相关论文: Estimating centrality in heavy-ion collisions usin…

In the context of radio galaxy classification, most state-of-the-art neural network algorithms have been focused on single survey data. The question of whether these trained algorithms have cross-survey identification ability or can be…

Jet modification in heavy-ion collisions provides microscopic access to the properties of the quark-gluon plasma. However, conventional approaches based on traditional global observables, such as \(R_{AA}\), capture limited information…

In these proceedings, we present the measurements of centrality, transverse momentum and rapidity dependences of proton ($p$) and light nuclei ($d$ ($\overline{d}$), $t$, $^{3}\mathrm{He}$ ($\overline{^{3}\mathrm{He}}$), and…

Hyperorder cumulants $C_5/C_1$ and $C_6/C_2$ are recommended as sensitive observables to explore the QCD phase transition in heavy-ion collisions. Precisely measuring their results remains a difficult task in experiments, when employing the…

Energy dependence of specific heat extracted from temperature fluctuation of Au + Au collisions at $\sqrt{s_{NN}}$ = 7.7 GeV to 200 GeV was investigated by using a multiphase transport (AMPT) model. The results were compared with those from…

Point Transformers are near state-of-the-art models for classification, segmentation, and detection tasks on Point Cloud data. They utilize a self attention based mechanism to model large range spatial dependencies between multiple point…

An important aspect of the study of Quark-Gluon Plasma (QGP) in ultra-relativistic collisions of heavy ions is the ability to identify, in experimental data, a subset of the jets that were strongly modified by the interaction with the QGP.…

Heavy ion collisions at intermediate energies can be studied in the context of the Vlasov-Uehling-Uhlenbeck (VUU) model. One of the main features in this model is the nucleon-nucleon (NN) cross section in the collisional term. Quark…

The objective of this study is to evaluate whether the number of requisite training samples can be reduced with the use of various transfer learning models for predicting, for example, the chemical source terms of the data-driven…

Event recognition in still images is an intriguing problem and has potential for real applications. This paper addresses the problem of event recognition by proposing a convolutional neural network that exploits knowledge of objects and…

Transfer learning for deep neural networks is the process of first training a base network on a source dataset, and then transferring the learned features (the network's weights) to a second network to be trained on a target dataset. This…

We report the largest scale deep learning with High Performance Computing (HPC) to physics analysis with the CMS simulation data in proton-proton collisions at 13 TeV. We build a Convolutional Neural Network (CNN) model that takes low-level…

We present a measurement of the pseudorapidity density of primary charged particles near mid-rapidity in Au+Au collisions at sqrt(s_NN) = 130 GeV as a function of the number of participating nucleons. These results are compared to models in…

Additive manufacturing (AM) is gaining attention across various industries like healthcare, aerospace, and automotive. However, identifying defects early in the AM process can reduce production costs and improve productivity - a key…

Transverse energy ($E_T$) distributions have been measured for Au+Au collisions at $\sqrt{s_{NN}}= 200$ GeV by the STAR collaboration at RHIC. $E_T$ is constructed from its hadronic and electromagnetic components, which have been measured…

Automatic defect recognition is one of the research hotspots in steel production, but most of the current methods mainly extract features manually and use machine learning classifiers to recognize defects, which cannot tackle the situation,…

We present results on charged particle and transverse energy densities measured at mid-rapidity in Au-Au collisions at sqrt(s_{NN})=200 GeV. The mean transverse energy per charged particle is derived. The results are presented as a function…

Employing the isospin-dependent Boltzmann-Uehling-Uhlenbeck transport model, the 1 GeV/nucleon deformed uranium-uranium ultra-central collisions are simulated. Based on sensitive observables, mean square collective flow and pion meson…

There are many award-winning pre-trained Convolutional Neural Network (CNN), which have a common phenomenon of increasing depth in convolutional layers. However, I inspect on VGG network, which is one of the famous model submitted to…

In this work, by using the machine learning methods, we study the sensitivities of heavy pseudo-Dirac neutrino $N$ in the inverse seesaw at the high-energy hadron colliders. The production process for the signal is $pp \to \ell N \to 3 \ell…