Related papers: Machine Learning model driven prediction of the in…
The search for the chiral magnetic effect (CME) in relativistic heavy-ion collisions (HICs) is challenged by significant background contamination. We present a novel deep learning approach based on a U-Net architecture to time-reversely…
There is a well-established relation between the spatial asymmetry in the initial stage of a heavy-ion collision and the final momentum anisotropy, which allows for a separation of effects from initial conditions vs. later evolution and has…
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…
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…
Heavy-ion collision physics has entered the high precision era, demanding theoretical models capable of generating huge statistics to compare with experimental data. However, traditional hybrid models, which combine hydrodynamics and…
We present a scalable machine learning (ML) framework for predicting intensive properties and particularly classifying phases of many-body systems. Scalability and transferability are central to the unprecedented computational efficiency of…
In the Large Hardron Collider (LHC), multiple proton-proton collisions cause pileup in reconstructing energy information for a single primary collision (jet). This project aims to select the most important features and create a model to…
The spatial distribution of interactions in high energy collisions of heavy nuclei is discussed using the wounded nucleon, binary collision, hard sphere, and colliding disk parameterizations of interaction densities. The mean radius, its…
The first results from heavy ion collisions at the Large Hadron Collider for charged particle spectra and elliptic flow are compared to an event-by-event hybrid approach with an ideal hydrodynamic expansion. This approach has been shown to…
Building a sustainable burn platform in inertial confinement fusion (ICF) requires an understanding of the complex coupling of physical processes and the effects that key experimental design changes have on implosion performance. While…
This study demonstrates a proof-of-concept application of a deep neural network for particle identification in simulated high transverse momentum proton-proton collisions, with a focus on evaluating model performance under controlled…
Collisions of isobar nuclei, those with the same mass number but different structure parameters, provide a new way to probe the initial condition of the heavy ion collisions. Using transport model simulation of $^{96}$Ru+$^{96}$Ru and…
This article presents differential protection of the distribution line connecting a wind farm in a microgrid. Machine Learning (ML) based models are built using differential features extracted from currents at both ends of the line to…
We have obtained a new set of parameters in a multiphase transport (AMPT) model that are able to describe both the charged particle multiplicity density and elliptic flow measured in Au+Au collisions at center of mass energy…
Machine learning (ML) algorithms have undergone an explosive development impacting every aspect of computational chemistry. To obtain reliable predictions, one needs to maintain the proper balance between the black-box nature of ML…
The H\"uckel Hamiltonian is an incredibly simple tight-binding model famed for its ability to capture qualitative physics phenomena arising from electron interactions in molecules and materials. Part of its simplicity arises from using only…
Collision centrality is a key parameter for studying nuclear matter properties, as it determines the initial interaction geometry and the size of the produced system. Accurate centrality determination is essential for comparing experimental…
In this article, we briefly review recent progress on hydrodynamic modeling and its implementations to relativistic heavy-ion collisions at RHIC and the LHC. The related topics include: 1) initial state fluctuations, final state…
This presentation discusses some recently active topics in the theoretical interpretation of high energy heavy ion collisions at the LHC and at RHIC. We argue that the standard paradigm for understanding the spacetime evolution of the bulk…
Hamiltonian parameter estimation is crucial in condensed matter physics, but time and cost consuming in terms of resources used. With advances in observation techniques, high-resolution images with more detailed information are obtained,…