Related papers: Diffusion-Based Point-Cloud Generation of Heavy-Io…
The fission-fusion reaction mechanism was proposed in order to generate extremely neutron-rich nuclei close to the waiting point N = 126 of the rapid neutron capture nucleosynthesis process (r-process). The production of such isotopes and…
The precise reconstruction of jet transverse momenta in heavy-ion collisions is a challenging task. A major obstacle is the large number of (mainly) low-$p_{\rm T}$ particles overlaying the jets. Strong region-to-region fluctuations of this…
A method for selecting events with densely populated narrow regions or spikes in a given data sample is discussed. Applying this method to 200 A GeV/c 32S-AgBr and 32S-Gold collision data, a few events having "hot regions" are chosen for…
Jet substructure observables serve as essential tools for probing the quark-gluon plasma produced in relativistic heavy-ion collisions. Their interpretation, however, is often complicated by edge effects, which arise when correlated…
While recent work on text-conditional 3D object generation has shown promising results, the state-of-the-art methods typically require multiple GPU-hours to produce a single sample. This is in stark contrast to state-of-the-art generative…
Diffusion models have demonstrated remarkable success in image generation, but they are computationally intensive and time-consuming to train. In this paper, we introduce a novel diffusion model that benefits from quantum computing…
High-energy collisions at the Large Hadron Collider (LHC) provide valuable insights into open questions in particle physics. However, detector effects must be corrected before measurements can be compared to certain theoretical predictions…
The class-conditional image generation based on diffusion models is renowned for generating high-quality and diverse images. However, most prior efforts focus on generating images for general categories, e.g., 1000 classes in ImageNet-1k. A…
Diffusion models have been popular for point cloud generation tasks. Existing works utilize the forward diffusion process to convert the original point distribution into a noise distribution and then learn the reverse diffusion process to…
Acquiring high-quality data for training discriminative models is a crucial yet challenging aspect of building effective predictive systems. In this paper, we present Diffusion Inversion, a simple yet effective method that leverages the…
We analyze the multiplicity correlations between distant forward and backward rapidity regions in relativistic heavy-ion collisions in a superposition framework, where the particle production occurs through independent emission from…
Autonomous driving demands high-quality LiDAR data, yet the cost of physical LiDAR sensors presents a significant scaling-up challenge. While recent efforts have explored deep generative models to address this issue, they often consume…
A new method of event characterization based on Deep Learning is presented. The PointNet models can be used for fast, online event-by-event impact parameter determination at the CBM experiment. For this study, UrQMD and the CBM detector…
Most natural objects have inherent complexity and variability. While some simple objects can be modeled from first principles, many real-world phenomena, such as cloud formation, require computationally expensive simulations that limit…
Point cloud completion estimates complete shapes from incomplete point clouds to obtain higher-quality point cloud data. Most existing methods only consider global object features, ignoring spatial and semantic information of adjacent…
We present a new study of jet interactions in the quark-gluon plasma created in high-energy heavy-ion collisions, using a multistage event generator within the JETSCAPE framework. We focus on medium-induced modifications in the rate of…
Initial states of high energy heavy ion collisions are studied using a dipole model through the DIPSY event generator that dynamically includes saturation together with the fluctuations and correlations of the BFKL cascade. The…
HYDJET++ is a Monte-Carlo event generator for simulation of relativistic heavy ion AA collisions considered as a superposition of the soft, hydro-type state and the hard state resulting from multi-parton fragmentation. This model is the…
Motivated by the ion-collision program at the Large Hadron Collider, plans for its high-luminosity upgrade, and on-going discussions for multi-TeV future hadron colliders, we systematically investigate hard-scattering, Standard Model…
The coordinate and momentum space configurations of the net baryon number in heavy ion collisions that undergo spinodal decomposition, due to a first-order phase transition, are investigated using state-of-the-art machine-learning methods.…