arXiv subjects

Yizhou Gu

Publications and source records attributed to Yizhou Gu.

At least 19 recordsLinked to original sources

arXiv2026

ELUCID-DESI II. Revealing dark matter mass, tidal, and velocity (MTV) fields using galaxy group phase information

We introduce a novel method for reconstructing the cosmic mass, tidal, and velocity (MTV) fields over the redshift range $0 < z < 0.6$ using the phase information of galaxy groups. This approach replaces the explicit theoretical bias correction typically needed to relate galaxy groups to the underlying dark matter density field with a simulation-calibrated statistical mapping, reducing a major source of systematic uncertainty and making the method directly applicable to spectroscopic redshift surveys such as the DESI Bright Galaxy Survey (BGS). We evaluate the performance of our MTV reconstruction pipeline with mock redshift surveys that include a comprehensive set of observational selection effects. The galaxy groups used as tracers are identified with an extended halo-based group finder applied to the DESI mock galaxy catalogue with an apparent magnitude limit of $m_z < 19.65$, yielding a galaxy number comparable to that of the DESI BGS faint sample ($m_r < 20.175$). Our tests show that the reconstructed velocities are accurate and unbiased, with a residual dispersion of $\sim 120\ \mathrm{km\,s^{-1}}$ across the redshift bins. The recovered velocity field allows us to shift galaxy groups to their real-space positions, thereby correcting for the Kaiser effect. By iteratively applying this Kaiser correction to the galaxy groups, we further reconstruct the tidal field and the mass-density distribution. The reconstruction is stable with respect to the grid resolution. Overall, our results demonstrate that this group-based phase-space reconstruction provides a robust pathway to recovering the dark matter MTV fields, with strong prospects for application to DESI BGS data.

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arXiv2026

FASHI DR2: A Catalog of 132 Low-Redshift HI 21 cm Absorption Systems

We present an untargeted survey of 21 cm HI absorption systems based on the second data release of the FAST All Sky HI survey (FASHI DR2), covering approximately 19,500 deg$^{2}$ at $z\lesssim0.09$. A total of 132 HI absorbers are identified, including approximately 60 new discoveries, forming one of the largest homogeneous samples of low-redshift HI absorbers assembled to date. The sample extends to continuum flux densities as low as 2.6 mJy, substantially below the limits of previous flux-limited surveys. The absorber population is dominated by narrow systems ($W_{50}<100$ km s$^{-1}$), while broad absorbers ($W_{50}>200$ km s$^{-1}$) account for 13.6% of the sample. Most absorbers are optically thin, with a median optical depth of $\tau_{\rm HI}\approx0.14$. The velocity-offset distribution is broadly symmetric about the systemic velocities of the host galaxies. The associated absorbers are preferentially found in massive, actively star-forming galaxies. We find tentative evidence for a weak anti-correlation between HI column density and stellar mass, although the relation exhibits substantial scatter. These results provide the first statistical characterization of the low-redshift HI absorber population based on the FASHI DR2 sample and establish a valuable benchmark for future HI absorption surveys with next-generation radio facilities.

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arXiv2026

Quantifying Environmental Effects on Galaxy Properties using Non-spherical Voids Identified from SDSS DR7

Cosmic voids provide a distinct low-density region for studying the environmental effects of galaxy properties. Using the SDSS DR7 catalog, we identify non-spherical voids via Voronoi tessellation and the watershed algorithm, and classify void galaxies based on their local volume. We compare and find that void galaxies classified by this method are systematically less massive, fainter, bluer, and have higher specific star formation rate (sSFR) than non-void galaxies and all galaxy samples. We then divide void and non-void galaxies into stellar mass bins to focus on the environmental dependence of $g-r$ color and sSFR. By further classifying galaxies into blue/red and star-forming/quiescent populations, we calculate the ratio of blue to red and star-forming to quiescent for void and non-void galaxies separately. Comparing the ratio of the void value to the non-void value for both metrics presents an overall decreasing trend with stellar mass $M_*$ over the $9.4-10.4$ range in $\log[M_*/\mathrm{M}_\odot]$, indicating a stronger environmental effect in lower-mass systems. These results show that our classification of void galaxies in non-spherical voids based on local volume offers a robust approach for quantifying the influence of underdense environments on galaxy evolution.

