16 citations · 49 across the 9 of their papers we have counts for
9 papers
TIGER: A Topology-Agnostic, Hierarchical Graph Network for Event Reconstruction
Nathalie Soybelman, Francesco A. Di Bello, Nilotpal Kakati +1
Event reconstruction at the LHC, the task of assigning observed physics objects to their true origins, is a central challenge for precision measurements and searches. Many existing…
Self-Supervised Learning Strategies for Jet Physics
Patrick Rieck, Kyle Cranmer, Etienne Dreyer +5
We extend the re-simulation-based self-supervised learning approach to learning representations of hadronic jets in colliders by exploiting the Markov property of the standard simu…
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…
Accelerating Graph-based Tracking Tasks with Symbolic Regression
Nathalie Soybelman, Carlo Schiavi, Francesco A. Di Bello +1
The reconstruction of particle tracks from hits in tracking detectors is a computationally intensive task due to the large combinatorics of detector signals. Recent efforts have pr…
Parnassus: An Automated Approach to Accurate, Precise, and Fast Detector Simulation and Reconstruction
Etienne Dreyer, Eilam Gross, Dmitrii Kobylianskii +3
Detector simulation and reconstruction are a significant computational bottleneck in particle physics. We develop Particle-flow Neural Assisted Simulations (Parnassus) to address t…
Advancing Set-Conditional Set Generation: Diffusion Models for Fast Simulation of Reconstructed Particles
Dmitrii Kobylianskii, Nathalie Soybelman, Nilotpal Kakati +3
The computational intensity of detector simulation and event reconstruction poses a significant difficulty for data analysis in collider experiments. This challenge inspires the co…
CaloGraph: Graph-based diffusion model for fast shower generation in calorimeters with irregular geometry
Dmitrii Kobylianskii, Nathalie Soybelman, Etienne Dreyer +1
Denoising diffusion models have gained prominence in various generative tasks, prompting their exploration for the generation of calorimeter responses. Given the computational chal…
Set-Conditional Set Generation for Particle Physics
Francesco Armando Di Bello, Etienne Dreyer, Sanmay Ganguly +6
The simulation of particle physics data is a fundamental but computationally intensive ingredient for physics analysis at the Large Hadron Collider, where observational set-valued…
Back to the Formula -- LHC Edition
Anja Butter, Tilman Plehn, Nathalie Soybelman +1
While neural networks offer an attractive way to numerically encode functions, actual formulas remain the language of theoretical particle physics. We show how symbolic regression…