arXiv subjects

Zhan-He Wang

Publications and source records attributed to Zhan-He Wang.

5 recordsLinked to original sources

arXiv2026

Constraining supermassive primordial black hole clustering with the angular auto-correlation of $z\simeq 6$ quasars

High-redshift quasars provide a direct probe of the origin and environment of the earliest supermassive black holes. We use their angular auto-correlation function at $z\simeq 6$ to test scenarios in which supermassive primordial black holes (SMPBHs) are associated with the observed quasar population. The evolved PBH correlation functions, for both Poisson fluctuations and initial PBH clustering, are projected over the quasar redshift window and compared with the measured angular correlation function using Markov chain Monte Carlo inference. It is observed that for the Poisson model, the posterior favors a small abundance, $f_{\rm PBH}\sim 10^{-3}$, and a supermassive effective mass scale, $m_{\rm PBH}\sim 10^{12}M_\odot$, interpreted here as a scale controlling quasar host-halo formation and clustering, and for the initially clustered model, the data prefer an effective clustering amplitude $ξ_{\rm eff}\simeq 2.1$ and a top-hat boundary scale $r_{\rm cl}\simeq 76\,{\rm Mpc}$, corresponding to weak relative contraction of PBH pairs in comoving coordinates.

astro-ph.CO↗

arXiv2026

Search for primordial black holes from gravitational wave populations using deep learning

Gravitational waves (GWs) signals detected by the LIGO/Virgo/KAGRA collaboration might be sourced (partly) by the merges of primordial black holes (PBHs). The conventional hierarchical Bayesian inference methods can allow us to study population properties of GW events to search for the hints for PBHs. However, hierarchical Bayesian analysis require an analytic population model, and becomes increasingly computationally expensive as the number of sources grows. In this paper, we present a novel population analysis method based on deep learning, which enables the direct and efficient estimation of PBH population hyperparameters, such as the PBH fraction in dark matter, $f_{\rm PBH}$. Our approach leverages neural posterior estimation combined with conditional normalizing flows and two embedding networks. Our results demonstrate that inference can be performed within seconds, highlighting the promise of deep learning as a powerful tool for population inference with an increasing number of GW signals for next-generation detectors.

gr-qc↗

arXiv2026

Testing the wormhole echo hypothesis for GW231123

The short-duration gravitational-wave (GW) event GW231123 has inferred component masses in the pair-instability mass gap and exhibits a burst-like morphology with no clearly inspiral, making it an interesting target for tests beyond the standard binary black hole (BBH) interpretation. In this work, motivated by its phenomenological similarity to GW190521, we test whether GW231123 is compatible with a wormhole-echo scenario by modeling a leading echo pulse with a well-motivated phenomenological sine-Gaussian wavepacket. We perform Bayesian model comparison against a BBH baseline described by the IMRPhenomXPHM-SpinTaylor waveform, and obtain the Bayes factor ratio $\ln B^{\rm Echo}_{\rm BBH} = 1.87$, corresponding to weak-to-moderate support for the echo hypothesis. In our previous analysis for GW190521 within the same overall framework, we found $\ln B^{\rm Echo}_{\rm BBH} \approx -2.9$, implying a shift of $Δ\ln B \approx 4.8$ between the two events. This sign change indicates that GW231123 is more compatible with a single-pulse echo description than GW190521.

gr-qc↗

arXiv2025

News-Aware Direct Reinforcement Trading for Financial Markets

The financial market is known to be highly sensitive to news. Therefore, effectively incorporating news data into quantitative trading remains an important challenge. Existing approaches typically rely on manually designed rules and/or handcrafted features. In this work, we directly use the news sentiment scores derived from large language models, together with raw price and volume data, as observable inputs for reinforcement learning. These inputs are processed by sequence models such as recurrent neural networks or Transformers to make end-to-end trading decisions. We conduct experiments using the cryptocurrency market as an example and evaluate two representative reinforcement learning algorithms, namely Double Deep Q-Network (DDQN) and Group Relative Policy Optimization (GRPO). The results demonstrate that our news-aware approach, which does not depend on handcrafted features or manually designed rules, can achieve performance superior to market benchmarks. We further highlight the critical role of time-series information in this process.

q-fin.CP↗

arXiv2025

Broad primordial power spectrum and $μ$-distortion constraints on primordial black holes

Supermassive black holes (SMBHs) might originate from supermassive primordial black holes (PBHs). However, the hypothesis that these PBHs formed through the enhancement of the primordial curvature perturbations has consistently faced significant challenges due to the stringent constraints imposed by $μ$-distortion in the cosmic microwave background (CMB). In this work, we investigate the impact of non-Gaussianity on $μ$-distortion constraint in the context of broad power spectra. Our results show that, under the assumption of non-Gaussian curvature perturbations, a broad power spectrum may lead to weaker $μ$-distortion constraints compared to the Gaussian cases. Our findings highlight the potential of the broad power spectrum to alleviate the $μ$-distortion constraints on supermassive PBHs under large non-Gaussianity.

astro-ph.CO↗