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arXiv2026

Weak Evolution of Cosmic Atomic Hydrogen over the Past 4.5 Billion Years

The cosmic star formation rate density (CSFRD) has declined sharply toward the present day, but the roles of the atomic and molecular gas reservoirs remain uncertain. We measure the cosmic HI density, $\Omega_{\mathrm{HI}}$, over $0<z<0.41$ by combining HI spectra from the Five-hundred-meter Aperture Spherical Telescope with optical spectroscopy from the Dark Energy Spectroscopic Instrument for $\sim2.5$ million galaxies across $\sim12,000\,{\rm deg}^2$. We measure a raw decrease in $\Omega_{\mathrm{HI}}$ by a factor of $1.35\pm0.10$ over the past 4.5 Gyr. Even after applying the conservative systematic corrections from our forward model, the inferred decline is only $1.12\pm0.10$ -- still far weaker than the CSFRD decline (a factor of 2.46). The molecular gas density, in contrast, is known to evolve more closely with star formation. At fixed stellar mass, the average HI gas fraction evolves by less than 0.2 dex, showing that the weak evolution is present across the galaxy population. These quantitative differences rule out rapid depletion of galaxy HI as the primary driver of the late-time CSFRD decline, and provide a stringent benchmark for models of gas accretion, phase conversion and star-formation regulation.

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arXiv2026

CSST large-scale structure analysis pipeline: IV. Cosmic Voids Identified from Galaxy Group Samples as Probes of the Large-scale Structure

Because groups are directly associated with halos, they allow for considerably simpler theoretical modeling than approaches based on individual galaxies. We therefore propose to use voids identified in galaxy group catalogs, referred to as group-voids, to investigate the cosmic large-scale structure (LSS). Using the reference mock galaxy redshift survey (MGRS) designed for the Chinese Space-station Survey Telescope (CSST), we build two galaxy group catalogs representing ideal and realistic scenarios, derived from galaxy samples with 100\% and roughly 30\% spectroscopic redshift completeness, respectively. We then identify voids in these two mock group catalogs, as well as in the underlying halo catalog, and measure two void statistics, the void size function (VSF) and the void density profile, within five redshift intervals spanning $z=0$ to $1.0$. We compare the statistics obtained from two kinds of voids: those defined by galaxy groups (group-voids) and those defined by dark matter halos (halo-voids). In the void-finding process, we adopt the brightest central galaxy (BCG) as the group center to improve the accuracy of the inferred void centers. Our analysis shows that void statistics derived from group-voids with spectroscopic redshift completeness of at least 40\% can faithfully reproduce the corresponding statistics from halo-voids. Even when the redshift completeness of galaxies falls to as low as 30\%, we can still reliably describe group-voids via halo-voids by incorporating a redshift error term. This indicates that group-voids are a promising tool for probing LSS and offer a valuable complement to standard void studies, which is especially advantageous for emulator-based methods.

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arXiv2026

What can galaxy clustering really tell us about the galaxy-halo connections?

Subhalo abundance matching (SHAM) is a commonly used framework for modeling the galaxy-halo connection. Yet, its standard implementation has difficulty reproducing the observed galaxy clustering with high accuracy (e.g., $\chi^2/\mathrm{dof} \approx 1$). To overcome this issue, we propose a novel CS-SHAM framework, in which central and satellite galaxies are independently matched to main and satellite subhalos in simulations. Within this scheme, we introduce three free parameters to explicitly characterize the satellite fraction, $f_{\mathrm{sat}}$, as a function of stellar mass or absolute magnitude. To evaluate the performance of CS-SHAM, we apply it to two sets of mock galaxy catalogs built with the conventional SHAM method but using different subhalo mass proxies, $M_{\mathrm{peak}}$ and $V_{\mathrm{peak}}$, as well as two additional galaxy samples generated from a SAM and from TNG-300. We demonstrate that CS-SHAM reliably reproduces galaxy clustering whether $M_{\mathrm{peak}}$ or $V_{\mathrm{peak}}$ is used as the subhalo mass proxy. We also find that the models are unable to place robust constraints on $f_{\mathrm{sat}}$ if different mass proxies are employed. Indeed, within the CS-SHAM framework the halo occupation distribution (HOD) and conditional luminosity or stellar mass function (CLF/CSMF) are accurately recovered. Furthermore, we demonstrate for the first time that galaxy clustering constrains the HOD and CLF/CSMF primarily for relatively massive halos. Because the halo bias is nearly constant for low-mass halos, galaxy clustering is generally not very sensitive to the satellite population residing in these low-mass systems.

