arXiv subjects

Ziyong Wu

Publications and source records attributed to Ziyong Wu.

8 recordsLinked to original sources

arXiv2026

Redshift Evolution of the Ratio of Supermassive Black Hole Mass to Stellar Mass

We run and analyze a suite of high-redshift zoom-in cosmological simulations with varying supernova feedback and supermassive black hole (SMBH) accretion prescriptions to study the joint evolution of stellar and SMBH mass in high-redshift galaxies down to $z=10$. The simulations reproduce the observed high-$z$ $M_{\mathrm{BH}}/M_{\star}$ relation if super-Eddington accretion is allowed prior to the final self-regulated phase. To extend the evolution to lower redshift, we model subsequent black hole and host growth using analytic halo assembly histories combined with a redshift-dependent effective Eddington duty cycle, $f_{\rm duty}=0.0004(1+z)^3$, calibrated to observations at $z\le6$, with conservative uncertainties at higher redshift. Within this framework, $M_{\mathrm{BH}}/M_{\star}$ exhibits a broad peak at $z\sim7$--10, reaching a few percent up to $\sim30\%$, followed by a steady, approximately power-law decline toward $z=0$. The model predicts $M_{\mathrm{BH}}/M_{\star}\sim(0.002,0.003,0.006,0.016,0.071,0.156)$ at $z=(0,1,2,3,5,10)$, consistent with available observations. This evolution is driven by rapid SMBH growth at high redshift, with effective mass e-folding times shorter than those of stellar mass, while at later times galaxy growth dominates, leading to the decline in $M_{\mathrm{BH}}/M_{\star}$. These results demonstrate that the emergence of a high-redshift peak and subsequent decline is robust despite uncertainties in the duty-cycle normalization.

astro-ph.GA↗

arXiv2026

Closing the Observational Gap in Cosmic Dynamics: AI-Enabled Reconstruction of the Universe's Vorticity and Rotational Flow Morphology

The cosmic vorticity field, an essential tracer of nonlinear structure formation, has remained observationally inaccessible because transverse galaxy motions are difficult to measure and analytic models struggle to capture shell-crossing. Here we report an empirical reconstruction of this field by applying an artificial intelligence framework trained on simulations of the concordance LambdaCDM model to Sloan Digital Sky Survey galaxies. The recovered three-dimensional velocity and vorticity fields reveal coherent vortical structures, including spiral flows in clusters, filaments, and voids, and the cosmic web inferred from vorticity closely matches that derived from density segmentation. The power spectra of the reconstructed velocity and vorticity fields agree statistically with LambdaCDM predictions, and the inferred velocity field effectively removes redshift-space distortions, yielding an almost isotropic clustering signal. These converging lines of evidence, obtained from an independent perspective, reinforce the concordance cosmological model. By closing a long-standing observational gap, our results highlight the potential of AI-driven reconstruction to access otherwise unobservable quantities and to address fundamental questions in cosmology and galaxy formation.

astro-ph.CO↗

arXiv2025

How Fast Could Supermassive Black Holes Grow At the Epoch of Reionization?

Utilizing cosmological hydrodynamic simulations we show that there is a brief super-Eddington accretion phase in typical halos at high redshift, impervious to AGN self-regulation. However, once having attained a black hole mass of $10^4-10^5\msun$, AGN feedback process can self-regulate to guide the SMBHs to grow at a significantly slower, sub-Eddington rate. By redshift $z\sim 10$ the black hole mass with an initial super-Eddington jump-start is caught up by that in the case with a steady Eddington limited case. Thus a continuous Eddington limit case represents the fastest possible route to maximally grow SMBHs. To account for the observed $z=7-10$ quasars with supermassive black holes of billions of solar masses, our analysis establishes firmer ground for the need of seed masses of $10^4-10^5\msun$ that are not grown via an earlier super-Eddington phase.

astro-ph.GA↗

arXiv2025

Fresh2comm: Information Freshness Optimized Collaborative Perception

Collaborative perception is a cornerstone of intelligent connected vehicles, enabling them to share and integrate sensory data to enhance situational awareness. However, measuring the impact of high transmission delay and inconsistent delay on collaborative perception in real communication scenarios, as well as improving the effectiveness of collaborative perception under such conditions, remain significant challenges in the field. To address these challenges, we incorporate the key factor of information freshness into the collaborative perception mechanism and develop a model that systematically measures and analyzes the impacts of real-world communication on collaborative perception performance. This provides a new perspective for accurately evaluating and optimizing collaborative perception performance. We propose and validate an Age of Information (AoI)-based optimization framework that strategically allocates communication resources to effectively control the system's AoI, thereby significantly enhancing the freshness of information transmission and the accuracy of perception. Additionally, we introduce a novel experimental approach that comprehensively assesses the varying impacts of different types of delay on perception results, offering valuable insights for perception performance optimization under real-world communication scenarios.

