arXiv subjects

Zhijian Zhang

Publications and source records attributed to Zhijian Zhang.

7 recordsLinked to original sources

arXiv2026

Quantum-statistical effects of bosonic warm dark matter in microscopic interacting dark sectors

We investigate the impact of the quantum statistical properties of bosonic warm dark matter (BWDM) on a microscopic interacting dark-sector model mediated by a Yukawa coupling. We consider a BWDM scenario containing a Bose--Einstein condensed (BEC) component. By separating the BWDM phase-space distribution into thermal and condensate components, we derive the thermally averaged annihilation cross sections for the thermal--thermal, thermal--condensate, and condensate--condensate channels. The long-range scalar interaction and its Sommerfeld enhancement are included in the annihilation processes. We find that the condensate fraction provides an additional quantum-statistical degree of freedom controlling the microscopic dark-sector energy transfer. In particular, the transition between the thermal--thermal dominated regime and the condensate--condensate dominated regime is characterized by a critical condensate fraction r_c, which is mainly determined by the BWDM mass and the dark energy scalar field mass. For condensate fractions above this critical value, the condensate--condensate channel dominates the present-day interaction rate. By imposing the condition that the present-day interaction rate does not exceed the Hubble expansion rate, we determine the corresponding region of the dark-sector parameter space satisfying this condition.

hep-ph↗

arXiv2025

Quantum Spin Correlation Amplification Enables Macroscopic Detection of Atomic-Level Fatigue in Ferromagnetic Metals

Structural fatigue failures account for most of catastrophic metal component failures, annually causing thousands of accidents, tens of thousands of casualties, and $100 billion in global economic losses. Current detection methods struggle to identify early-stage fatigue damage characterized by sub-nanometer atomic displacements and localized bond rupture. Here we present a quantum-enhanced monitoring framework leveraging the fundamental symbiosis between metallic bonding forces and magnetic interactions. Through magnetic excitation of quantum spin correlation in metallic structures, we establish a macroscopic quantum spin correlation amplification technology that visualizes fatigue-induced magnetic flux variations corresponding to bond strength degradation. Our multi-scale analysis integrates fatigue life prediction with quantum mechanical parameters (bonding force constants, crystal orbital overlap population) and ferromagnetic element dynamics, achieving unprecedented prediction accuracy (R^2>0.9, p<0.0001). In comprehensive fatigue trials encompassing 193 ferromagnetic metal specimens across 3,700 testing hours, this quantum magnetic signature consistently provided macroscopic fracture warnings prior to failure - a critical advance enabling 100% early detection success. This transformative framework establishes the first operational platform for preemptive fatigue mitigation in critical infrastructure, offering a paradigm shift from post-failure analysis to quantum-enabled predictive maintenance.

cond-mat.mtrl-sci↗

arXiv2024

Quantum Statistical Effects on Warm Dark Matter and the Mass Constraint from the Cosmic Structure at Small Scales

The suppression of the small-scale matter power spectrum is a distinct feature of Warm Dark Matter (WDM), which permits a constraint on the WDM mass from galaxy surveys. In the thermal relic WDM scenario, quantum statistical effects are not manifest. In a unified framework, we investigate the quantum statistical effects for a fermion case with a degenerate pressure and a boson case with a Bose-Einstein condensation (BEC). Compared to the thermal relic case, the degenerate fermion case only slightly lowers the mass bound, while the boson case with a high initial BEC fraction ($\gtrsim90\%$) significantly lowers it. On the other hand, the BEC fraction drops during the relativistic-to-nonrelativistic transition and completely disappears if the initial fraction is below $\sim64$\%. Given the rising interest in resolving the late-time galaxy-scale problems with boson condensation, a question is posed on how a high initial BEC fraction can be dynamically created so that a condensed DM component remains today.

astro-ph.CO↗

arXiv2022

Unsupervised Galaxy Morphological Visual Representation with Deep Contrastive Learning

