arXiv subjects

Ji Yao

Publications and source records attributed to Ji Yao.

At least 19 recordsLinked to original sources

arXiv2026

Executable verification through formalized expert reasoning in astronomical spectroscopy

Artificial intelligence has reshaped scientific prediction, but scientific verification remains a human bottleneck. Automated systems can map observations to labels, parameters or hypotheses, yet scientific conclusions require evidence, must satisfy physical consistency, and need explicit testing of alternatives before a decision is made. Here we introduce FORMA (Formalized Observational Reasoning with Auditable Decisions), an executable verification protocol that reconstructs expert reasoning into a workflow: it extracts evidence, generates hypotheses under physical constraints, tests alternatives, and performs auditable consistency checks. Unlike prediction or post-hoc interpretability, executable verification records and tests the evidential path leading to a decision. Astronomical spectroscopy provides a natural testbed, because ambiguous survey spectra are still adjudicated by expert visual inspection. Applied to the Dark Energy Spectroscopic Instrument (DESI) visual inspection catalogue, FORMA combines template-fitting candidate redshifts, spectral evidence extraction and physical audit into an auditable credibility score. A medium-or-higher credibility threshold identifies $331$ definite predictions with $95.5\%$ binary agreement with expert-adjudicated classes, while increasing credibility is associated with improved redshift consistency and higher classification reliability. These results show that automated inference can be coupled to explicit verification, allowing candidate outputs to be evaluated before they enter scientific use.

astro-ph.CO↗

arXiv2026

AKRA 3.0: A matrix-free Inversion Framework for Weak Lensing Mass Mapping and Its Application to DES Y3 Data

Weak gravitational lensing mass mapping offers a direct probe of the matter distribution. Accurate reconstruction of mass maps from masked shear catalogs remains challenging due to survey boundaries and spatially varying noise. In AKRA 2.0, we addressed the mask problem on the curved sky by constructing and inverting the normal-equation matrix $\bf{H} \equiv \mathbf{A}^\mathrm{T}\mathbf{N}^{-1} \mathbf{A}$ explicitly, necessitating a split-scale strategy that reconstructed different angular scales independently to reach high resolution. Here we present AKRA 3.0, in which $\mathbf{H}$ is treated as a linear operator and the normal equations are solved by the conjugate gradient (CG) method. This reformulation reduces the memory requirement from $O(N^2)$ to $O(N)$ and the inversion cost from $O(N^3)$ to $O(N_{\rm iter}N^{3/2}), N \sim \ell_{\rm{max}}^2$ for full-sky (SHT-based) operations. Such optimizations render high-resolution full-sky reconstruction tractable for Stage~III and Stage~IV surveys. Applying AKRA 3.0 to the DES Y3 \texttt{METACALIBRATION} catalog, we produce the highest-resolution convergence map of this dataset to date at HEALPix $N_{\rm{nside}}= 2048$ without imposing any prior assumptions. We extract the convergence power spectrum directly from the reconstructed map and demonstrate that unbiased two-point measurements can be obtained directly from the reconstructed map. The reconstructed E-mode convergence map will be publicly released as data products to enable future studies of non-Gaussian statistics, higher-order moments, and cross-correlations with external datasets.

astro-ph.CO↗

arXiv2026

First Observational Evidence for Split Infall Flow of Cosmic Filaments into Clusters

Velocity fields in the cosmic web are fundamental to structure formation but remain difficult to observe directly beyond the linear regime. Here we present observational evidence that galaxy filaments connecting pairs of galaxy clusters undergo a split infall, with opposite velocity flows toward the two clusters. Using spectroscopic galaxies from the Sloan Digital Sky Survey, we isolate the internal filament velocity field by subtracting its rigid-body background motion and Hubble flow, and detect this effect at greater than $5\sigma$ significance across a wide range of cluster and filament selections. The measured velocity profile exhibits a sign reversal near the filament midpoint and a maximum infall amplitude of $\sim30$ km/s ($\sim20$ km/s projected onto the line-of-sight) for clusters of mass $\sim10^{14.3}M_\odot$, substantially lower than expected for infall from an average cosmic environment. Multiple results on density-velocity correlation, mass-dependency, and validation with simulation indicate that filaments dynamically respond to competing gravitational potentials rather than acting as passive mass transport channels. Our results establish a new observational window on quasi-linear velocity fields in the cosmic web and provide a promising probe of mass measurement, testing gravity and velocity reconstruction with upcoming wide-field spectroscopic surveys.