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arXiv2026

Galaxy populations in groups and clusters-II. Conditional luminosity functions at redshifts from z~1 to z~0

Using DESI SV3 spectroscopic group centrals and HSC photometric data, we measure conditional luminosity functions (CLFs) of central and satellite galaxies for red and blue populations in dark matter haloes spanning $M_h\sim10^{12}- 10^{15}M_{\odot}$ and $0<z<1$. HSC depth permits measurements to $M_r \approx -15$ at $0.2 \leqslant z < 0.5$ and $M_r \approx -17$ at $0.5 \leqslant z < 1.0$. We find satellite CLFs evolve weakly over $0<z<1$. Blue satellite CLFs are well described by a single Schechter function across halo masses and redshifts, with a nearly constant slope of $-1.25\lesssim \alpha\lesssim -1.2$. In contrast, red satellite CLFs exhibit a pronounced faint-end upturn in all halo mass and redshift bins, with little evolution in the faint-end slope ($-1.8\lesssim \alpha_f\lesssim -1.7$). The low-mass red sequence was therefore already established in clusters/groups by $z\sim1$. The lack of faint-end-slope evolution favors models where the steep upturn originates from early formation processes at $z\gtrsim2$, rather than environmental quenching after infall. Satellite characteristic magnitudes and central galaxy luminosities fade with time. Red central galaxies are consistent with passive evolution, whereas blue-central luminosity evolution is dominated by ongoing star formation. Satellites evolve more rapidly than predicted by simple stellar population models, highlighting environmental effects. Satellite quenched fractions as a function of stellar mass exhibit a minimum at $M_{*} \sim 10^9M_{\odot}$ that is consistent across halo masses and redshifts. We discuss possible interpretations of these results and their implications for galaxy formation and evolution.

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arXiv2026

BayeSED-GALAXIES II. Bayesian full spectrum analysis of galaxies and application in the CSST wide-field slitless spectroscopy survey

The China Space Station Telescope (CSST) will conduct wide-field multiband photometric imaging and slitless spectroscopic surveys, advancing cosmology and galaxy evolution studies. Achieving CSST's cosmological goals requires precise redshifts ($\sigma_{\rm NMAD}\lesssim 0.002-0.005$) from low-resolution ($R\sim200$) and potentially blended slitless spectra. We present BayeSED3, extended for Bayesian full-spectrum analysis, including nebular emission modeling (via \textsc{Cloudy}) and a Bayesian treatment of the model scaling factor, improving reliability over optimization methods for low SNR spectra. Validated on realistic mock data generated with the CESS emulator (median SNR=1.65, including instrumental and self-blending effects), our method achieves excellent redshift precision with three-band (GU+GV+GI) spectroscopy: $\sigma_{\rm NMAD}=0.0008$ ($\sim$80% success) for star-forming and $\sigma_{\rm NMAD}=0.0015$ ($\sim$50% success) for quiescent galaxies. Stellar mass ($\sigma_{\rm NMAD}\approx0.015$ dex for SF, $\approx0.016$ dex for quiescent) and SFR ($\sigma_{\rm NMAD}\approx0.05$ dex for SF, especially at SNR>1) are reliably recovered. Self-blending increases scatter by $\gtrsim30%$, but combining spectroscopy with CSST's seven-band photometry significantly improves accuracy, especially for quiescent galaxies and data-limited cases. Single-band spectroscopy plus photometry yields reasonable redshifts: GU+photometry is limited, GI+photometry gives >60% (SF) and >40% (quiescent) success at $\sigma_{\rm NMAD}\lesssim0.002$, GV+photometry gives >35% (SF) and $\sim$40% (quiescent) at similar precision. The Bayesian framework offers a powerful method for accurate galaxy characterization, enhancing CSST's scientific outcomes despite the challenges of slitless spectroscopy.

