29 citations · 42 across the 8 of their papers we have counts for
9 papers
SPADE: Split-and-Delay Embeddings for Autoregressive High-Granularity Calorimeter Simulation
Joschka Birk, Frank Gaede, Anna Hallin +3
We introduce SPADE (SPlit And Delay Embeddings), an autoregressive transformer for sequences whose tokens carry multiple features. Rather than embedding these features jointly, SPA…
Pre-Training for Simulation-Based Science: A Study on Jet Foundation Model Training Objectives
Ibrahim Elsharkawy, Joschka Birk, Vinicius Mikuni +3
Foundation models (FMs) trained on large datasets and fine-tuned on downstream tasks have emerged as a powerful paradigm in AI for science. Industrial FMs are typically trained usi…
A Scientific Human-Agent Reproduction Pipeline
Joschka Birk, Gregor Kasieczka, Siddharth Mishra-Sharma +3
Reproducing scientific analyses is essential for preserving knowledge, building extensible codebases, and deepening researcher understanding - yet the effort often outweighs its ac…
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…
Reconstructing hadronically decaying tau leptons with a jet foundation model
Laurits Tani, Joosep Pata, Joschka Birk
The limited availability and accuracy of simulated data has motivated the use of foundation models in high energy physics, with the idea to first train a task-agnostic model on lar…
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…
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…
Flow Matching Beyond Kinematics: Generating Jets with Particle-ID and Trajectory Displacement Information
Joschka Birk, Erik Buhmann, Cedric Ewen +2
We introduce the first generative model trained on the JetClass dataset. Our model generates jets at the constituent level, and it is a permutation-equivariant continuous normalizi…