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arXiv2025

The Shear-to-Cosmology Paradigm I. Hybrid Field-Level and Simulation-Based Framework for Weak Lensing Surveys

Precise cosmological inference from next-generation weak lensing surveys requires extracting non-Gaussian information beyond standard two-point statistics. We present a hybrid machine-learning (ML) framework that integrates field-level inference (FLI) with simulation-based inference (SBI) to map observed shear fields directly to cosmological parameters, eliminating the need for convergence reconstruction. The FLI network extracts rich non-Gaussian information from the shear field to produce informative features, which are then used by SBI to model the resulting complex posteriors. To mitigate noise from intrinsic galaxy shapes, we develop a blind, training-free, PCA-based shear denoising method. Tests on CSST-like mock catalogs reveal significant performance gains. The shear-based inference achieves approximately twice the cosmological constraining power in Figure of Merit (FoM) compared to the conventional convergence-based approach. Moreover, the combination of PCA denoising and ML compression can deliver a 36.4% improvement in FoM over standard shear two-point statistics. This work establishes a scalable and robust pathway for cosmological inference, unlocking the full potential of Stage-IV weak-lensing surveys.

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arXiv2025

AKRA 2.0: Accurate Kappa Reconstruction Algorithm for masked shear catalog

Cosmic shear surveys serve as a powerful tool for mapping the underlying matter density field, including non-visible dark matter. A key challenge in cosmic shear surveys is the accurate reconstruction of lensing convergence ($κ$) maps from shear catalogs impacted by survey boundaries and masks, which seminal Kaiser-Squires (KS) method are not designed to handle. To overcome these limitations, we previously proposed the Accurate Kappa Reconstruction Algorithm (AKRA), a prior-free maximum likelihood map-making method. Initially designed for flat sky scenarios with periodic boundary conditions, AKRA has proven successful in recovering high-precision $κ$ maps from masked shear catalogs. In this work, we upgrade AKRA to AKRA 2.0 by integrating the tools designed for spherical geometry. This upgrade employs spin-weighted spherical harmonic transforms to reconstruct the convergence field over the full sky. To optimize computational efficiency, we implement a scale-splitting strategy that segregates the analysis into two parts: large-scale analysis on the sphere (referred to as AKRA-sphere) and small-scale analysis on the flat sky (referred to as AKRA-flat); the results from both analyses are then combined to produce final reconstructed $κ$ map. We tested AKRA 2.0 using simulated shear catalogs with various masks, demonstrating that the reconstructed $κ$ map by AKRA 2.0 maintains high accuracy. For the reconstructed $κ$ map in unmasked regions, the reconstructed convergence power spectrum $C_κ^{\rm{rec}}$ and the correlation coefficient with the true $κ$ map $r_\ell$ achieve accuracies of $(1-C_\ell^{\rm{rec}}/C_\ell^{\rm{true}}) \lesssim 1\%$ and $(1-r_\ell) \lesssim 1\%$, respectively. Our algorithm is capable of straightforwardly handling further issues such as inhomogeneous shape measurement noise, which we will address in subsequent analysis.

astro-ph.IM↗

arXiv2025

Halo spin and orientation in Interacting Dark Matter Dark Energy Cosmology

In recent years, the interaction between dark matter (DM) and dark energy (DE) has become a topic of interest in cosmology. Interacting dark matter-dark energy (IDE) models have a substantial impact on the formation of cosmological large-scale structures, which serve as the background for DM halo evolution. This impact can be examined through the shape and spin orientation of halos in numerical simulations incorporating IDE effects. In our work, we use the N-body simulation pipeline ME-GADGET to simulate and study the halo spin and orientation in IDE models. We found that in models where DM transfers into DE (IDE I), the alignment of halo shapes with the surrounding tidal field is enhanced, while the alignment of halo spins with the tidal field is decreased compared to $Λ$CDM. Conversely, in models where DE transfers into DM (IDE II), the opposite occurs. We have provided fitted functions to describe these alignment signals. Our study provides the foundation for more accurate modeling of observations in the future such as China Space Station Telescope.

