arXiv subjects

Xian-Min Meng

Publications and source records attributed to Xian-Min Meng.

At least 19 recordsLinked to original sources

arXiv2026

Extracting redshifts from 2D slitless spectroscopic images using deep learning for the CSST galaxy survey

Wide-field slitless spectroscopic galaxy surveys, such as the one performed by the upcoming Chinese Space Station Survey Telescope (CSST), are crucial for precision cosmology but present formidable data analysis challenges. Because spectra are dispersed directly onto the detector, they are convolved with the 2-dimensional (2D) spatial morphology, which complicates wavelength calibration and consequently degrades the fidelity of subsequent 1-dimensional (1D) spectral extraction. To overcome these limitations, we present a deep learning framework that extracts redshifts directly from 2D slitless spectral images, bypassing 1D extraction entirely. We construct a realistic mock dataset for the CSST $GV$ and $GI$ band using high-resolution images from HSC-SSP PDR3 and spectral energy distributions (SEDs) from DESI DR1. A Bayesian convolutional neural network implemented by Monte Carlo dropout is employed to map the 2D spectral images to redshift estimations while simultaneously quantifying uncertainties. We find that our model can achieve a precision $σ_{\rm NMAD}=0.0104$ and mean uncertainty $\langle E / (1 + z_{\rm true}) \rangle=0.0155$ for sources with ${\rm SNR}_{GI}\geq1$. For sources with ${\rm SNR}_{GI}$ higher than 3.0, 5.0 and 10.0, $σ_{\rm NMAD}$ can achieve 0.0047, 0.0037 and 0.0024 respectively, matching the redshift precision requirements for studies such as BAO using the CSST slitless spectroscopic surveys. Furthermore, by utilizing spatial augmentations, the network demonstrates resilience to wavelength calibration errors. This work provides a novel and robust pathway for data analysis of next-generation slitless spectroscopic galaxy surveys.

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arXiv2025

Photo-$z$ Estimation with Normalizing Flow

Accurate photometric redshift (photo-$z$) estimation is a key challenge in cosmology, as uncertainties in photo-$z$ directly limit the scientific return of large-scale structure and weak lensing studies, especially in upcoming Stage IV surveys. The problem is particularly severe for faint galaxies with sparse spectroscopic training data. In this work, we introduce nflow-$z$, a novel photo-$z$ estimation method using the powerful machine learning technique of normalizing flow. nflow-$z$ explicitly models the redshift probability distribution conditioned on the observables such as fluxes and colors. We build two nflow-$z$ implementations, dubbed cINN and cNSF, and compare their performance. We demonstrate the effectiveness of nflow-$z$ on several datasets, including a CSST mock, the COSMOS2020 catalog, and samples from DES Y1, SDSS, and DESCaLS. Our evaluation against state-of-the-art algorithms shows that nflow-$z$ performs favorably. For instance, cNSF surpasses Random Forest, Multi-Layer Perceptron, and Convolutional Neutral Network on the CSST mock test. We also achieve a ~30% improvement over official results for the faint DESCaLS sample and outperform conditional Generative Adversarial Network and Mixture Density Network methods on the DES Y1 dataset test. Furthermore, nflow-$z$ is computationally efficient, requiring only a fraction of the computing time of some of the competing algorithms. Our algorithm is particularly effective for the faint sample with sparse training data, making it highly suitable for upcoming Stage IV surveys.

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arXiv2025

LEO Satellite Track Correction for CSST Multi-Band Imaging Data

Low Earth Orbit satellite (LEOsat) mega-constellations are considered to be an unavoidable source of contamination for survey observations to be carried out by the China Space Station Telescope (CSST) over the next decade. This study reconstructs satellite trail profiles based on simulated parameters, including brightness levels and orbital altitudes, in combination with multi-band simulated images. Compared to our previous work, the simulated images in this study more accurately replicate the realistic observational conditions of CSST and extend beyond single-band analysis. Variations in LEOsat trail brightness, source brightness, background noise, and source density across different bands result in differing levels of accuracy in trail reconstruction and subsequently affect the reliability of photometric measurements. The reconstructed trail profiles are subsequently applied to correct the contaminated regions. Simulation results reveal varying levels of contamination effects across different bands following LEOsat trail correction, including both reconstruction and subtraction. To evaluate the effectiveness of the correction, we quantified the fraction of affected sources using two metrics: (1) magnitude errors greater than 0.01 mag attributable to LEOsats, and (2) LEOsat-induced noise exceeding 10% of other noise contributions. Following trail repair, the analysis reveals a reduction of over 50% in the fraction of affected sources in the NUV band for both 550 km and 1200 km altitudes, assuming a maximum brightness of 7 in the V band. In the i band, the reduction exceeds 30%. The degree of improvement varies across spectral bands, and depends on both satellite altitude and the adopted brightness model.

