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Shengyi Liu

Publications and source records attributed to Shengyi Liu.

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Implications of Inelastic Dark Matter for Primordial Dark-Star Evolution: Kinematic Thresholds and Nonthermal Capture

The recent 248-keV nuclear recoil candidate reported by LUX-ZEPLIN has renewed interest in endothermic inelastic dark matter, motivating us to examine its capture in primordial dark stars. Relative to the conventional elastic-capture picture in dark star evolution, endothermic capture adds two qualitative features. First, it opens only after the growing star crosses a compactness threshold, expressed as a kinematic radius $R_{\rm kin}\propto\mu_{\chi}M_\star/\delta$ set by the stellar mass, the dark-matter--nucleus reduced mass and the mass splitting. The accreting growth carries the star through the threshold, and the capture rate turns on quadratically above it. Second, although newly captured particles generically begin on nonthermal bound orbits, endothermic kinematics can keep this normally transient population spatially extended, turning it into a persistent reservoir rather than an intermediate step toward a thermal core. Following complete chains of state-changing collisions, we find that the reservoir compacts sharply and then stalls, because a ground state particle below a compactness-dependent orbital energy has no allowed up-scatter anywhere in the star. A percent-level radius contraction reopens the relaxation. These kinematic results do not depend on whether the excited state decays promptly or is long-lived. As a result, inelastic capture does not replenish a thermal annihilation core. The captured population forms an evolving orbital distribution that sets up the co-evolutionary dynamics between the star and the dark matter in the core and the reservoir, which we develop in a companion paper.

hep-ph

Probing Axion via M\"ossbauer Spectroscopy

We propose using the ultra-narrow 88 keV M\"ossbauer transition in $^{109}$Ag to search for QCD axion dark matter. The sub-eV axion field oscillates coherently, inducing a time-varying effective $\bar{\theta}_{\rm QCD}$ angle. This, in turn, modulates the nuclear binding energy. From existing linewidth measurements, we derive constraints on the $f_a^{-1}$-$m_a$ plane that already surpass other laboratory bounds. We further detail an experimental setup to directly probe this time-dependent signature via precision M\"ossbauer spectroscopy in the gravitational potential. This Letter demonstrates that this approach can significantly extend search capability and probe a vast, unexplored region of axion parameter space. Particularly, this setup can probe axion masses beyond the reach of existing experiments, such as atomic-clock measurements, offering a powerful new way for exploring higher-mass axion dark matter. The sensitivity has the potential to be further improved with advancing experimental capabilities.

hep-ph

DPGP: A Hybrid 2D-3D Dual Path Potential Ghost Probe Zone Prediction Framework for Safe Autonomous Driving

Modern robots must coexist with humans in dense urban environments. A key challenge is the ghost probe problem, where pedestrians or objects unexpectedly rush into traffic paths. This issue affects both autonomous vehicles and human drivers. Existing works propose vehicle-to-everything (V2X) strategies and non-line-of-sight (NLOS) imaging for ghost probe zone detection. However, most require high computational power or specialized hardware, limiting real-world feasibility. Additionally, many methods do not explicitly address this issue. To tackle this, we propose DPGP, a hybrid 2D-3D fusion framework for ghost probe zone prediction using only a monocular camera during training and inference. With unsupervised depth prediction, we observe ghost probe zones align with depth discontinuities, but different depth representations offer varying robustness. To exploit this, we fuse multiple feature embeddings to improve prediction. To validate our approach, we created a 12K-image dataset annotated with ghost probe zones, carefully sourced and cross-checked for accuracy. Experimental results show our framework outperforms existing methods while remaining cost-effective. To our knowledge, this is the first work extending ghost probe zone prediction beyond vehicles, addressing diverse non-vehicle objects. We will open-source our code and dataset for community benefit.

cs.RO