astro-ph.CO↗

arXiv2025

The first AKRA mass map reconstruction from HSC Y1 data

Weak lensing mass-mapping from shear catalogs faces systematic challenges from survey masks and spatially varying noise. To overcome these issues and reconstruct unbiased convergence $\kappa$ maps, we have constructed the AKRA (Accurate Kappa Reconstruction Algorithm), a prior-free and maximum-likelihood based analytical method. It has been validated for mock shear catalogs with a variety of survey masks. In this work, we present the first real-data application of the AKRA on the Subaru Hyper Suprime-Cam Year 1 (HSC Y1) data. We first validate AKRA using mock shear catalogs from the \texttt{Kun} simulation suite, with masks corresponding to the six HSC Y1 regions (\texttt{GAMA09H}, \texttt{GAMA15H}, \texttt{HECTOMAP}, \texttt{VVDS}, \texttt{WIDE12H}, and \texttt{XMMLSS}). The investigated statistics, including the lensing power spectrum, $\langle \kappa^2\rangle$, $\langle \kappa^3\rangle$, and the one-point probability distribution function of $\kappa$, are all unbiased. We then apply AKRA to the HSC Y1 shear catalog and provide reconstructed $\kappa$ maps ready for subsequent scientific analyses.

astro-ph.CO↗

arXiv2025

CURLING -- II. Improvement on the $H_{0}$ Inference from Pixelized Cluster Strong Lens Modeling

Strongly lensed supernovae (glSNe) provide a powerful, independent method to measure the Hubble constant, $H_{0}$, through time delays between their multiple images. The accuracy of this measurement depends critically on both the precision of time delay estimation and the robustness of lens modeling. In many current cluster-scale modeling algorithms, all multiple images used for modeling are simplified as point sources to reduce computational costs. In the first paper of the CURLING program, we demonstrated that such a point-like approximation can introduce significant uncertainties and biases in both magnification reconstruction and cosmological inference. In this study, we explore how such simplifications affect $H_0$ measurements from glSNe. We simulate a lensed supernova at $z=1.95$, lensed by a galaxy cluster at $z=0.336$, assuming time delays are measured from LSST-like light curves. The lens model is constructed using JWST-like imaging data, utilizing both Lenstool and a pixelated method developed in CURLING. Under a fiducial cosmology with $H_0=70\rm \ km \ s^{-1}\ Mpc^{-1}$, the Lenstool model yields $H_0=69.91^{+6.27}_{-5.50}\rm \ km\ s^{-1}\ Mpc^{-1}$, whereas the pixelated framework improves the precision by over an order of magnitude, $H_0=70.39^{+0.82}_{-0.60}\rm \ km \ s^{-1}\ Mpc^{-1}$. Our results indicate that in the next-generation observations (e.g., JWST), uncertainties from lens modeling dominate the error budget for $H_0$ inference, emphasizing the importance of incorporating the extended surface brightness of multiple images to fully leverage the potential of glSNe for cosmology.

astro-ph.CO↗

arXiv2025

A Comparative Study of Halo Mass Estimates from Group Catalogs and Lensing Signals

We compare halo mass estimates from three galaxy group catalogs (redMaPPer, Yang21, and Zou21) with those derived from gravitational lensing measurements. Each catalog employs distinct methodologies, including mass-richness relations, abundance matching, and luminosity-based calibration. A linear correlation is observed between catalog-estimated and lensing-derived masses. The redMaPPer catalog shows the best agreement, especially for lower-redshift groups, with minor deviations in higher-redshift bins. Yang21 is the only catalog containing low mass groups, which gives a reasonably good mass estimation, except for the lowest mass bin. Cross-matched groups between redMaPPer and Yang21 reveal the former catalog provides more accurate mass estimation, while the Yang21 makes under-estimation of halo mass for those sharing the central galaxy with redMaPPer and over-estimation of halo mass for those with different center determination with redMaPPer and for the unique Yang21 groups. These findings emphasize the importance of redshift-dependent calibration and refined group definitions for accurate mass estimation.

astro-ph.CO↗

arXiv2025

Meta-Calibration of the Cosmic Magnification Coefficient: Toward Unbiased Weak Lensing Reconstruction by Counting Galaxies