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arXiv2025

Impact of Low-Earth Orbit Satellites on the China Space Station Telescope Observations

It is projected that more than 100,000 communication satellites will be deployed in Low-Earth Orbit (LEO) over the next decade. These LEO satellites (LEOsats) will be captured frequently by the survey camera onboard the China Space Station Telescope (CSST), contaminating sources in the images. As such, it is necessary to assess the impact of LEOsats on CSST survey observations. We use the images taken by the Hubble Space Telescope (HST) in its F814W band to simulate $i$-band images for the CSST. The simulation results indicate that LEOsats at higher altitudes cause more contamination than those at lower altitudes. If 100,000 LEOsats are deployed at altitudes between 550 km and 1200 km with a 53-degree orbital inclination, the fraction of contaminated sources in a 150-s exposure image would remain below 0.50%. For slitless spectroscopic images, the contaminated area is expected to be below 1.50%. After removing the LEOsat trails, the residual photon noise contributes to relative photometric errors that exceed one-tenth of the total error budget in approximately 0.10% of all sources. Our investigation shows that even though LEOsats are unavoidable in CSST observations, they only have a minor impact on samples extracted from the CSST survey.

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arXiv2025

GalaxyGenius: Mock galaxy image generator for various telescopes from hydrodynamical simulations

We introduce GalaxyGenius, a Python package designed to produce synthetic galaxy images tailored to different telescopes based on hydrodynamical simulations. Its implementation will support and advance research on galaxies in the era of large-scale sky surveys. The package comprises three main modules: data preprocessing, ideal data cube generation, and mock observation. Specifically, the preprocessing module extracts necessary properties of star and gas particles for a selected subhalo from hydrodynamical simulations and creates the execution file for the following radiative transfer procedure. Subsequently, building on the above information, the ideal data cube generation module executes a widely used radiative transfer project, specifically the SKIRT, to perform the SED assignment for each particle and the radiative transfer procedure to produce an IFU-like ideal data cube. Lastly, the mock observation module takes the ideal data cube and applies the throughputs of aiming telescopes, while also incorporating the relevant instrumental effects, point spread functions (PSFs), and background noise to generate the required mock observational images of galaxies. To showcase the outcomes of GalaxyGenius, we created a series of mock images of galaxies based on the IllustrisTNG and EAGLE simulations for both space and ground-based surveys, spanning ultraviolet (UV) to infrared (IR) wavelength coverage, including CSST, Euclid, HST, JWST, Roman, and HSC. GalaxyGenius offers a flexible framework to generate mock galaxy images with customizable recipes. These generated images can serve as valuable references for verifying and validating new approaches in astronomical research. They can also serve as training sets for relevant studies using deep learning in cases where real observational data are insufficient.

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arXiv2024

Accurately Estimating Redshifts from CSST Slitless Spectroscopic Survey using Deep Learning

Chinese Space Station Telescope (CSST) has the capability to conduct slitless spectroscopic survey simultaneously with photometric survey. The spectroscopic survey will measure slitless spectra, potentially providing more accurate estimations of galaxy properties, particularly redshifts, compared to using broadband photometry. CSST relies on these accurate redshifts to perform baryon acoustic oscilliation (BAO) and other probes to constrain the cosmological parameters. However, due to low resolution and signal-to-noise ratio of slitless spectra, measurement of redshifts is significantly challenging.} In this study, we employ a Bayesian neural network (BNN) to assess the accuracy of redshift estimations from slitless spectra anticipated to be observed by CSST. The simulation of slitless spectra is based on real observational data from the early data release of the Dark Energy Spectroscopic Instrument (DESI-EDR) and the 16th data release of the Baryon Oscillation Spectroscopic Survey (BOSS-DR16), combined with the 9th data release of the DESI Legacy Survey (DESI LS DR9). The BNN is constructed employing transfer learning technique, by appending two Bayesian layers after a convolutional neural network (CNN), leveraging the features learned from the slitless spectra and corresponding redshifts. Our network can provide redshift estimates along with corresponding uncertainties, achieving an accuracy of $σ_{\rm NMAD} = 0.00063$, outlier percentage $η=0.92\%$ and weighted mean uncertainty $\bar{E} = 0.00228$. These results successfully fulfill the requirement of $σ_{\rm NMAD} < 0.005$ for BAO and other studies employing CSST slitless spectroscopic surveys.