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arXiv2026

ELUCID-DESI I: A Parallel MPI Implementation of the Initial Condition Solver for Large-Scale Reconstruction Simulations

We present a highly scalable, MPI-parallelized framework for reconstructing the initial cosmic density field, designed to meet the computational demands of next-generation cosmological simulations, particularly the upcoming ELUCID-DESI simulation based on DESI BGS data. Building upon the Hamiltonian Monte Carlo approach and the FastPM solver, our code employs domain decomposition to efficiently distribute memory between nodes. Although communication overhead increases the per-step runtime of the MPI version by roughly a factor of eight relative to the shared-memory implementation, our scaling tests-spanning different particle numbers, core counts, and node layouts-show nearly linear scaling with respect to both the number of particles and the number of CPU cores. Furthermore, to significantly reduce computational costs during the initial burn-in phase, we introduce a novel ``guess'' module that rapidly generates a high-quality initial density field. The results of the simulation test confirm substantial efficiency gains: for $256^3$ particles, 53 steps ($\sim$ 54 core hours) are saved, accelerating convergence by a factor of $\sim$ 18; for $1024^3$, 106 steps ($\sim$7500 core hours), achieving a speedup factor of $\sim$ 3. The total core hour gain grows with the number of particles, rendering large-volume reconstructions computationally practical for upcoming surveys, including our planned ELUCID-DESI reconstruction simulation with $4096^3$ particles. We estimate that achieving convergence for this scale (targeting DESI-BGS data) requires about 800 HMCMC steps ($\sim$ 5 million core hours). Our initial guess module will save approximately 360 steps ($\sim$2.3 million core hours), reducing the total computational time by about 45\%.

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arXiv2026

Filtering Interlopers with Photometry and Diagnostic Features: A Machine Learning Framework Validated with CSST Slitless Spectroscopy

The slitless spectroscopic method employed by missions such as Euclid and the Chinese Space-station Survey Telescope (CSST) faces a fundamental challenge: spectroscopic redshifts derived from their data are susceptible to emission-line misidentification due to the limited spectral resolution and signal-to-noise ratio. This effect systematically introduces interloper galaxies into the sample. Conventional strict selection not only struggles to secure high redshift purity but also drastically reduces completeness by discarding valuable data. To overcome this limitation, we develop an XGBoost classifier that leverages photometric properties and spectroscopic diagnostics to construct a high-purity redshift catalog while maximizing completeness. We validate this method on a simulated sample with spectra generated by the CSST emulator for slitless spectroscopy. Of the $\sim$62 million galaxies that obtain valid redshifts (parent sample), approximately 43% achieve accurate measurements, defined as $|\Delta z| \leqslant 0.002(1+z)$. From this parent sample, the XGBoost classifier selects galaxies with a selection efficiency of 42.3% on the test set and 42.2% when deployed on the entire parent sample. Crucially, among the retained galaxies, 96.6% (parent sample: 96.5%) achieve accurate measurements, while the outlier fraction ($|\Delta z|>0.01(1+z)$) is constrained to 0.13% (0.11%). We verified that simplified configurations that exclude either spectroscopic diagnostics (except the measured redshift) or photometric data yield significantly higher outlier fractions, increasing by factors of approximately 3.5 and 6.3, respectively, with the latter case also introducing notable catastrophic interloper contamination. This framework effectively resolves the purity-completeness trade-off, enabling robust large-scale cosmological studies with CSST and similar surveys.

