Related papers: Diffusion-Based Point-Cloud Generation of Heavy-Io…
In High Energy Physics, detailed and time-consuming simulations are used for particle interactions with detectors. To bypass these simulations with a generative model, the generation of large point clouds in a short time is required, while…
Cluster production plays an important role in heavy-ion collisions at intermediate beam energies, where light nuclei contribute substantially to final-state yields and to other observables that are used to infer the nuclear equation of…
Fast data generation based on Machine Learning has become a major research topic in particle physics. This is mainly because the Monte Carlo simulation approach is computationally challenging for future colliders, which will have a…
Convolutional Neural Nets, which is a powerful method of Deep Learning, is applied to classify equation of state of heavy-ion collision event generated within the UrQMD model. Event-by-event transverse momentum and azimuthal angle…
We develop an Effective Field Theory approach for jet observables in heavy-ion collisions, where the jet is treated as an open quantum system interacting with a hot and dense QCD medium. Within this framework, we derive a novel…
Accurate and fast simulation of particle physics processes is crucial for the high-energy physics community. Simulating particle interactions with detectors is both time consuming and computationally expensive. With the proton-proton…
Jet interactions in a hot QCD medium created in heavy-ion collisions are conventionally assessed by measuring the modification of the distributions of jet observables with respect to the proton-proton baseline. However, the steeply falling…
We present a scalable technique for the simulation of collider events with multi-jet final states, based on an improved parton-level event file format. The method is implemented for both leading- and next-to-leading order QCD calculations.…
The diffusion model has demonstrated promising results in image generation, recently becoming mainstream and representing a notable advancement for many generative modeling tasks. Prior applications of the diffusion model for both fast…
We discuss the rapidity distribution of produced jets in heavy-ion collisions at LHC. The process allows one to determine to a good accuracy the value of the impact parameter of the nuclear collision in each single inelastic event. The…
We propose a diffusion model designed to generate point-based shape representations with correspondences. Traditional statistical shape models have considered point correspondences extensively, but current deep learning methods do not take…
Generating realistic 3D point clouds is a fundamental problem in computer vision with applications in remote sensing, robotics, and digital object modeling. Existing generative approaches primarily capture geometry, and when semantics are…
Heavy-ion collision is an important tool to understand the dense nuclear matter properties. In order to understand the results of the heavy-ion collision experiments, both theoretical approaches to dense nuclear matter using effective…
We present PointInfinity, an efficient family of point cloud diffusion models. Our core idea is to use a transformer-based architecture with a fixed-size, resolution-invariant latent representation. This enables efficient training with…
Nuclear modification factor predicts whether a medium is formed in a collision system or not. One may verify, if scaling of momentum (transverse) distribution from heavy-ion systems with incoherent superposition of number of binary…
We present a new technique to calculate the cross-section for diffractive vector meson production and DVCS in electron-ion collisions based on the dipole model. The measurement of these processes can provide valuable information on…
Diffusion models have emerged as a powerful tool for point cloud generation. A key component that drives the impressive performance for generating high-quality samples from noise is iteratively denoise for thousands of steps. While…
Simulating showers of particles in highly-granular detectors is a key frontier in the application of machine learning to particle physics. Achieving high accuracy and speed with generative machine learning models would enable them to…
Systems with different interactions could develop the same critical behaviour due to the underlying symmetry and universality. Using this principle of universality, we can embed critical correlations modeled on the 3D Ising model into the…
This work presents an analysis of event-by-event multiplicity fluctuations as a sensitive tool for diagnosing the state of matter produced in relativistic heavy-ion collisions. Using a modified version of the HIJING Monte Carlo generator,…