6 papers
Towards a foundation model for astrophysical source detection: An End-to-End Gamma-Ray Data Analysis Pipeline Using Deep Learning
Judit Pérez-Romero, Saptashwa Bhattacharyya, Sascha Caron +9
The increasing volume of gamma-ray data demands new analysis approaches that can handle large-scale datasets while providing robustness for source detection. We present a Deep Lear…
Precision calibration of calorimeter signals in the ATLAS experiment using an uncertainty-aware neural network
ATLAS Collaboration
The ATLAS experiment at the Large Hadron Collider explores the use of modern neural networks for a multi-dimensional calibration of its calorimeter signal defined by clusters of to…
Towards a Large Physics Benchmark
Kristian G. Barman, Sascha Caron, Faegheh Hasibi +5
We introduce a benchmark framework developed by and for the scientific community to evaluate, monitor and steer large language model development in fundamental physics. Building on…
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…
Universal Anomaly Detection at the LHC: Transforming Optimal Classifiers and the DDD Method
Sascha Caron, José Enrique GarcÃa Navarro, MarÃa Moreno Llácer +5
In this work, we present a novel approach to transform supervised classifiers into effective unsupervised anomaly detectors. The method we have developed, termed Discriminatory Det…
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).…