113 citations · 185 across the 17 of their papers we have counts for
7 papers · 1 filter
HGPflow: Extending Hypergraph Particle Flow to Collider Event Reconstruction
Nilotpal Kakati, Etienne Dreyer, Anna Ivina +4
In high energy physics, the ability to reconstruct particles based on their detector signatures is essential for downstream data analyses. A particle reconstruction algorithm based…
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…
Denoising Graph Super-Resolution towards Improved Collider Event Reconstruction
Nilotpal Kakati, Etienne Dreyer, Eilam Gross
In preparation for Higgs factories and energy-frontier facilities, future colliders are moving toward high-granularity calorimeters to improve reconstruction quality. However, the…
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…
PASCL: Supervised Contrastive Learning with Perturbative Augmentation for Particle Decay Reconstruction
Junjian Lu, Siwei Liu, Dmitrii Kobylianski +3
In high-energy physics, particles produced in collision events decay in a format of a hierarchical tree structure, where only the final decay products can be observed using detecto…