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arXiv2024

Imputation of Missing Photometric Data and Photometric Redshift Estimation for CSST

Accurate photometric redshift (photo-$z$) estimation requires support from multi-band observational data. However, in the actual process of astronomical observations and data processing, some sources may have missing observational data in certain bands for various reasons. This could greatly affect the accuracy and reliability of photo-$z$ estimation for these sources, and even render some estimation methods unusable. The same situation may exist for the upcoming Chinese Space Station Telescope (CSST). In this study, we employ a deep learning method called Generative Adversarial Imputation Networks (GAIN) to impute the missing photometric data in CSST, aiming to reduce the impact of data missing on photo-$z$ estimation and improve estimation accuracy. Our results demonstrate that using the GAIN technique can effectively fill in the missing photometric data in CSST. Particularly, when the data missing rate is below 30\%, the imputation of photometric data exhibits high accuracy, with higher accuracy in the $g$, $r$, $i$, $z$, and $y$ bands compared to the $NUV$ and $u$ bands. After filling in the missing values, the quality of photo-$z$ estimation obtained by the widely used Easy and Accurate Zphot from Yale (EAZY) software is notably enhanced. Evaluation metrics for assessing the quality of photo-$z$ estimation, including the catastrophic outlier fraction ($f_{out}$), the normalized median absolute deviation ($\rm {σ_{NMAD}}$), and the bias of photometric redshift ($bias$), all show some degree of improvement. Our research will help maximize the utilization of observational data and provide a new method for handling sample missing values for applications that require complete photometry data to produce results.

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arXiv2023

Estimating Photometric Redshift from Mock Flux for CSST Survey by using Weighted Random Forest

Accurate estimation of photometric redshifts (photo-$z$) is crucial in studies of both galaxy evolution and cosmology using current and future large sky surveys. In this study, we employ Random Forest (RF), a machine learning algorithm, to estimate photo-$z$ and investigate the systematic uncertainties affecting the results. Using galaxy flux and color as input features, we construct a mapping between input features and redshift by using a training set of simulated data, generated from the Hubble Space Telescope Advanced Camera for Surveys (HST-ACS) and COSMOS catalogue, with the expected instrumental effects of the planned China Space Station Telescope (CSST). To improve the accuracy and confidence of predictions, we incorporate inverse variance weighting and perturb the catalog using input feature errors. Our results show that weighted RF can achieve a photo-$z$ accuracy of $\rm σ_{NMAD}=0.025$ and an outlier fraction of $\rm η=2.045\%$, significantly better than the values of $\rm σ_{NMAD}=0.043$ and $\rm η=6.45\%$ obtained by the widely used Easy and Accurate Zphot from Yale (EAZY) software which uses template-fitting method. Furthermore, we have calculated the importance of each input feature for different redshift ranges and found that the most important input features reflect the approximate position of the break features in galaxy spectra, demonstrating the algorithm's ability to extract physical information from data. Additionally, we have established confidence indices and error bars for each prediction value based on the shape of the redshift probability distribution function, suggesting that screening sources with high confidence can further reduce the outlier fraction.

astro-ph.CO↗

arXiv2022

Photometric redshift estimates using Bayesian neural networks in the CSST survey

Galaxy photometric redshift (photo-$z$) is crucial in cosmological studies, such as weak gravitational lensing and galaxy angular clustering measurements. In this work, we try to extract photo-$z$ information and construct its probability distribution function (PDF) using the Bayesian neural networks (BNN) from both galaxy flux and image data expected to be obtained by the China Space Station Telescope (CSST). The mock galaxy images are generated from the Advanced Camera for Surveys of Hubble Space Telescope ($HST$-ACS) and COSMOS catalog, in which the CSST instrumental effects are carefully considered. And the galaxy flux data are measured from galaxy images using aperture photometry. We construct Bayesian multilayer perceptron (B-MLP) and Bayesian convolutional neural network (B-CNN) to predict photo-$z$ along with the PDFs from fluxes and images, respectively. We combine the B-MLP and B-CNN together, and construct a hybrid network and employ the transfer learning techniques to investigate the improvement of including both flux and image data. For galaxy samples with SNR$>$10 in $g$ or $i$ band, we find the accuracy and outlier fraction of photo-$z$ can achieve $σ_{\rm NMAD}=0.022$ and $η=2.35\%$ for the B-MLP using flux data only, and $σ_{\rm NMAD}=0.022$ and $η=1.32\%$ for the B-CNN using image data only. The Bayesian hybrid network can achieve $σ_{\rm NMAD}=0.021$ and $η=1.23\%$, and utilizing transfer learning technique can improve results to $σ_{\rm NMAD}=0.019$ and $η=1.17\%$, which can provide the most confident predictions with the lowest average uncertainty.

