arXiv subjects

Dmitrii Kobylianskii

Publications and source records attributed to Dmitrii Kobylianskii.

9 recordsLinked to original sources

arXiv2026

Differentiable Parametric Simulation and Reconstruction Models in Parnassus

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 generative networks trained on paired samples, which are fit automatically to a target detector, or from parametric prescriptions of the kind used by Delphes, which are constructed by hand. We remove this asymmetry by making the parametric models fully differentiable so that their parameters can be fit to a target sample by gradient descent. We demonstrate closure by fitting a parametric model to samples from a known configuration of itself, recovering the generating parameters and characterizing the degeneracies among them, and we present a first fit to CMS full simulation. The resulting models are interpretable, inexpensive, and run in the standard Parnassus pipeline which is fully Python based and GPU enabled.

hep-ex↗

arXiv2026

An AI-based Detector Simulation and Reconstruction Model for the ALEPH Experiment at LEP

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). Training on simulated $e^+e^-$ to Z to qqbar events processed through the ALEPH detector simulation and reconstruction, we demonstrate that Parnassus faithfully reproduces the detector response at the event, jet, and particle levels, with substantially better agreement than the Delphes fast simulation. The clean $e^+e^-$ environment, free of pileup and characterized by simple event topologies, provides a well-controlled benchmark for evaluating the generative model's fidelity. Our results demonstrate that modern neural-network-based generative simulation approaches, developed primarily for LHC experiments, generalize naturally to historical collider experiments with distinct detector geometries and physics environments. This work shows that Parnassus can be applied beyond the LHC context and serves as an important tool for legacy data analysis where archival software tools are challenging to resurrect.

physics.ins-det↗

arXiv2026

Parnassus: A GPU-enabled, Python-based Package for Fast Particle Detector Simulation and Reconstruction

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. Parnassus provides a user-friendly framework with interchangeable detector models: neural models can emulate computationally expensive Geant4-based detector simulation and reconstruction chains, while parametric models provide PyTorch implementations of selected Delphes-style detector responses. This initial release includes two models of the CMS detector: one based on a flow-matching neural network architecture and one based on a PyTorch implementation of the Delphes CMS card (parametric bias and smearing). PyTorch versions of the ATLAS and ALEPH Delphes cards are also available, together with a flow-matching neural model of the ALEPH detector that extends the framework to the e+e- LEP environment. All detector-specific backends share the same process-agnostic and detector-agnostic API: users select a detector card - analogous to choosing a detector card in Delphes - and the same tool can be applied to new physics processes without retraining the released detector model. There are native interfaces to the event generator Pythia and the event clustering package FastJet. Unlike previous C++/ROOT-based tools, Parnassus provides GPU-capable PyTorch detector-response backends and requires no ROOT installation. We describe the installation, command-line and Python API, configuration system, and demonstrate the framework on Standard Model and BSM processes.

hep-ex↗

arXiv2025

CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simulation

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 increasing dimensionality, ranging from a few hundred voxels to a few tens of thousand voxels. The 31 individual submissions span a wide range of current popular generative architectures, including Variational AutoEncoders (VAEs), Generative Adversarial Networks (GANs), Normalizing Flows, Diffusion models, and models based on Conditional Flow Matching. We compare all submissions in terms of quality of generated calorimeter showers, as well as shower generation time and model size. To assess the quality we use a broad range of different metrics including differences in 1-dimensional histograms of observables, KPD/FPD scores, AUCs of binary classifiers, and the log-posterior of a multiclass classifier. The results of the CaloChallenge provide the most complete and comprehensive survey of cutting-edge approaches to calorimeter fast simulation to date. In addition, our work provides a uniquely detailed perspective on the important problem of how to evaluate generative models. As such, the results presented here should be applicable for other domains that use generative AI and require fast and faithful generation of samples in a large phase space.

physics.ins-det↗

arXiv2025

GLOW: A Unified Particle Flow Transformer

We present GLOW, a transformer-based particle flow model that combines incidence matrix supervision from HGPflow with a MaskFormer architecture. Evaluated on CLIC detector simulations, GLOW achieves state-of-the-art performance and, together with prior work, demonstrates that a single unified transformer architecture can effectively address diverse reconstruction tasks in particle physics.

hep-ex↗

arXiv2025

Conditional Deep Generative Models for Simultaneous Simulation and Reconstruction of Entire Events

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 Artificial Intelligence (genAI) tools, continuous normalizing flows and diffusion models, to create a set of reconstructed particle-flow objects conditioned on truth-level particles from CMS Open Simulations. While previous work focused on jets, our updated methods now can accommodate all particle-flow objects in an event along with particle-level attributes like particle type and production vertex coordinates. This approach is fully automated, entirely written in Python, and GPU-compatible. Using a variety of physics processes at the LHC, we show that the extended Parnassus is able to generalize beyond the training dataset and outperforms the standard, public tool Delphes.

hep-ex↗

arXiv2024

Advancing Set-Conditional Set Generation: Diffusion Models for Fast Simulation of Reconstructed Particles

The computational intensity of detector simulation and event reconstruction poses a significant difficulty for data analysis in collider experiments. This challenge inspires the continued development of machine learning techniques to serve as efficient surrogate models. We propose a fast emulation approach that combines simulation and reconstruction. In other words, a neural network generates a set of reconstructed objects conditioned on input particle sets. To make this possible, we advance set-conditional set generation with diffusion models. Using a realistic, generic, and public detector simulation and reconstruction package (COCOA), we show how diffusion models can accurately model the complex spectrum of reconstructed particles inside jets.

hep-ex↗

arXiv2024

Parnassus: An Automated Approach to Accurate, Precise, and Fast Detector Simulation and Reconstruction

Detector simulation and reconstruction are a significant computational bottleneck in particle physics. We develop Particle-flow Neural Assisted Simulations (Parnassus) to address this challenge. Our deep learning model takes as input a point cloud (particles impinging on a detector) and produces a point cloud (reconstructed particles). By combining detector simulations and reconstruction into one step, we aim to minimize resource utilization and enable fast surrogate models suitable for application both inside and outside large collaborations. We demonstrate this approach using a publicly available dataset of jets passed through the full simulation and reconstruction pipeline of the CMS experiment. We show that Parnassus accurately mimics the CMS particle flow algorithm on the (statistically) same events it was trained on and can generalize to jet momentum and type outside of the training distribution.

physics.data-an↗

arXiv2024

CaloGraph: Graph-based diffusion model for fast shower generation in calorimeters with irregular geometry

Denoising diffusion models have gained prominence in various generative tasks, prompting their exploration for the generation of calorimeter responses. Given the computational challenges posed by detector simulations in high-energy physics experiments, the necessity to explore new machine-learning-based approaches is evident. This study introduces a novel graph-based diffusion model designed specifically for rapid calorimeter simulations. The methodology is particularly well-suited for low-granularity detectors featuring irregular geometries. We apply this model to the ATLAS dataset published in the context of the Fast Calorimeter Simulation Challenge 2022, marking the first application of a graph diffusion model in the field of particle physics.

hep-ex↗