arXiv subjects

Xiao-Dong Li

Publications and source records attributed to Xiao-Dong Li.

At least 19 recordsLinked to original sources

arXiv2026

Improved Cosmological Constraints from Morphology-Based Marked Correlation Functions

The cosmic web contains morphology-dependent information that is not fully captured by standard two-point statistics. We construct morphology-based marked correlation functions (MCFs) by assigning marks to halos according to the cosmic-web morphology identified with the \textsc{Nexus} algorithm. Using the \textsc{Kun} simulation suite, which spans 129 $w_0w_a$CDM cosmologies, we build Gaussian-process emulators for the MCFs as functions of cosmological parameters and tracer bias. We then apply the emulators to mock halo catalogues from the independent \textsc{Jiutian} simulation and perform a joint likelihood analysis to quantify the resulting cosmological constraints. We consider two marker choices: a discrete morphology marker and a continuous morphology strength marker. The continuous marker improves the Figure of Merit (FoM) by a factor of $\sim 8.6$ relative to the standard 2PCF and reduces the $1σ$ uncertainty on $σ_8$ by a factor of $\sim 5$. The discrete marker gives a more modest FoM improvement of $\sim 17\%$. We further test the impact of tracer selection by varying the halo mass threshold by a factor of $\sim 4.5$. Even for the lowest mass threshold, the continuous marker remains unbiased and achieves a FoM about $\sim 3.4$ times higher than that of the 2PCF alone. These results show that morphology-based MCFs, combined with simulation-based emulation, provide a useful framework for extracting additional cosmological information from large-scale structure surveys.

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arXiv2026

Cosmological constraints from neighbor-density-weighted marked correlation functions

We investigate whether neighbor-density-weighted marked correlation functions (MCFs) can extract cosmological information beyond the standard redshift-space two-point correlation function (2PCF). Using the Kun suite of 129 $w_0w_a$CDM$+\sum m_ν$ simulations in $1~h^{-1}{\rm Gpc}$ boxes, we construct Gaussian-process emulators for the normalized scale statistic $\widehat{W}^α(s)$ and the angular statistic $\widehat{W}^α_{Δs}(μ)$. We perform joint analyses combining multiple mark parameters $α$ and quantify the information gain using the FoM in the $Ω_m$--$σ_8$ plane. Relative to the 2PCF case, three-mark combinations improve the FoM by factors of $1.7$--$2.5$, while five-mark combinations increase the gain to $1.9$--$2.4$, depending on the statistic and mark definition. We further compare density and normalized-gradient marks, finding that they are nearly redundant for isotropic statistics but complementary for angular statistics, where their combination improves the FoM by up to $43\%$. Tests of scale range and halo selection show that the marked statistics remain robust under changes in analysis choices, with the angular statistic retaining additional cosmological information that is less sensitive to tracer selection. Our results demonstrate that MCFs substantially enhance cosmological constraints beyond the standard 2PCF and provide a robust probe for next-generation galaxy surveys.

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arXiv2026

Cosmological Constraints from Bias-Robust Wavelet Scattering Statistics for Stage-IV Galaxy Surveys

A central challenge in precision cosmology with galaxy surveys is to extract non-Gaussian information from large-scale structure while controlling systematic uncertainties such as tracer bias. Conventional clustering statistics, such as the two-point correlation function (2PCF), capture limited nonlinear information and typically require explicit bias modeling, which can introduce systematic errors if the adopted bias prescription is inaccurate. To address this problem, we introduce $R^{\rm wst}$, a bias-robust statistic constructed from $m$-mode ratios of the wavelet scattering transform (WST). Using simulation-based inference, we train a Gaussian-process-regression emulator on the \texttt{Kun} simulation suite and use \texttt{JiuTian} simulations for covariance estimation and validation. The emulator achieves percent-level accuracy, sufficient for the expected observational uncertainties. We show that $R^{\rm wst}$ yields unbiased constraints on $Ω_m$, $σ_8$, $n_s$, and $w_0$, and improves the breaking of the $Ω_m$--$σ_8$ degeneracy by about a factor of two compared with 2PCF. Its constraining power remains stable across a broad range of tracer-bias scenarios, demonstrating that $R^{\rm wst}$ can mitigate bias-induced systematics without explicit bias modeling. These results establish $R^{\rm wst}$ as a powerful and robust statistic for precision cosmology with Stage-IV surveys.