Galaxy morphology reflects structural properties which contribute to understand the formation and evolution of galaxies. Deep convolutional networks have proven to be very successful in learning hidden features that allow for unprecedented performance on galaxy morphological classification. Such networks mostly follow the supervised learning paradigm which requires sufficient labelled data for training. However, it is an expensive and complicated process of labeling for million galaxies, particularly for the forthcoming survey projects. In this paper, we present an approach based on contrastive learning with aim for learning galaxy morphological visual representation using only unlabeled data. Considering the properties of low semantic information and contour dominated of galaxy image, the feature extraction layer of the proposed method incorporates vision transformers and convolutional network to provide rich semantic representation via the fusion of the multi-hierarchy features. We train and test our method on 3 classifications of datasets from Galaxy Zoo 2 and SDSS-DR17, and 4 classifications from Galaxy Zoo DECaLS. The testing accuracy achieves 94.7%, 96.5% and 89.9% respectively. The experiment of cross validation demonstrates our model possesses transfer and generalization ability when applied to the new datasets. The code that reveals our proposed method and pretrained models are publicly available and can be easily adapted to new surveys.

astro-ph.GA↗

arXiv2019

Hierarchical Reinforcement Learning for Multi-agent MOBA Game

Real Time Strategy (RTS) games require macro strategies as well as micro strategies to obtain satisfactory performance since it has large state space, action space, and hidden information. This paper presents a novel hierarchical reinforcement learning model for mastering Multiplayer Online Battle Arena (MOBA) games, a sub-genre of RTS games. The novelty of this work are: (1) proposing a hierarchical framework, where agents execute macro strategies by imitation learning and carry out micromanipulations through reinforcement learning, (2) developing a simple self-learning method to get better sample efficiency for training, and (3) designing a dense reward function for multi-agent cooperation in the absence of game engine or Application Programming Interface (API). Finally, various experiments have been performed to validate the superior performance of the proposed method over other state-of-the-art reinforcement learning algorithms. Agent successfully learns to combat and defeat bronze-level built-in AI with 100% win rate, and experiments show that our method can create a competitive multi-agent for a kind of mobile MOBA game {\it King of Glory} in 5v5 mode.

cs.LG↗

arXiv2017

Multi-parameter mechanical and thermal sensing based on multi-mode planar photonic crystals

This paper proposes a novel multifunctional sensing platform based on multimode planar photonic crystals (PPCs). We analytically and numerically demonstrate that the reflection spectrum of PPCs exhibits multiple high-Q resonant modes, and the fundamental and higher-order modes respond distinctively to external mechanical and thermal perturbations, rendering the PPCs superior capability for detection and discrimination of multiple parameters. We further demonstrate simultaneous pressure and temperature sensing with a PPC sensor. Other advantages of PPC sensors include on-chip integration and wafer-scale fabrications.

physics.optics↗

arXiv2013

Target Mass Monitoring and Instrumentation in the Daya Bay Antineutrino Detectors

The Daya Bay experiment measures sin^2 2θ_13 using functionally identical antineutrino detectors located at distances of 300 to 2000 meters from the Daya Bay nuclear power complex. Each detector consists of three nested fluid volumes surrounded by photomultiplier tubes. These volumes are coupled to overflow tanks on top of the detector to allow for thermal expansion of the liquid. Antineutrinos are detected through the inverse beta decay reaction on the proton-rich scintillator target. A precise and continuous measurement of the detector's central target mass is achieved by monitoring the the fluid level in the overflow tanks with cameras and ultrasonic and capacitive sensors. In addition, the monitoring system records detector temperature and levelness at multiple positions. This monitoring information allows the precise determination of the detectors' effective number of target protons during data taking. We present the design, calibration, installation and in-situ tests of the Daya Bay real-time antineutrino detector monitoring sensors and readout electronics.

physics.ins-det↗