相关论文: Estimating centrality in heavy-ion collisions usin…
In this paper, we analyze the effectiveness of transfer learning on classifying electronic components. Transfer learning reuses pre-trained models to save time and resources in building a robust classifier rather than learning from scratch.…
The nucleus-nucleus impact parameter and collision geometry of a heavy ion collision are typically characterized by assigning a collision "centrality". In all present heavy ion experiments centrality is measured indirectly, by detecting the…
A semi-classical model describing the charge transfer collisions of $C_{60}$ fullerene with different slow ions has been developed to explain available experimental data. This data reveals multiple Breit-Wigner like peaks in the cross…
We report the collision-centrality dependence of cumulants of event-by-event net-proton, net-charge and net-kaon distributions in Au+Au collisions for center-of-mass energy $\sqrt{s_{NN}}$ = 54.4 GeV from the STAR experiment. Strong…
Using deep convolutional neural network (CNN), the nature of the QCD transition can be identified from the final-state pion spectra from hybrid model simulations of heavy-ion collisions that combines a viscous hydrodynamic model with a…
We implement the quark-meson coupling model in Daejeon Boltzmann-Uehling-Uhlenbeck (DJBUU) transport model and perform Au+Au collision simulations at intermediate energies. Results are compared with simulations using a conventional quantum…
The transport approach is a useful tool to study dynamics of non-equilibrium systems. For heavy-ion collisions at intermediate energies, where both the smooth nucleon potential and the hard-core nucleon-nucleon collision are important, the…
Active target time projection chambers are important tools in low energy radioactive ion beams or gamma rays related researches. In this work, we present the application of machine learning methods to the analysis of data obtained from an…
Collisions of heavy ions (nuclei) at ultra-relativistic energies (sqrt(s_NN) >> 10 GeV per nucleon-nucleon collision in the centre of mass system) are regarded as a unique tool to produce in the laboratory a high energy density and high…
Breast cancer is a major global health issue that affects millions of women worldwide. Classification of breast cancer as early and accurately as possible is crucial for effective treatment and enhanced patient outcomes. Deep transfer…
Transfer learning (TL) is used to extrapolate the physics information encoded in a Generative Adversarial Network (GAN) trained on synthetic neutrino-carbon inclusive scattering data to related processes such as neutrino-argon 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…
The centrality or the number of initial-state sources $V$ of the system produced in heavy ion collision is a concept that is not uniquely defined and subject to significant theoretical and experimental uncertainties. We argue that a more…
Classification of the extent of damage suffered by a building in a seismic event is crucial from the safety perspective and repairing work. In this study, authors have proposed a CNN based autonomous damage detection model. Over 1200 images…
Accurate and timely prediction of crash severity is crucial in mitigating the severe consequences of traffic accidents. Accurate and timely prediction of crash severity is crucial in mitigating the severe consequences of traffic accidents.…
To support N-1 pre-fault transient stability assessment, this paper introduces a new data collection method in a data-driven algorithm incorporating the knowledge of power system dynamics. The domain knowledge on how the disturbance effect…
It has been demonstrated that deep neural networks outperform traditional machine learning. However, deep networks lack generalisability, that is, they will not perform as good as in a new (testing) set drawn from a different distribution…
Transfer learning for high-dimensional Gaussian graphical models (GGMs) is studied with the goal of estimating the target GGM by utilizing the data from similar and related auxiliary studies. The similarity between the target graph and each…
The charged-particle multiplicity distribution and the transverse-energy distribution measured in heavy-ion collisions at top RHIC and LHC energies are described using the two-component model approach based on a convolution of the Monte…
The balance function is a new observable based on the principle that charge is locally conserved when particles are pair produced. Balance functions have been measured for charged particle pairs and identified charged pion pairs in Au+Au…