9 citations · 24 across the 18 of their papers we have counts for
18 papers
Improved Cosmological Constraints from Morphology-Based Marked Correlation Functions
Xu Xiao, Zhao Chen, Yu Yu +2
The cosmic web contains morphology-dependent information that is not fully captured by standard two-point statistics. We construct morphology-based marked correlation functions (MC…
Data-driven modeling of Galactic diffuse emission with multi-wavelength observations
Xi Liu, Xiaodong Li, Sujie Lin +4
We present a data-driven investigation of Galactic diffuse emission. Using multi-frequency Planck radio/microwave maps (30-857 GHz) and Fermi-LAT gamma-ray data (50 MeV-814 GeV), w…
Cosmological constraints from neighbor-density-weighted marked correlation functions
Xu Xiao, Zhao Chen, Yu Yu +3
We investigate whether neighbor-density-weighted marked correlation functions (MCFs) can extract cosmological information beyond the standard redshift-space two-point correlation f…
Cosmological Constraints from Bias-Robust Wavelet Scattering Statistics for Stage-IV Galaxy Surveys
Zhujun Jiang, Xu Xiao, Zhiwei Min +5
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…
Physics-Informed Generative Solver: Bridging Data-Driven Priors and Conservation Laws for Stable Spatiotemporal Field Reconstruction
Ziyuan Zhu, Keyu Hu, Zhifei Chen +10
Reconstructing continuous physical fields from sparse measurements is a central inverse problem, but data-driven generative models can produce states that violate governing dynamic…
Into the Gompverse: A robust Gompertzian reionization model for CMB analyses
Paulo Montero-Camacho, Yin Li, Marco Barquero-Hernández +3
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 pa…
Extracting Cosmological Information from Lightcone Data: A Comparison of CNNs and Summary-Statistic-Based Approaches
Min Zhiwei, Xiao Xu, Jiang Zhujun +9
Lightcone observations are the natural data format of galaxy surveys, but their evolving geometry breaks the translational symmetry assumed by standard convolutional neural network…
Enhancing cosmological constraints with nonlinear tanh transformations of Hermite-Gaussian Derivative fields
Zhiwei Min, Ye Ma, Zhujun Jiang +4
A key goal in large-scale structure analysis is to extract multi-scale information to improve cosmological parameter constraints. In particular, higher-order derivative fields are…
Enhancing Cosmological Constraints by Two-dimensional -cosmic-web Weighted Angular Correlation Functions
Fenfen Yin, Liang Xiao, Wenying Du +6
In this study, we investigate the potential of mark-weighted angular correlation functions (MACFs), which integrate -cosmic-web classification with angular correlation function…
A New Wavelet Scattering Transform-Based Statistic for Cosmological Analysis of Large-Scale Structure
Zhujun Jiang, Xiaolin Luo, Wenying Du +6
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…
Tomographic Alcock-Paczynski Test with Marked Correlation Functions
Liang Xiao, Limin Lai, Zhujun Jiang +2
The tomographic Alcock-Paczynski(AP) method, developed over the past decade, exploits redshift evolution for cosmological determination, aiming to mitigate contamination from redsh…
AI-Driven Reconstruction of Large-Scale Structure from Combined Photometric and Spectroscopic Surveys
Wenying Du, Xiaolin Luo, Zhujun Jiang +7
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-$…
Testing the Equivalence Principle on Cosmological Scales Using Peculiar Acceleration Power Spectrum
Guoyuan Lu, Yi Zheng, Le Zhang +3
While the (weak) Equivalence Principle (EP) has been rigorously tested within the solar system, its validity on cosmological scales, particularly in the context of dark matter and…
AI-Powered Reconstruction of Dark Matter Velocity Fields from Redshift-Space Halo Distribution
Xu Xiao, Jiacheng Ding, XiaoLin Luo +6
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 sta…
Deep learning for cosmological parameter inference from a dark matter halo density field
Zhiwei Min, Xu Xiao, Jiacheng Ding +11
We propose a lightweight deep convolutional neural network (lCNN) to estimate cosmological parameters from simulated three-dimensional dark matter (DM) halo distributions and assoc…
Improving SDSS cosmological constraints through -skeleton weighted correlation functions
Fenfen Yin, Jiacheng Ding, Limin Lai +6
The -skeleton approach can be conveniently utilized to construct the cosmic web based on the spatial geometry distribution of galaxies, particularly in sparse samples. This meth…
Improving Constraint on from SDSS Using Marked Correlation Functions
L. M. Lai, J. C. Ding, X. L. Luo +9
Large-scale structure (LSS) surveys will increasingly provide stringent constraints on our cosmological models. Recently, the density-marked correlation function (MCF) has been int…
Fast generation of mock galaxy catalogues with COLA
Jiacheng Ding, Shaohong Li, Yi Zheng +3
We investigate the feasibility of using COmoving Lagrangian Acceleration (COLA) technique to efficiently generate galaxy mock catalogues that can accurately reproduce the statistic…