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arXiv2025

Unveiling the Sources of X-ray Luminosity in DESI Galaxy Groups: Insights from the SRG/eROSITA All-Sky Survey

We use the first eROSITA all-sky survey (eRASS1) to investigate the contributions of AGN and extended gas to the total X-ray luminosity ($L_X$) of galaxy groups with different halo masses ($M_h$) at different redshifts. The presence of AGN in their central galaxies is identified using multi-wavelength catalogs, including the X-ray counterparts, the ASKAP radio catalog, and the DESI spectroscopic measurements. We apply the stacking method to obtain sufficient statistics for the X-ray surface brightness profile and the $L_X$ for groups with different central AGN properties. We find that the X-ray groups exhibit the highest $L_X$, followed by groups with QSO, radio, BPT-AGN, and non-AGN centrals. Moreover, the $L_X$ of the $M_h \lesssim 10^{13}h^{-1}M_\odot$ groups is dominated by the central AGN, while the X-ray emission from extended gas tends to be more prominent in the $M_h \gtrsim 10^{13}h^{-1}M_\odot$ groups. In groups where the AGN play a major role in X-ray emission, the contribution from extended gas is minor, resulting in significant uncertainties concerning the extended X-ray emission. When the subset containing the X-ray detected counterparts is excluded, the extended gas component becomes easier to obtain. A correlation has been identified between the X-ray luminosity of the central AGN and extended gas. However, once we account for the positional offset, their correlation becomes less prominent. Currently, the results are not conclusive enough to confirm whether there is a connection between the AGN feedback and extended gas. However, they provide a new perspective on the feedback processes in the history of group assembly.

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arXiv2025

The Jiutian simulations for the CSST extra-galactic surveys

We provide an overview of the Jiutian simulations, a hybrid simulation suite for the China Space Survey Telescope (CSST) extragalactic surveys. It consists of four complementary modules: the primary runs with high resolutions with the fiducial concordance cosmology, the emulator runs exploring the parameter uncertainties around the fiducial cosmology, the reconstruction runs intended for recovering the observed Universe position by position, and the extension runs employing extended cosmologies beyond the standard model. For the primary runs, two independent pipelines are adopted to construct subhaloes and merger trees. On top of them, four sets of mock galaxy light-cone catalogs are produced from semi-analytical models and subhalo abundance matching, providing a variety of observational properties including galaxy SED, emission lines, lensing distortions, and mock images. The 129 emulator runs are used to train the CSST emulator, achieving one percent accuracy in predicting the matter power spectrum over $k\leq 10h{\rm Mpc}^{-1}$ and $z\leq 2$. The reconstruction runs employ a number of subgrid baryonic models to predict the evolution and galaxy population resembling certain regions in the real Universe with constrained initial conditions, enabling controlled investigation of galaxy formation on top of structure formation. The extension runs cover models with warm dark matter, $f(R)$ gravity, interacting dark energy, and nonzero neutrino masses, revealing differences in the cosmic structure under alternative cosmological models. We introduce the specifications for each run, the data products derived from them, the corresponding pipeline developments, and present some main tests. Using the primary runs, we also show that the subhalo peak mass functions of different levels are approximately universal. These simulations form a comprehensive and open library for CSST surveys and beyond.

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arXiv2025

Luminosity and stellar mass functions of faint photometric satellites around spectroscopic central galaxies from DESI Year-1 Bright Galaxy Survey

We measure the luminosity functions (LFs) and stellar mass functions (SMFs) of photometric satellite galaxies around spectroscopically identified isolated central galaxies (ICGs). The photometric satellites are from the DESI Legacy Imaging Surveys (DR9), while the spectroscopic ICGs are selected from the DESI Year-1 BGS sample. We can measure satellite LFs down to $r$-band absolute magnitudes of $M_{r,\mathrm{sat}}\sim-7$, around ICGs as small as $7.1<\log_{10}M_{\ast,\mathrm{ICG}}/\mathrm{M_\odot}<7.8$, with the stellar mass of ICGs measured by the DESI Fastspecfit pipeline. The satellite SMF can be measured down to $\log_{10}M_{\ast,\mathrm{sat}}/\mathrm{M_\odot}\sim 5.5$. Interestingly, we discover that the faint/low-mass end slopes of satellite LFs/SMFs become steeper with the decrease in the stellar masses of host ICGs, with smaller and nearby host ICGs capable of being used to probe their fainter satellites.. The steepest slopes are $-2.298\pm0.656$ and $-$2.888$\pm$0.916 for satellite LF and SMF, respectively. Detailed comparisons are performed between the satellite LFs around ICGs selected from DESI BGS or from the SDSS NYU-VAGC spectroscopic Main galaxies over $7.1<\log_{10}M_{\ast,\mathrm{ICG}}/\mathrm{M_\odot}<11.7$, showing reasonable agreements, but we show that the differences between DESI and SDSS stellar masses for ICGs play a role to affect the results. We also compare measurements based on DESI Fastspecfit and Cigale stellar masses used to bin ICGs, with the latter including the modeling of AGN based on WISE photometry, and we find good agreements in the measured satellite LFs by using either of the DESI stellar mass catalogs.

