31 citations · 98 across the 10 of their papers we have counts for
10 papers
Collider-Bench: Benchmarking AI Agents with Particle Physics Analysis Reproduction
Darius A. Faroughy, Sofia Palacios Schweitzer, Ian Pang +2
Autonomous language-model agents are increasingly evaluated on long-horizon tool-use tasks, but existing benchmarks rarely capture the complexity and nuance of real scientific work…
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…
SURFing to the Fundamental Limit of Jet Tagging
Ian Pang, Darius A. Faroughy, David Shih +2
Beyond the practical goal of improving search and measurement sensitivity through better jet tagging algorithms, there is a deeper question: what are their upper performance limits…
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…
CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simulation
Claudius Krause, Michele Faucci Giannelli, Gregor Kasieczka +66
We present the results of the "Fast Calorimeter Simulation Challenge 2022" - the CaloChallenge. We study state-of-the-art generative models on four calorimeter shower datasets of i…
Unifying Simulation and Inference with Normalizing Flows
Haoxing Du, Claudius Krause, Vinicius Mikuni +3
There have been many applications of deep neural networks to detector calibrations and a growing number of studies that propose deep generative models as automated fast detector si…
Anomaly detection with flow-based fast calorimeter simulators
Claudius Krause, Benjamin Nachman, Ian Pang +2
Recently, several normalizing flow-based deep generative models have been proposed to accelerate the simulation of calorimeter showers. Using CaloFlow as an example, we show that t…
Calorimeter shower superresolution
Ian Pang, John Andrew Raine, David Shih
Calorimeter shower simulation is a major bottleneck in the Large Hadron Collider computational pipeline. There have been recent efforts to employ deep-generative surrogate models t…
Inductive Simulation of Calorimeter Showers with Normalizing Flows
Matthew R. Buckley, Claudius Krause, Ian Pang +1
Simulating particle detector response is the single most expensive step in the Large Hadron Collider computational pipeline. Recently it was shown that normalizing flows can accele…
CaloFlow for CaloChallenge Dataset 1
Claudius Krause, Ian Pang, David Shih
CaloFlow is a new and promising approach to fast calorimeter simulation based on normalizing flows. Applying CaloFlow to the photon and charged pion Geant4 showers of Dataset 1 of…