44 citations · 90 across the 20 of their papers we have counts for
4 papers · 1 filter
Aspen Open Jets: Unlocking LHC Data for Foundation Models in Particle Physics
Oz Amram, Luca Anzalone, Joschka Birk +7
Foundation models are deep learning models pre-trained on large amounts of data which are capable of generalizing to multiple datasets and/or downstream tasks. This work demonstrat…
Universal New Physics Latent Space
Anna Hallin, Gregor Kasieczka, Sabine Kraml +4
We develop a machine learning method for mapping data originating from both Standard Model processes and various theories beyond the Standard Model into a unified representation (l…
Convolutional L2LFlows: Generating Accurate Showers in Highly Granular Calorimeters Using Convolutional Normalizing Flows
Thorsten Buss, Frank Gaede, Gregor Kasieczka +2
In the quest to build generative surrogate models as computationally efficient alternatives to rule-based simulations, the quality of the generated samples remains a crucial fronti…
Complete Optimal Non-Resonant Anomaly Detection
Gregor Kasieczka, John Andrew Raine, David Shih +1
We propose the first-ever complete, model-agnostic search strategy based on the optimal anomaly score, for new physics on the tails of distributions. Signal sensitivity is achieved…