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arXiv2025

CSST Large Scale Structure Analysis Pipeline: III. Emission-line Redshift Measurement for Slitless Spectra

The China Space Station Telescope (CSST) is a forthcoming space-based optical telescope designed to co-orbit with the Chinese Space Station. With a planned slitless spectroscopic survey spanning a broad wavelength range of $255-1000$nm and an average spectral resolution exceeding 200, the CSST holds significant potential for cosmic large-scale structure analysis. In this study, we focus on redshift determinations from slitless spectra through emission line analysis within the CSST framework. Our tailored redshift measurement process involves identifying emission lines in one-dimensional slitless spectra, aligning observed wavelengths with their rest-frame counterparts from prominent galaxy emissions, and calculating wavelength shifts to determine redshifts accurately. To validate our redshift measurement algorithm, we leverage simulated spectra generated by the CSST emulator for slitless spectroscopy. The outcomes demonstrate a remarkable redshift completeness exceeding 95 per cent for emission line galaxies (ELGs), alongside a purity surpassing 85 per cent. The redshift uncertainty remains impressively below than $\sim 0.001$. Notably, when concentrating on galaxies with more than three matched emission lines, the completeness of ELGs and the purity of measurable galaxies can reach 98 per cent and 97 per cent, respectively. Furthermore, we explore the influence of parameters like magnitude, spectral signal-to-noise ratio, and redshift on redshift completeness and purity. The discussion also delves into redshift degeneracies stemming from emission-line matching confusion. Our developed redshift measurement process will be applied to extensive simulated datasets and forthcoming CSST slitless spectroscopic observations for further cosmological and extragalactic analyses.

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arXiv2025

DarkAI: Reconstructing the density, velocity and tidal field of dark matter from a DESI-like bright galaxy sample

Reconstructing the mass density, velocity, and tidal (MTV) fields of dark matter from galaxy surveys is essential for advancing our understanding of the LSS of the Universe. In this work, we present a machine learning-based framework using a UNet convolutional neural network to reconstruct the MTV fields from mock samples of the DESI bright galaxy survey within the redshift range $0.1 < z < 0.4$. Our approach accounts for realistic observational effects, including geometric selection, flux-limited data, and redshift space distortion (RSD) effects, thereby improving the fidelity of the reconstructed fields. Testing on mock galaxy catalogs generated from the Jiutian N-body simulation, our method achieves significant accuracy level. The reconstructed density field exhibits strong consistency with the true field, effectively eliminating most RSD effects and achieving a cross-correlation power spectrum coefficient greater than 0.985 on scales with $k < 0.1 \, h \, \mathrm{Mpc}^{-1}$. The velocity field reconstruction accurately captures large-scale coherent flows and small-scale turbulent features, exhibiting slopes of grid-to-grid relationships close to unity and scatter below $\sim$100 $\mathrm{km} \, \mathrm{s}^{-1}$. Additionally, the tidal field is reconstructed without bias, successfully recovering the features of the large-scale cosmic web, including clusters, filaments, sheets, and voids. Our results confirm that the proposed framework effectively captures the large-scale distribution and dynamics of dark matter while addressing key systematic challenges. These advancements provide a reliable and robust tool for analyzing current and future galaxy surveys, paving the way for new insights into cosmic structure formation and evolution.