Weak lensing alters galaxy sizes and fluxes, influencing the clustering patterns of galaxies through cosmic magnification. This effect enables the reconstruction of weak lensing convergence $\hatκ$ maps for DES and DECaLS by linearly combining galaxy overdensities across magnitude bins in the $g$, $r$, and $z$ photometry bands \citep{Qin+,Qin2+}. In this study, we enhance the lensing reconstruction method by addressing biases in the magnification coefficient estimation, which arise from incomplete consideration of selection effects, especially those induced by photometric redshift (photo-$z$) selection. Using a Random Forest-based photo-$z$ estimation for DECaLS and DES galaxies, we quantify the impact of photo-$z$ induced selection on magnification coefficient estimation. Our results show that neglecting photo-$z$ selection introduces significant biases in the magnification coefficient, leading to deviations in the reconstructed convergence map amplitude $A$, with values ranging from 0.4 to 3.5 depending on the survey, redshift, and magnitude cuts. By incorporating an improved magnification coefficient estimation that accounts for photo-$z$ selection, these biases are significantly reduced, with $A$ converging to $\sim 1$ as the magnitude cuts approach optimal values. This improvement is consistently observed across DES and DECaLS datasets and redshift bins, despite differences in survey strategies and depths. Our findings highlight the importance of addressing photo-$z$ induced selection to achieve unbiased weak lensing reconstructions and accurate cosmic magnification measurements.

astro-ph.CO↗

arXiv2025

The fifth data release of the Kilo Degree Survey: Multi-epoch optical/NIR imaging covering wide and legacy-calibration fields

We present the final data release of the Kilo-Degree Survey (KiDS-DR5), a public European Southern Observatory (ESO) wide-field imaging survey optimised for weak gravitational lensing studies. We combined matched-depth multi-wavelength observations from the VLT Survey Telescope and the VISTA Kilo-degree INfrared Galaxy (VIKING) survey to create a nine-band optical-to-near-infrared survey spanning $1347$ deg$^2$. The median $r$-band $5σ$ limiting magnitude is 24.8 with median seeing $0.7^{\prime\prime}$. The main survey footprint includes $4$ deg$^2$ of overlap with existing deep spectroscopic surveys. We complemented these data in DR5 with a targeted campaign to secure an additional $23$ deg$^2$ of KiDS- and VIKING-like imaging over a range of additional deep spectroscopic survey fields. From these fields, we extracted a catalogue of $126\,085$ sources with both spectroscopic and photometric redshift information, which enables the robust calibration of photometric redshifts across the full survey footprint. In comparison to previous releases, DR5 represents a $34\%$ areal extension and includes an $i$-band re-observation of the full footprint, thereby increasing the effective $i$-band depth by $0.4$ magnitudes and enabling multi-epoch science. Our processed nine-band imaging, single- and multi-band catalogues with masks, and homogenised photometry and photometric redshifts can be accessed through the ESO Archive Science Portal.

astro-ph.GA↗

arXiv2025

KiDS-1000: Weak lensing and intrinsic alignment around luminous red galaxies

We study the properties of luminous red galaxies (LRGs) selected from the fourth data release of the Kilo Degree Survey (KiDS-1000) via galaxy-galaxy lensing of the background galaxies from KiDS-1000. We used a halo model formalism to interpret our measurements and obtain estimates of the halo masses as well as the satellite fractions of the LRGs, resulting in halo masses of $2.7 \times 10^{12} h^{-1} {\rm M}_{\odot}<M_{\rm h}< 2.6 \times 10^{13} h^{-1} {\rm M}_{\odot}$. We studied the strength of intrinsic alignments (IA) using the position-shape correlations as a function of LRG luminosity, where we used a double power law to describe the relation between luminosity and halo mass to allow for a comparison with previous works. Here, we directly linked the observed IA of the (central) galaxy to the mass of the hosting halo, which is expected to be a fundamental quantity in establishing the alignment. We find that the dependence of the IA amplitude on halo mass is described well by a single power law, with an amplitude of $A = 5.74\pm{0.32}$ and slope of $β_M = 0.44\pm{0.04}$, in the range of $1.9 \times 10^{12}h^{-1} {\rm M}_{\odot}<M_{\rm h}<3.7 \times 10^{14} h^{-1} {\rm M}_{\odot}$. We also find that both red and blue galaxies from the source sample associated with the LRGs are randomly oriented, with respect to the LRGs, although our detection significance is limited by the uncertainty in our photometric redshifts.

