6 papers
BGRem: A background noise remover for astronomical images based on a diffusion model
Rodney Nicolaas, Sascha Caron, Fiorenzo Stoppa +4
Context: Astronomical imaging aims to maximize signal capture while minimizing noise. Enhancing the signal-to-noise ratio directly on detectors is difficult and expensive, leading…
Masked-Token Prediction for Anomaly Detection at the Large Hadron Collider
Ambre Visive, Roberto Ruiz de Austri, Polina Moskvitina +2
Anomaly detection in High Energy Physics requires identifying rare signals against overwhelming backgrounds, without prior knowledge of the signal. We present the first application…
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…
Event Tokenization and Masked-Token Prediction for Anomaly Detection at the Large Hadron Collider
Ambre Visive, Polina Moskvitina, Clara Nellist +2
We propose a novel use of Large Language Models (LLMs) as unsupervised anomaly detectors in particle physics. Using lightweight LLM-like networks with encoder-based architectures t…
Attention to the strengths of physical interactions: Transformer and graph-based event classification for particle physics experiments
Luc Builtjes, Sascha Caron, Polina Moskvitina +4
A major task in particle physics is the measurement of rare signal processes. Even modest improvements in background rejection, at a fixed signal efficiency, can significantly enha…
A comparison of Bayesian sampling algorithms for high-dimensional particle physics and cosmology applications
Joshua Albert, Csaba Balazs, Andrew Fowlie +4
For several decades now, Bayesian inference techniques have been applied to theories of particle physics, cosmology and astrophysics to obtain the probability density functions of…