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arXiv2025

Cosmological distance forecasts for the CSST Galaxy Survey using BAO peaks

The measurement of cosmological distances using baryon acoustic oscillations (BAO) is crucial for studying the universe's expansion. The Chinese Space Station Telescope (CSST) galaxy redshift survey, with its vast volume and sky coverage, provides an opportunity to address key challenges in cosmology. However, redshift uncertainties in galaxy surveys can degrade both angular and radial distance estimates. In this study, we forecast the precision of BAO distance measurements using mock CSST galaxy samples, applying a two-point correlation function (2PCF) wedge approach to mitigate redshift errors. We simulate redshift uncertainties of $\sigma_0 = 0.003$ and $\sigma_0 = 0.006$, representative of expected CSST errors, and examine their effects on the BAO peak and distance scaling factors, $\alpha_\perp$ and $\alpha_\parallel$, across redshift bins within $0.0 < z \leqslant 1.0$. The wedge 2PCF method proves more effective in detecting the BAO peak compared to the monopole 2PCF, particularly for $\sigma_0 = 0.006$. Constraints on the BAO peaks show that $\alpha_\perp$ is well constrained around 1.0, regardless of $\sigma_0$, with precision between 1% and 3% across redshift bins. In contrast, $\alpha_\parallel$ measurements are more sensitive to increases in $\sigma_0$. For $\sigma_0 = 0.003$, the results remain close to the fiducial value, with uncertainties ranging between 4% and 9%; for $\sigma_0 = 0.006$, significant deviations from the fiducial value are observed. We also study the ability to measure parameters $(\Omega_m, H_0r_\mathrm{d})$ using distance measurements, proving robust constraints as a cosmological probe under CSST-like redshift uncertainties.

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arXiv2024

An efficient unsupervised classification model for galaxy morphology: Voting clustering based on coding from ConvNeXt large model

In this work, we update the unsupervised machine learning (UML) step by proposing an algorithm based on ConvNeXt large model coding to improve the efficiency of unlabeled galaxy morphology classifications. The method can be summarized into three key aspects as follows: (1) a convolutional autoencoder is used for image denoising and reconstruction and the rotational invariance of the model is improved by polar coordinate extension; (2) utilizing a pre-trained convolutional neural network (CNN) named ConvNeXt for encoding the image data. The features were further compressed via a principal component analysis (PCA) dimensionality reduction; (3) adopting a bagging-based multi-model voting classification algorithm to enhance robustness. We applied this model to I-band images of a galaxy sample with $I_{\rm mag}< 25$ in the COSMOS field. Compared to the original unsupervised method, the number of clustering groups required by the new method is reduced from 100 to 20. Finally, we managed to classify about 53\% galaxies, significantly improving the classification efficiency. To verify the validity of the morphological classification, we selected massive galaxies with $M(*)>10^{10}(M(sun))$ for morphological parameter tests. The corresponding rules between the classification results and the physical properties of galaxies on multiple parameter surfaces are consistent with the existing evolution model. Our method has demonstrated the feasibility of using large model encoding to classify galaxy morphology, which not only improves the efficiency of galaxy morphology classification, but also saves time and manpower. Furthermore, in comparison to the original UML model, the enhanced classification performance is more evident in qualitative analysis and has successfully surpassed a greater number of parameter tests.

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arXiv2024

Preparation for CSST: Star-galaxy Classification using a Rotationally Invariant Supervised Machine Learning Method

Most existing star-galaxy classifiers depend on the reduced information from catalogs, necessitating careful data processing and feature extraction. In this study, we employ a supervised machine learning method (GoogLeNet) to automatically classify stars and galaxies in the COSMOS field. Unlike traditional machine learning methods, we introduce several preprocessing techniques, including noise reduction and the unwrapping of denoised images in polar coordinates, applied to our carefully selected samples of stars and galaxies. By dividing the selected samples into training and validation sets in an 8:2 ratio, we evaluate the performance of the GoogLeNet model in distinguishing between stars and galaxies. The results indicate that the GoogLeNet model is highly effective, achieving accuracies of 99.6% and 99.9% for stars and galaxies, respectively. Furthermore, by comparing the results with and without preprocessing, we find that preprocessing can significantly improve classification accuracy (by approximately 2.0% to 6.0%) when the images are rotated. In preparation for the future launch of the China Space Station Telescope (CSST), we also evaluate the performance of the GoogLeNet model on the CSST simulation data. These results demonstrate a high level of accuracy (approximately 99.8%), indicating that this model can be effectively utilized for future observations with the CSST.

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