astro-ph.CO↗

arXiv2024

Weak Lensing Reconstruction by Counting DECaLS Galaxies

Alternative to weak lensing measurements through cosmic shear, we present a weak lensing convergence $\hatκ$ map reconstructed through cosmic magnification effect in DECaLS galaxies of the DESI imaging surveys DR9. This is achieved by linearly weighing $12$ maps of galaxy number overdensity in different magnitude bins of $grz$ photometry bands. The weight is designed to eliminate the mean galaxy deterministic bias, minimize galaxy shot noise while maintaining the lensing convergence signal. We also perform corrections of imaging systematics in the galaxy number overdensity. The $\hatκ$ map has $8365$ deg$^2$ sky coverage. Given the low number density of DECaLS galaxies, the $\hatκ$ map is overwhelmed by shot noise and the map quality is difficult to evaluate using the lensing auto-correlation. Alternatively, we measure its cross-correlation with the cosmic shear catalogs of DECaLS galaxies of DESI imaging surveys DR8, which has $8365$ deg$^2$ overlap in sky coverage with the $\hatκ$ map. We detect a convergence-shear cross-correlation signal with $S/N\simeq 10$. The analysis also shows that the galaxy intrinsic clustering is suppressed by a factor $\mathcal{O}(10^2)$ and the residual galaxy clustering contamination in the $\hatκ$ map is consistent with zero. Various tests with different galaxy and shear samples, and the Akaike information criterion analysis all support the lensing detection. So is the imaging systematics corrections, which enhance the lensing signal detection by $\sim 30\%$. We discuss various issues for further improvement of the measurements.

astro-ph.CO↗

arXiv2024

Weak Lensing Reconstruction by Counting Galaxies: Improvement with DES Y3 Galaxies

In \citep{Qin+}, we attempted to reconstruct the weak lensing convergence map $\hatκ$ from cosmic magnification by linearly weighting the DECaLS galaxy overdensities in different magnitude bins of $grz$ photometry bands. The $\hatκ$ map is correlated with cosmic shear at 20-$σ$ significance. However, the low galaxy number density in the DECaLS survey prohibits the measurement of $\hatκ$ auto-correlation. In this paper, we apply the reconstruction method to the Dark Energy Survey Year 3 (DES Y3) galaxies from the DES Data Release 2 (DR2). With greater survey depth and higher galaxy number density, convergence-shear cross-correlation signals are detected with $S/N\approx 9,16,20$ at $0.4<z_κ<0.6,0.6<z_κ<0.8$ and $0.8<z_κ<1.0$ respectively. More remarkably, the $\hatκ-\hatκ$ correlations of the $0.4<z_κ<0.6$ and $0.6<z_κ<0.8$ bins show reasonably good agreement with predictions based on theoretical interpretation of $\hatκ-γ$ measurement. This result takes a step further towards the cosmological application of our lensing reconstruction method.

astro-ph.CO↗

arXiv2024

The measurement of the splashback radius of dark matter halo

In the hierarchical evolution framework of cosmology, larger halos grow through matter accretion and halo mergers. To clarify the halo evolution, we need to define the halo mass and radius physically. However, the pseudo-evolution problem makes the process difficult. Thus, we aim to measure the splashback radius, a physically defined halo radius for a large number of halos with various mass and redshift, and to determine the most important parameters to affect it. We use the typical definition of splashback radius (Rsp) as the radius with the steepest radial density profile. In this work, we measure Rsp of dark matter halos within the mass of 1e13-3e15Msun and redshifts spanning 0.08-0.65. This is the measurement of the Rsp in the largest range of halo mass and redshift. Using the shear catalog of the DECaLS DR8, we investigate Rsp of halos associated with galaxies and galaxy clusters identified in the various catalogs. Our finding reveals a trend wherein massive halos demonstrate a larger Rsp, and the normalized splashback radius (Rsp/R200m) shows a U-shaped mass evolution. The upturn in these relations mainly comes from the contribution of massive halos with low redshifts. We further find Rsp increases with the peak height, while Rsp/R200m has a negative relation with the peak height. We also find the Rsp >~R200m for most halos, indicating their low accretion rates. Our result is consistent with previous literature across a wide range of mass, redshift, and peak height, as well as the simulation work from More et al. (2015).

