113 citations · 185 across the 17 of their papers we have counts for
9 papers · 1 filter
GLOW: A Unified Particle Flow Transformer
Dmitrii Kobylianskii, Samuel Van Stroud, Kwok Yiu Wong +5
We present GLOW, a transformer-based particle flow model that combines incidence matrix supervision from HGPflow with a MaskFormer architecture. Evaluated on CLIC detector simulati…
Conditional Deep Generative Models for Simultaneous Simulation and Reconstruction of Entire Events
Etienne Dreyer, Eilam Gross, Dmitrii Kobylianskii +2
We extend the Particle-flow Neural Assisted Simulations (Parnassus) framework of fast simulation and reconstruction to entire collider events. In particular, we use two generative…
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…
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…
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…
Configurable calorimeter simulation for AI applications
Francesco Armando Di Bello, Anton Charkin-Gorbulin, Kyle Cranmer +9
A configurable calorimeter simulation for AI (COCOA) applications is presented, based on the Geant4 toolkit and interfaced with the Pythia event generator. This open-source project…
Reconstructing particles in jets using set transformer and hypergraph prediction networks
Francesco Armando Di Bello, Etienne Dreyer, Sanmay Ganguly +8
The task of reconstructing particles from low-level detector response data to predict the set of final state particles in collision events represents a set-to-set prediction task r…
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…