111 citations · 148 across the 10 of their papers we have counts for
5 papers · 1 filter
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…
Via Machinae 3.0: A search for stellar streams in Gaia with the CATHODE algorithm
Anna Hallin, David Shih, Claudius Krause +1
We apply the model-agnostic anomaly detection method Cathode - originally developed for particle physics - to search for stellar streams in Gaia data. We combine Cathode with Via M…
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…