astro-ph.CO↗

arXiv2024

Forecast of joint analysis of cosmic shear and supernovae magnification from CSST and LSST

Cosmic shear and cosmic magnification reflect the same gravitational lensing field. Each of these two probes are affected by different systematics. We study the auto- and cross-correlations of the cosmic shear from the China Space Survey Telescope (CSST) and cosmic magnification of supernovae from Large Synoptic Survey Telescope (LSST). We want to answer, to what extent, by adding the magnification data we can remove the systematic bias in cosmic shear measurement. We generate the mock shear/magnification maps based on the correlation between of different tomographic bins. After obtaining the corrected power spectra, we adopt the Markov Chain Monte Carlo (MCMC) technique to fit the theoretical models, and investigate the constraints on the cosmological and nuisance parameters. We find that the with only cosmic shear data, there are $1σ$ bias in $σ_8$ and intrinsic alignment model parameters. By adding the magnification data, we are able to remove these biases perfectly.

astro-ph.CO↗

arXiv2024

CURLING - I. The Influence of Point-like Image Approximation on the Outcomes of Cluster Strong Lens Modeling

Cluster-scale strong lensing is a powerful tool for exploring the properties of dark matter and constraining cosmological models. However, due to the complex parameter space, pixelized strong lens modeling in galaxy clusters is computationally expensive, leading to the point-source approximation of strongly lensed extended images, potentially introducing systematic biases. Herein, as the first paper of the ClUsteR strong Lens modelIng for the Next-Generation observations (CURLING) program, we use lensing ray-tracing simulations to quantify the biases and uncertainties arising from the point-like image approximation for JWST-like observations. Our results indicate that the approximation works well for reconstructing the total cluster mass distribution, but can bias the magnification measurements near critical curves and the constraints on the cosmological parameters, the total matter density of the Universe $Ω_{\rm m}$, and dark energy equation of state parameter $w$. To mitigate the biases, we propose incorporating the extended surface brightness distribution of lensed sources into the modeling. This approach reduces the bias in magnification from 46.2 per cent to 0.09 per cent for $μ\sim 1000$. Furthermore, the median values of cosmological parameters align more closely with the fiducial model. In addition to the improved accuracy, we also demonstrate that the constraining power can be substantially enhanced. In conclusion, it is necessary to model cluster-scale strong lenses with pixelized multiple images, especially for estimating the intrinsic luminosity of highly magnified sources and accurate cosmography in the era of high-precision observations.

astro-ph.CO↗

arXiv2024

Forklens: Accurate weak-lensing shear measurement with deep learning

Weak gravitational lensing is one of the most important probes of the nature of dark matter and dark energy. In order to extract cosmological information from next-generation weak lensing surveys (e.g., Euclid, Roman, LSST, and CSST) as much as possible, accurate measurements of weak lensing shear are required. There are existing algorithms to measure the weak lensing shear on imaging data, which have been successfully applied in previous surveys. In the meantime, machine learning (ML) has been widely recognized in various astrophysics applications in modeling and observations. In this work, we present a fully deep-learning-based approach to measuring weak lensing shear accurately. Our approach comprises two modules. The first one contains a convolutional neural network (CNN) with two branches for taking galaxy images and point spread function (PSF) simultaneously, and the output of this module includes the galaxy's magnitude, size, and shape. The second module includes a multiple-layer neural network (NN) to calibrate weak-lensing shear measurements. We name the program Forklens and make it publicly available online. Applying Forklens to CSST-like mock images, we achieve consistent accuracy with traditional approaches (such as moment-based measurement and forward model fitting) on the sources with high signal-to-noise ratios (S/N, > 20). For the sources with S/N < 10, Forklens exhibits an $\sim 36\%$ higher Pearson coefficient on galaxy ellipticity measurements. After adopting galaxy weighting, the shear measurements with Forklens deliver accuracy levels to $0.2\%$. The whole procedure of Forklens is automated and costs about $0.7$ milliseconds per galaxy, which is appropriate for adequately taking advantage of the sky coverage and depth of the upcoming weak lensing surveys.

astro-ph.CO↗