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Yonghong Wang

Publications and source records attributed to Yonghong Wang.

4 recordsLinked to original sources

Performance of the MPD experiment in dielectron measurements at NICA

The Multi-Purpose Detector (MPD) experiment at NICA is designed to investigate strongly interacting matter at high net-baryon density through a broad program of heavy-ion measurements. Dielectrons constitute one of the key probes in this program, with contributions emitted throughout the entire space-time evolution of the collision. Their experimental measurement is, however, challenged by a large combinatorial background arising primarily from incompletely reconstructed photon conversions and Dalitz decays of light neutral mesons. In this work, we study the performance and capabilities of the MPD experiment for dielectron measurements using simulated minimum-bias $^{209}$Bi+$^{209}$Bi collisions at $\sqrt{s_{NN}}$ = 9.2 GeV. Electron identification is performed using the combined information from the TPC, the TOF detector and the ECal. A multilayer-perceptron classifier is used to optimize electron identification, leading to a substantial increase in electron detection efficiency relative to sequential one-dimensional selections while preserving an electron-sample purity close to unity over a broad momentum range. To address the dominant sources of combinatorial background, a pair-analysis strategy is developed that exploits partially reconstructed electron tracks. The method combines the pair opening angle, the TPC dE/dx signal, and an approximate reconstruction of the pair invariant mass to tag tracks from photon conversions and $π^0$ Dalitz decays at the pair level. In the mass interval 0.2 $< m_{ee}<0.7 $ GeV/$c^2$, the method improves the signal-to-background ratio by a factor of about 3.5 with the current track reconstruction algorithm. The study demonstrates the strong potential of the MPD experiment for dielectron measurements at NICA energies and highlights the importance of further improvements in low-$p_{\rm T}$ track reconstruction.

physics.ins-det↗

Coherent Phonons, Localization and Slow Polaron Formation in Lead-free Gold Perovskite

Lead-free metal halide perovskites are emerging as less-toxic alternatives to their lead-based counterparts. However, their applicability in optoelectronic devices is limited, and the charge transport dynamics remain poorly understood. Understanding photo-induced charge and structural dynamics is critical for unlocking the potential of these novel systems. In this work, we employ ultrafast optical and Raman spectroscopy combined with band structure calculations to investigate the coupled electronic and vibrational dynamics in Caesium gold bromide, a promising lead-free perovskite. We find that the band-edge charge transfer states are strongly coupled to Au-Br stretching phonon modes, leading to frequency modulation of absorption by impulsively excited coherent phonons. Early-stage relaxation is characterized by dynamics of delocalized charge transfer excitation and slowly decaying coherent phonons. The electronic and vibrational relaxation reveals a slow formation of a localized polaronic state in the 10-20 ps timescale. Using a displaced harmonic oscillator model, the polaronic binding energy is estimated to be ~80 meV following lattice relaxation along the phonon modes. Strong exciton-phonon coupling and slow polaron formation via coupling to lattice modes make this material a promising testbed for the control of coherent phonons and localized polaronic states using light.

cond-mat.mtrl-sci↗

Trinity: A No-Code AI platform for complex spatial datasets

We present a no-code Artificial Intelligence (AI) platform called Trinity with the main design goal of enabling both machine learning researchers and non-technical geospatial domain experts to experiment with domain-specific signals and datasets for solving a variety of complex problems on their own. This versatility to solve diverse problems is achieved by transforming complex Spatio-temporal datasets to make them consumable by standard deep learning models, in this case, Convolutional Neural Networks (CNNs), and giving the ability to formulate disparate problems in a standard way, eg. semantic segmentation. With an intuitive user interface, a feature store that hosts derivatives of complex feature engineering, a deep learning kernel, and a scalable data processing mechanism, Trinity provides a powerful platform for domain experts to share the stage with scientists and engineers in solving business-critical problems. It enables quick prototyping, rapid experimentation and reduces the time to production by standardizing model building and deployment. In this paper, we present our motivation behind Trinity and its design along with showcasing sample applications to motivate the idea of lowering the bar to using AI.

cs.SE↗

A Probabilistic Approach for Maintaining Trust Based on Evidence

Leading agent-based trust models address two important needs. First, they show how an agent may estimate the trustworthiness of another agent based on prior interactions. Second, they show how agents may share their knowledge in order to cooperatively assess the trustworthiness of others. However, in real-life settings, information relevant to trust is usually obtained piecemeal, not all at once. Unfortunately, the problem of maintaining trust has drawn little attention. Existing approaches handle trust updates in a heuristic, not a principled, manner. This paper builds on a formal model that considers probability and certainty as two dimensions of trust. It proposes a mechanism using which an agent can update the amount of trust it places in other agents on an ongoing basis. This paper shows via simulation that the proposed approach (a) provides accurate estimates of the trustworthiness of agents that change behavior frequently; and (b) captures the dynamic behavior of the agents. This paper includes an evaluation based on a real dataset drawn from Amazon Marketplace, a leading e-commerce site.

cs.MA↗