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arXiv2026

MUltiplexed Survey Telescope (MUST) Science White Paper I: Overview of Large-Scale Structure Cosmology in the Era of Stage-V Spectroscopic Surveys

The MUltiplexed Survey Telescope (MUST) is a 6.5-meter telescope under development. Dedicated to highly-multiplexed, wide-field spectroscopic surveys, MUST observes over 20,000 targets simultaneously using 6.2-mm pitch positioning robots within a ~5 deg$^2$ field of view. MUST aims to conduct the first Stage-V spectroscopic survey in the 2030s, mapping the 3D Universe with over 100 million galaxies and quasars, spanning from the nearby Universe to a redshift of z ~ 5.5, corresponding to approximately 1 billion years after the Big Bang. To cover this extensive redshift range, we present an initial conceptual target selection algorithm for different types of galaxies, ranging from local bright galaxies and luminous red galaxies to emission-line galaxies, and high-redshift (2 < z < 5.5) Lyman-break galaxies. Using Fisher forecasts, we demonstrate that MUST can address fundamental questions in cosmology, including the nature of dark energy, tests of gravity theories, and investigations into primordial physics. This is the first paper in the series of science white papers for MUST, with subsequent developments focusing on additional scientific cases such as galaxy and quasar evolution, Milky Way physics, and dynamic phenomena in the time-domain Universe.

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arXiv2026

Tomographic Alcock-Paczynski Test with Marked Correlation Functions

The tomographic Alcock-Paczynski(AP) method, developed over the past decade, exploits redshift evolution for cosmological determination, aiming to mitigate contamination from redshift distortions and capture nonlinear scale information. Marked Correlation Functions (MCFs) extend information beyond the two-point correlation. For the first time, this study integrated the tomographic AP test with MCFs to constrain the flat $w$CDM cosmology model. Our findings show that multiple density weights in MCFs outperform the traditional two-point correlation function, reducing the uncertainties of the matter density parameter $Ω_m$ and dark energy equation of state $w$ by 48\% and 45\%, respectively. Furthermore, we introduce a novel principal component analysis (PCA) compression scheme that efficiently projects high-dimensional statistical measurements into a compact set of eigenmodes while preserving most of the cosmological information. This approach retains significantly more information than traditional coarse binning methods, which simply average adjacent bins in a lossy manner, yielding an additional $\sim 40\%$ reduction in error margins. To assess robustness, we incorporate realistic redshift errors expected in future spectroscopic surveys. While these errors modestly degrade cosmological constraints, our combined framework, which utilizes MCFs and PCA compression within tomographic AP tests, is less affected and always yields tight cosmological constraints. This scheme remains highly promising for upcoming slitless spectroscopic surveys, such as the Chinese Space Station Telescope (CSST).

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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.

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arXiv2026

Into the Gompverse: A robust Gompertzian reionization model for CMB analyses

Cosmic reionization is driven by the formation of sources of ultraviolet photons, and hence it is an intrinsically asymmetric process, where its earlier stages occur at a slower pace relative to its later stages. Yet most modern cosmic microwave background (CMB) analyses rely on a hyperbolic tangent template, i.e. a symmetric sigmoid, that is not well suited for joint fitting of CMB and reionization observations. In this work, we introduce a physically motivated Gompertzian reionization model with three astrophysical (nuisance) parameters, designed to enable joint analyses of CMB and reionization data and to be applicable to a wide range of datasets and cosmological models. This robust Gompertzian model leverages the connection between cosmology and reionization, typically ignored in standard CMB analyses, to demote the optical depth ($τ_{\rm reio}$) to derived parameter, reducing its uncertainty by approximately a factor of three compared to the conventional $\tanh$ prescription. The $τ_{\rm reio}$ improvement enables tighter constraints on the sum of the neutrino masses, revealing potential tension with neutrino oscillation experiments even after accounting for the known relaxation of neutrino mass bounds in $w_0w_a$CDM models -- a tension that is partially obscured by the conventional treatment of reionization. In addition, the inferred constraints on the astrophysical parameters governing reionization naturally synergize with current and upcoming 21 cm experiments, providing physically informed parameter ranges for future 21 cm studies.

