new model for conditional and continuous data morphing called TRansport Adversarial Network for Smooth InTerpolation (TRANSIT). We apply it to create a background da…
Large Physics Models: Towards a collaborative approach with Large Language Models and Foundation Models
Kristian G. Barman, Sascha Caron, Emily Sullivan +19
This paper explores ideas and provides a potential roadmap for the development and evaluation of physics-specific large-scale AI models, which we call Large Physics Models (LPMs).…
Robust resonant anomaly detection with NPLM
Gaia Grosso, Debajyoti Sengupta, Tobias Golling +1
In this study, we investigate the application of the New Physics Learning Machine (NPLM) algorithm as an alternative to the standard CWoLa method with Boosted Decision Trees (BDTs)…