111 citations · 148 across the 10 of their papers we have counts for
9 papers · 1 filter
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…
Enhancing next token prediction based pre-training for jet foundation models
Joschka Birk, Anna Hallin, Gregor Kasieczka +3
Next token prediction is an attractive pre-training task for jet foundation models, in that it is simulation free and enables excellent generative capabilities that can transfer ac…
Foundation models for high-energy physics
Anna Hallin
The rise of foundation models -- large, pretrained machine learning models that can be finetuned to a variety of tasks -- has revolutionized the fields of natural language processi…
Agents of Discovery
Sascha Diefenbacher, Anna Hallin, Gregor Kasieczka +3
The substantial data volumes encountered in modern particle physics and other domains of fundamental physics research allow (and require) the use of increasingly complex data analy…
OmniJet-: Learning point cloud calorimeter simulations using generative transformers
Joschka Birk, Frank Gaede, Anna Hallin +3
We show the first use of generative transformers for generating calorimeter showers as point clouds in a high-granularity calorimeter. Using the tokenizer and generative part of th…
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…
OmniJet-: The first cross-task foundation model for particle physics
Joschka Birk, Anna Hallin, Gregor Kasieczka
Foundation models are multi-dataset and multi-task machine learning methods that once pre-trained can be fine-tuned for a large variety of downstream applications. The successful d…
Classifying Anomalies THrough Outer Density Estimation (CATHODE)
Anna Hallin, Joshua Isaacson, Gregor Kasieczka +6
We propose a new model-agnostic search strategy for physics beyond the standard model (BSM) at the LHC, based on a novel application of neural density estimation to anomaly detecti…