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arXiv2025

A New Wavelet Scattering Transform-Based Statistic for Cosmological Analysis of Large-Scale Structure

Large-scale structure (LSS) analysis in galaxy surveys is a powerful cosmological probe but is limited by tracer bias, which can obscure underlying information and weaken parameter constraints. Existing methods either model bias or restrict analyses to low-density regions, yet their sensitivity to bias remains poorly understood. We propose a novel method based on the wavelet scattering transform (WST) to distinguish LSS across cosmological models while mitigating tracer bias. Central to our approach are the WST $m$-mode ratios, $R^{\rm wst}$, a new statistical measure, and a high-density apodization preprocessing that smoothly rescales extreme values. We use a reduced chi-square to assess the cosmological parameter constraints and find that $R^{\rm wst}$, in the scale range $j \in [3,7]$, achieves $χ^2_{ν, \rm cos} \approx 6$ for cosmology while maintaining $χ^2_{ν, \rm bias} \sim 1$--a regime unattained by other statistics. $R^{\rm wst}$ thus provides robust cosmological sensitivity with effective bias mitigation for future surveys.

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arXiv2025

AI-Driven Reconstruction of Large-Scale Structure from Combined Photometric and Spectroscopic Surveys

Galaxy surveys are crucial for studying large-scale structure (LSS) and cosmology, yet they face limitations--imaging surveys provide extensive sky coverage but suffer from photo-$z$ uncertainties, while spectroscopic surveys yield precise redshifts but are sample-limited. To take advantage of both photo-$z$ and spec-$z$ data while eliminating photo-$z$ errors, we propose a deep learning framework based on a dual UNet architecture that integrates these two datasets at the field level to reconstruct the 3D photo-$z$ density field. We train the network on mock samples representative of stage-IV spectroscopic surveys, utilizing CosmicGrowth simulations with a $z=0.59$ snapshot containing $2048^3$ particles in a $(1200~h^{-1}\rm Mpc)^3$ volume. Several metrics, including correlation coefficient, MAE, MSE, PSNR, and SSIM, validate the model's accuracy. Moreover, the reconstructed power spectrum closely matches the ground truth at small scales ($k \gtrsim 0.06~h/\rm Mpc$) within the $1σ$ confidence level, while the UNet model significantly improves the estimation of photo-$z$ power spectrum multipoles. This study demonstrates the potential of deep learning to enhance LSS reconstruction by using both spectroscopic and photometric data.

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arXiv2025

AI-Powered Reconstruction of Dark Matter Velocity Fields from Redshift-Space Halo Distribution

We propose a UNet-based deep learning model to reconstruct the real-space dark matter (DM) velocity field from the redshift-space distribution of sparse DM halos. Using various statistical measures, we show that the reconstructed velocity components--including velocity magnitude, momentum, and divergence--closely match the ground truth, achieving better than 10% relative error and a correlation coefficient of 0.88. In the power spectrum comparison over $k \in [0.05, 0.3] h/{\rm Mpc}$, the UNet reconstruction outperforms linear theory and agrees with the true field within $2σ$. The model also effectively corrects redshift-space distortions (RSD), yielding unbiased power spectrum multipoles of DM fields within $2σ$. Notably, the UNet remains robust even with incomplete halo mass information. These results highlight the model's broad applicability to cosmological analyses, including RSD, cosmic web studies, the kinetic Sunyaev-Zel'dovich effect, and BAO reconstruction.

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arXiv2025

Cosmic Web Classification through Stochastic Topological Ranking

This paper introduces ASTRA (Algorithm for Stochastic Topological RAnking), a new method for classifying galaxies into cosmic web structures -- voids, sheets, filaments, and knots -- specifically designed for large spectroscopic surveys. ASTRA operates on observed galaxy positions and a corresponding random catalog, generating probabilistic cosmic web classifications for both datasets. The method's key innovation lies in using random points to trace underdense regions, enabling robust identification of cosmic voids that are poorly sampled by galaxies. We evaluate ASTRA using N-body simulations (dark matter-only and hydrodynamical) and SDSS observational data, performing both visual inspections and quantitative analyses of mass and volume distributions. The algorithm successfully produces void catalogs with size functions following theoretical expectations and demonstrates consistent environmental statistics across diverse datasets. Comparative analysis against established cosmic web classifiers confirms ASTRA's effectiveness, particularly for filament identification. By incorporating both observed and random points in its classification scheme, ASTRA provides a full cosmic web characterization without requiring density field interpolation or fixed geometric assumptions. The method's ability to quantify spatial correlations among different cosmic web components offers promising avenues for enhancing cosmological parameter constraints through non-standard clustering statistics.

