8 citations · 28 across the 11 of their papers we have counts for
12 papers
Scaling Collider Event Generation with Residual-Quantized Tokens
Dan Godi, Dmitrii Kobylianskii, Eilam Gross
Full detector simulation and reconstruction of collider events are projected to become major bottlenecks at the High-Luminosity Large Hadron Collider, motivating the development of…
Differentiable Parametric Simulation and Reconstruction Models in Parnassus
Abdelrahman Elabd, Eilam Gross, Dmitrii Kobylianskii +2
Parnassus is a framework for fast detector simulation and reconstruction, directly mapping truth-level particles onto reconstructed objects. Such models can be built from deep gene…
Parnassus: A GPU-enabled, Python-based Package for Fast Particle Detector Simulation and Reconstruction
Abdelrahman Elabd, Eilam Gross, Dmitrii Kobylianskii +1
We present the public software release of Parnassus, a Python/PyTorch, GPU-compatible framework for fast detector simulation and reconstruction in particle and nuclear physics. Par…
An AI-based Detector Simulation and Reconstruction Model for the ALEPH Experiment at LEP
Ya-Feng Lo, Dmitrii Kobylianskii, Benjamin Nachman +1
We present the application of Parnassus, a generative model for full detector simulation and reconstruction, to the ALEPH detector at the Large Electron-Positron Collider (LEP). Tr…
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…
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…
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…