cs.MA↗

arXiv2024

Cosmological imprints in the filament with DisPerSE

In the regime of cosmology and large-scale structure formation, filaments are vital components of the cosmic web. This study employs statistical methods to examine the formation, evolution, and cosmological constraints of filaments identified by DisPerSe. We run large-sample of N-body simulations to study the filament length and its evolution. In general, the filament length distribution can be fitted by a power law with both the normalization and power index dependent on redshift and cosmological parameters. It is discovered that filament length distribution is influenced by various cosmological parameters, with $σ_8$ and $n_s$ exhibiting slightly stronger dependence than $Ω_m$. We also uncover a three-stage filament formation process from $z \sim 3$ to $z \sim 1$: rapid formation of both long and short filaments from $z \sim 3$ to $z \sim 2$, persistence of long filaments from $z \sim 2$ to $z \sim 1$, followed by fragmentation and increased prevalence of shorter filaments below $z \sim 1$. Finally, we employ initial power spectrum fluctuations to elucidate the cosmological dependence on the filament length function. These insights enhance our understanding of filament evolution and their cosmological relevance and also highlight the potential cosmological applications in observations.

astro-ph.CO↗

arXiv2023

AI-assisted reconstruction of cosmic velocity field from redshift-space spatial distribution of halos

The peculiar velocities of dark matter halos are crucial to study many issues in cosmology and galaxy evolution. In this study, by using the state-of-the-art deep learning technique, a UNet-based neural network, we propose to reconstruct the peculiar velocity field from the redshift-space distribution of dark matter halos. Through a point-to-point comparison and examination of various statistical properties, we demonstrate that, the reconstructed velocity field is in good agreement with the ground truth. The power spectra of various velocity field components, including velocity magnitude, divergence and vorticity, can be successfully recovered when $k\lesssim 1.1$ $h/\rm Mpc$ (the Nyquist frequency of the simulations) at about 80% accuracy. This approach is very promising and presents an alternative method to correct the redshift-space distortions using the measured 3D spatial information of halos. Additionally, for the reconstruction of the momentum field of halos, UNet achieves similar good results. Hence the applications in various aspects of cosmology are very broad, such as correcting redshift errors and improving measurements in the structure of the cosmic web, the kinetic Sunyaev-Zel'dovich effect, BAO reconstruction, etc.

astro-ph.CO↗

arXiv2021

Cosmic Velocity Field Reconstruction Using AI

We develop a deep learning technique to infer the non-linear velocity field from the dark matter density field. The deep learning architecture we use is an "U-net" style convolutional neural network, which consists of 15 convolution layers and 2 deconvolution layers. This setup maps the 3-dimensional density field of $32^3$-voxels to the 3-dimensional velocity or momentum fields of $20^3$-voxels. Through the analysis of the dark matter simulation with a resolution of $2 {h^{-1}}{\rm Mpc}$, we find that the network can predict the the non-linearity, complexity and vorticity of the velocity and momentum fields, as well as the power spectra of their value, divergence and vorticity and its prediction accuracy reaches the range of $k\simeq1.4$ $h{\rm Mpc}^{-1}$ with a relative error ranging from 1% to $\lesssim$10%. A simple comparison shows that neural networks may have an overwhelming advantage over perturbation theory in the reconstruction of velocity or momentum fields.

astro-ph.CO↗

arXiv2019

Cosmological constraints from the redshift dependence of the Alcock-Paczynski effect: Fourier space analysis

The tomographic Alcock-Paczynski (AP) method utilizes the redshift evolution of the AP distortion to place constraints on cosmological parameters. It has proved to be a robust method that can separate the AP signature from the redshift space distortion (RSD) effect, and deliver powerful cosmological constraints using the $\lesssim 40h^{-1}\ \rm Mpc$ clustering region. In previous works, the tomographic AP method was performed via the anisotropic 2-point correlation function statistic. In this work we consider the feasibility of conducting the analysis in the Fourier domain and examine the pros and cons of this approach. We use the integrated galaxy power spectrum (PS) as a function of direction, $\hat P_{Δk}(μ)$, to quantify the magnitude of anisotropy in the large-scale structure clustering, and use its redshift variation to do the AP test. The method is tested on the large, high resolution Big-MultiDark Planck (BigMD) simulation at redshifts $z=0-1$, using the underlying true cosmology $Ω_m=0.3071,\ w=-1$. Testing the redshift evolution of $\hat P_{Δk}(μ)$ in the true cosmology and cosmologies deviating from the truth with $δΩ_m=0.1,\ δw=0.3$, we find that the redshift evolution of the AP distortion overwhelms the effects created by the RSD by a factor of $\sim1.7-3.6$. We test the method in the range of $k\in(0.2,1.8)\ h\ \rm Mpc^{-1}$, and find that it works well throughout the entire regime. We tune the halo mass within the range $2\times 10^{13}$ to $10^{14}\ M_{\odot}$, and find that the change of halo bias results in $\lesssim 5 \%$ change in $\hat P_{Δk}(μ)$, which is less significant compared with the cosmological effect. Our work shows that it is feasible to conduct the tomographic AP analysis in the Fourier space.

astro-ph.CO↗