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arXiv2025

Enhancing Cosmological Constraints by Two-dimensional $β$-cosmic-web Weighted Angular Correlation Functions

In this study, we investigate the potential of mark-weighted angular correlation functions (MACFs), which integrate $β$-cosmic-web classification with angular correlation function analysis to improve cosmological constraints. Using SDSS DR12 CMASS-NGC galaxies and mock catalogs with $Ω_m$ varying from 0.25 to 0.40, we assess the discriminative power of different statistics via the average improvement in chi-squared, $Δ\overline{χ^2}$, across six redshift bins. This metric quantifies how effectively each statistic distinguishes between different cosmological models. Incorporating cosmic-web weights leads to substantial improvements. Using statistics weighted by the mean neighbor distance ($\bar{D}_{\rm nei}$) increases $Δ\overline{χ^2}$ by approximately 40%-130%, while applying inverse mean neighbor distance weighting ($1/\bar{D}_{\rm nei}$) yields even larger gains, boosting $Δ\overline{χ^2}$ by a factor of 2-3 compared to traditional unweighted angular statistics. These enhancements are consistent with previous 3D clustering results, demonstrating the superior sensitivity of the $β$-weighted approaches. Our method, based on thin redshift slices, is particularly suited for slitless surveys (e.g., Euclid, CSST) where redshift uncertainties limit 3D analyses. This study also offers a framework for applying marked statistics to 2D angular clustering.

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arXiv2025

Recovering Cosmic Structure with a Simple Physical Constraint

Radio observation of the large-scale structure (LSS) of our Universe faces major challenges from foreground contamination, which is many orders of magnitude stronger than the cosmic signal. While other foreground removal techniques struggle with complex systematics, methods like foreground avoidance emerge as effective alternatives. However, this approach inevitably results in the loss of Fourier modes and a reduction in cosmological constraints. We present a novel method that, by enforcing the non-negativity of the observed field in real space, allows us to recover some of the lost information, particularly phase angles. We demonstrate that the effectiveness of this straightforward yet powerful technique arises from the mode mixing from the non-linear evolution of LSS. Since the non-negativity is ensured by mass conservation, one of the key principles of the cosmic dynamics, we can restore the lost modes without explicitly expressing the exact form of the mode mixing. Unlike previous methods, our approach utilizes information from highly non-linear scales, and has the potential to revolutionize the analysis of radio observational data in cosmology. Crucially, we demonstrate that in long-baseline interferometric observations, such as those from the Square Kilometre Array (SKA), it is still possible to recover the baryonic acoustic oscillation (BAO) signature despite not directly covering the relevant scales. This opens up potential future survey designs for cosmological detection.

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arXiv2024

Deep learning for cosmological parameter inference from a dark matter halo density field

We propose a lightweight deep convolutional neural network (lCNN) to estimate cosmological parameters from simulated three-dimensional dark matter (DM) halo distributions and associated statistics. The training dataset comprises 2000 realizations of a cubic box with a side length of 1000 $h^{-1}{\rm Mpc}$, and interpolated over a cubic grid of $300^3$ voxels, with each simulation produced using $512^3$ DM particles and $512^3$ neutrinos. Under the flat $Λ$CDM model, simulations vary standard six cosmological parameters including $Ω_m$, $Ω_b$, $h$, $n_s$, $σ_8$, $w$, along with the neutrino mass sum, $M_ν$. We find that: 1) within the framework of lCNN, extracting large-scale structure information is more efficient from the halo density field compared to relying on the statistical quantities including the power spectrum, the two-point correlation function, and the coefficients from wavelet scattering transform; 2) combining the halo density field with its Fourier transformed counterpart enhances predictions, while augmenting the training dataset with measured statistics further improves performance; 3) achieving high accuracy in inferring $Ω_m$, $h$, and $σ_8$ by the neural network model, while being inefficient in predicting $Ω_b$, { $n_s$}, $M_ν$ and $w$; 4) { compared to the simple fully connected network trained with three statistical quantities, our CNN yields statistically reduced errors, showing improvements of approximately 23\% for $Ω_m$, 11\% for $h$, 8\% for $n_s$, and 21\% for $σ_8$. Additionally, in comparison with the likelihood-based analysis on $P(k)$ data, our CNN provides much tighter constraints on parameters, especially on $Ω_m$ and $σ_8$.} Our study emphasizes this lCNN-based novel approach in extracting large-scale structure information and estimating cosmological parameters.

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arXiv2024

Correlation-based Beam Calibration of 21cm Intensity Mapping

Foreground removal presents a significant obstacle in both current and forthcoming intensity mapping surveys. While numerous techniques have been developed that show promise in simulated datasets, their efficacy often diminishes when applied to real-world data. A primary issue is the frequency-dependent variations in the instrumental response. In this paper, we propose a novel approach utilizing the internal cross-correlation among different frequencies to calibrate the beam's frequency fluctuations. Using a simulated dataset that incorporates frequency-dependent random fluctuations into the beam model, we illustrate that our method can achieve considerable improvements over traditional techniques. Our results represent a step forward in enhancing the precision and reliability of foreground removal in intensity mapping surveys.

