Related papers: Diffusion-Based Point-Cloud Generation of Heavy-Io…
Motivated by the problem of pursuit-evasion, we present a motion planning framework that combines energy-based diffusion models with artificial potential fields for robust real time trajectory generation in complex environments. Our…
We present the first theoretical investigation of Fox-Wolfram moments (FWMs) for multi-jet production in relativistic heavy ion collisions. In this work, jet productions in p+p collisions are computed with a Monte Carlo event generator…
The generation of collider data using machine learning has emerged as a prominent research topic in particle physics due to the increasing computational challenges associated with traditional Monte Carlo simulation methods, particularly for…
In this paper, we explore point-cloud based deep learning models to analyze numerical simulations arising from finite element analysis. The objective is to classify automatically the results of the simulations without tedious human…
The method to simulate the rescattering and energy loss of hard partons in heavy ion collisions has been developed. The model is a fast Monte-Carlo tool introduced to modify a standard PYTHIA jet event. The full heavy ion event is obtained…
Generating a 3D point cloud from a single 2D image is of great importance for 3D scene understanding applications. To reconstruct the whole 3D shape of the object shown in the image, the existing deep learning based approaches use either…
Jets produced in high-energy heavy-ion collisions are modified compared to those in proton-proton collisions due to their interaction with the deconfined, strongly-coupled quark-gluon plasma (QGP). In this work, we employ machine learning…
We investigate the possibility of selecting heavy ion collision events with certain features in the initial state ("event engineering"). Anisotropic flow measurements in heavy ion reactions have confirmed the almost ideal fluid dynamical…
A generative model for high-fidelity point clouds is of great importance in synthesizing 3d environments for applications such as autonomous driving and robotics. Despite the recent success of deep generative models for 2d images, it is…
Jet cross sections can be in principle compared to simple pQCD calculations, based on the hypothesis of factorization. But often it is useful or even necessary to not only compute the production rate of the very high pt jets, but in…
By framing reinforcement learning as a sequence modeling problem, recent work has enabled the use of generative models, such as diffusion models, for planning. While these models are effective in predicting long-horizon state trajectories…
In this article, we study the production of Hydrogen and Helium isotopes in heavy-ion collisions in the incident energy range between 80 and 150 MeV/nucleon. We compare their inclusive multiplicities emitted in the transverse plane of the…
The dependencies of charged particle pseudorapidity density and transverse energy pseudorapidity density at midrapidity on the collision energy and on the number of nucleon participants, or centrality, measured in nucleus-nucleus collisions…
In a noncentral heavy-ion collision, the two colliding nuclei have finite angular momentum in the direction perpendicular to the reaction plane. After the collision, a fraction of the total angular momentum is retained in the produced hot…
We apply for the first time the Flow Matching method to the problem of phase-space sampling for event generation in high-energy collider physics. By training the model to remap the random numbers used to generate the momenta and helicities…
We propose a new class of generative models that naturally handle data of varying dimensionality by jointly modeling the state and dimension of each datapoint. The generative process is formulated as a jump diffusion process that makes…
Traditionally, events collected at relativistic heavy-ion colliders are classified according to some centrality estimator (e.g. the number of produced charged particles) related to the initial energy density and volume of the system. In a…
Artificial intelligence (AI) generative models, such as generative adversarial networks (GANs), variational auto-encoders, and normalizing flows, have been widely used and studied as efficient alternatives for traditional scientific…
The simple method for simulation of ``thermal'' hadron spectra in ultrarelativistic heavy ion collisions including longitudinal, transverse and elliptic flow is developed. The model is realized as fast Monte-Carlo event generator.
Generative modeling of high-energy collisions at the Large Hadron Collider (LHC) offers a data-driven route to simulations, anomaly detection, among other applications. A central challenge lies in the hybrid nature of particle-cloud data:…