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Yixuan Jin

Publications and source records attributed to Yixuan Jin.

3 recordsLinked to original sources

Production of Light Nuclei in Au+Au Collisions at $\sqrt{s_{\rm NN}} = 7.7-27$ GeV from STAR BES-II

The studies of the production of light nuclei, such as deuteron and helium nuclei, in heavy-ion collisions are essential for understanding the dynamics of nuclear matter under extreme conditions. The yields and ratios of light nuclei serve as an effective method to distinguish between the thermal and coalescence models of light nuclei formation. Within the coalescence framework, the energy dependence of the coalescence parameters reflects the effective volume of the collision system, while in the thermal model yields are governed by chemical freeze-out conditions. The significantly larger datasets from the STAR Beam Energy Scan Phase II (BES-II), combined with enhanced detector capabilities, allow more precise and comprehensive measurements than phase I. In these proceedings, we present measurements of light nuclei production, including p, $\rm \bar{p}$, d, $\rm \bar{d}$, $\rm ^3He$, in Au+Au collisions at BES-II energies of $\sqrt{s_{\rm NN}} = 7.7 - 27$ GeV. The results include centrality-dependent transverse momentum spectra and yields ($\mathrm{d}N/\mathrm{d}y$), along with coalescence parameters $B_\mathrm{A}$ and particle yield ratios. The physics implications of these results are discussed.

nucl-ex

Production of Light Nuclei in Au+Au Collisions with the STAR BES-II Program

The yields and ratios of light nuclei in heavy-ion collisions offer a method to distinguish between the thermal and coalescence models. Ratios such as $\rm N_t \times N_p/N_d^2$ and $\rm N_{^3He} \times N_p/N_d^2$ are suggested as potential probes to investigate critical phenomena within the QCD phase diagram. The significantly larger datasets from STAR BES-II compared to BES-I, combined with enhanced detector capabilities, allow for more precise measurements. In this proceeding, we present the centrality and energy dependence of transverse momentum spectra and particle yields of (anti-)proton, (anti-)deuteron, and $\rm ^3He$ at BES-II energies ($\sqrt{s_{\rm NN}}$ = $7.7 - 27$ GeV), as well as the light nuclei to proton yield ratios and coalescence parameters $(B_2(\rm d)$ and $B_3(\rm ^3He))$.

nucl-ex

Research on Image Recognition Technology Based on Multimodal Deep Learning

This project investigates the human multi-modal behavior identification algorithm utilizing deep neural networks. According to the characteristics of different modal information, different deep neural networks are used to adapt to different modal video information. Through the integration of various deep neural networks, the algorithm successfully identifies behaviors across multiple modalities. In this project, multiple cameras developed by Microsoft Kinect were used to collect corresponding bone point data based on acquiring conventional images. In this way, the motion features in the image can be extracted. Ultimately, the behavioral characteristics discerned through both approaches are synthesized to facilitate the precise identification and categorization of behaviors. The performance of the suggested algorithm was evaluated using the MSR3D data set. The findings from these experiments indicate that the accuracy in recognizing behaviors remains consistently high, suggesting that the algorithm is reliable in various scenarios. Additionally, the tests demonstrate that the algorithm substantially enhances the accuracy of detecting pedestrian behaviors in video footage.

cs.CV