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arXiv2024

Improving SDSS cosmological constraints through $β$-skeleton weighted correlation functions

The $β$-skeleton approach can be conveniently utilized to construct the cosmic web based on the spatial geometry distribution of galaxies, particularly in sparse samples. This method plays a key role in establishing the three-dimensional structure of the Universe and serves as a tool for quantitatively characterizing the nature of the cosmic web. This study is the first application of $β$-skeleton information as weights in mark weighted correlation functions (MCFs), presenting a novel statistical measure. We have applied the $β$-skeleton approach to the CMASS NGC galaxy samples from SDSS BOSS DR12 in the redshift interval $0.45 \leq z \leq 0.55$. Additionally, we applied this approach to three COLA cosmological simulations with different settings ($Ω_m=0.25, Ω_m=0.31, Ω_m=0.4$) for comparison. We measured three MCFs, each weighted by i) the number of neighboring galaxies around each galaxy, ii) the average distance of each galaxy from its surrounding neighbors, and iii) the reciprocal of the average distance of each galaxy from its surrounding neighbors. By comparing measurements and calculating corresponding $χ^2$ statistics, we observe high sensitivity to the cosmological parameter $Ω_m$ through a joint analysis of the two-point correlation and three MCFs.

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arXiv2024

Forecasting the BAO Measurements of the CSST galaxy and AGN Spectroscopic Surveys

The spectroscopic survey of China's Space Survey Telescope (CSST) is expected to obtain a huge number of slitless spectra, including more than one hundred million galaxy spectra and millions of active galactic nuclei (AGN) spectra. By making use of these spectra, we can measure the Baryon Acoustic Oscillation (BAO) signals over large redshift ranges with excellent precisions. In this work, we predict the CSST measurements of the post-reconstruction galaxy power spectra at $0<z<1.2$ and pre-reconstruction AGN power spectra at $0<z<4$, and derive the BAO signals at different redshift bins by constraining the BAO scaling parameters using the Markov Chain Monte Carlo method. Our result shows that the CSST spectroscopic survey can provide accurate BAO measurements with precisions higher than 1\% and 3\% for the galaxy and AGN surveys, respectively. By comparing with current measurements in the same range at low redshifts, this can improve the precisions by a factor of $2\sim3$, and similar precisions can be obtained in the pessimistic case. We also investigate the constraints on the cosmological parameters using the measured BAO data by the CSST, and obtain stringent constraint results for the energy density of dark matter, Hubble constant, and equation of state of dark energy.

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arXiv2023

Fast generation of mock galaxy catalogues with COLA

We investigate the feasibility of using COmoving Lagrangian Acceleration (COLA) technique to efficiently generate galaxy mock catalogues that can accurately reproduce the statistical properties of observed galaxies. Our proposed scheme combines the subhalo abundance matching (SHAM) procedure with COLA simulations, utilizing only three free parameters: the scatter magnitude ($σ_{\rm scat}$) in SHAM, the initial redshift ($z_{\rm init}$) of the COLA simulation, and the time stride ($da$) used by COLA. In this proof-of-concept study, we focus on a subset of BOSS CMASS NGC galaxies within the redshift range $z\in [0.45, 0.55]$. We perform $\mathtt{GADGET}$ simulation and low-resolution COLA simulations with various combinations of $(z_{\rm init}, da)$, each using $1024^{3}$ particles in an $800~h^{-1}{\rm Mpc}$ box. By minimizing the difference between COLA mock and CMASS NGC galaxies for the monopole of the two-point correlation function (2PCF), we obtain the optimal $σ_{\rm scat}$. We have found that by setting $z_{\rm init}=29$ and $da=1/30$, we achieve a good agreement between COLA mock and CMASS NGC galaxies within the range of 4 to $20~h^{-1}{\rm Mpc}$, with a computational cost two orders of magnitude lower than that of the N-body code. Moreover, a detailed verification is performed by comparing various statistical properties, such as anisotropic 2PCF, three-point clustering, and power spectrum multipoles, which shows similar performance between GADGET mock and COLA mock catalogues with the CMASS NGC galaxies. Furthermore, we assess the robustness of the COLA mock catalogues across different cosmological models, demonstrating consistent results in the resulting 2PCFs. Our findings suggest that COLA simulations are a promising tool for efficiently generating mock catalogues for emulators and machine learning analyses in exploring the large-scale structure of the Universe.

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