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arXiv2022

Extracting Photometric Redshift from Galaxy Flux and Image Data using Neural Networks in the CSST Survey

The accuracy of galaxy photometric redshift (photo-$z$) can significantly affect the analysis of weak gravitational lensing measurements, especially for future high-precision surveys. In this work, we try to extract photo-$z$ information from both galaxy flux and image data expected to be obtained by China Space Station Telescope (CSST) using neural networks. We generate mock galaxy images based on the observational images from the Advanced Camera for Surveys of Hubble Space Telescope (HST-ACS) and COSMOS catalogs, considering the CSST instrumental effects. Galaxy flux data are then measured directly from these images by aperture photometry. The Multi-Layer Perceptron (MLP) and Convolutional Neural Network (CNN) are constructed to predict photo-$z$ from fluxes and images, respectively. We also propose to use an efficient hybrid network, which combines MLP and CNN, by employing transfer learning techniques to investigate the improvement of the result with both flux and image data included. We find that the photo-$z$ accuracy and outlier fraction can achieve $σ_{\rm NMAD} = 0.023$ and $η= 1.43\%$ for the MLP using flux data only, and $σ_{\rm NMAD} = 0.025$ and $η= 1.21\%$ for the CNN using image data only. The result can be further improved in high efficiency as $σ_{\rm NMAD} = 0.020$ and $η= 0.90\%$ for the hybrid transfer network. These approaches result in similar galaxy median and mean redshifts ~0.8 and 0.9, respectively, for the redshift range from 0 to 4. This indicates that our networks can effectively and properly extract photo-$z$ information from the CSST galaxy flux and image data.

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arXiv2021

Spectroscopic and Photometric Redshift Estimation by Neural Networks For the China Space Station Optical Survey (CSS-OS)

The estimation of spectroscopic and photometric redshifts (spec-z and photo-z) is crucial for future cosmological surveys. It can directly affect several powerful measurements of the Universe, e.g. weak lensing and galaxy clustering. In this work, we explore the accuracies of spec-z and photo-z that can be obtained in the China Space Station Optical Surveys (CSS-OS), which is a next-generation space survey, using neural networks. The 1-dimensional Convolutional Neural Networks (1-d CNN) and Multi-Layer Perceptron (MLP, one of the simplest forms of Artificial Neural Network) are employed to derive the spec-z and photo-z, respectively. The mock spectral and photometric data used for training and testing the networks are generated based on the COSMOS catalog. The networks have been trained with noisy data by creating Gaussian random realizations to reduce the statistical effects, resulting in similar redshift accuracy for both high-SNR (signal to noise ratio) and low-SNR data. The probability distribution functions (PDFs) of the predicted redshifts are also derived via Gaussian random realizations of the testing data, and then the best-fit redshifts and 1-sigma errors also can be obtained. We find that our networks can provide excellent redshift estimates with accuracies ~0.001 and 0.01 on spec-z and photo-z, respectively. Compared to existing photo-z codes, our MLP has similar accuracy but is more efficient in the training process. The fractions of catastrophic redshifts or outliers can be dramatically suppressed comparing to the ordinary template-fitting method. This indicates that the neural network method is feasible and powerful for spec-z and photo-z estimations in future cosmological surveys.

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arXiv2019

An $Hα$ Imaging Survey of the all (Ultra-)Luminous Infrared Galaxies at $Dec. \ge -30^{\circ}$ in the GOALS Sample

This paper presents the result of $Hα$ imaging for luminous infrared galaxies (LIRGs) and ultra-luminous infrared galaxies (ULIRGs). \textbf{It is } a \textbf{complete subsample of Great Observatories All-sky LIRG Survery (GOALS) with $Dec. \ge -30^{\circ}$}, \textbf{and} consists 148 galaxies with $log(L_{IR}/L_{\odot}) \ge 11.0$. All the $Hα$ images were carried out using the 2.16-m telescope \textbf{at the Xinglong Station of the} National Astronomy Observatories, Chinese Academy of Sciences\textbf{ (NAOC),} during the year from 2006 to 2009. We obtained pure $Hα$ luminosity for each galaxy and corrected the luminosity for $[NII]$ emission, filter transmission and extinction. We also classified these galaxies based on their morphology and interaction. We found that the distribution of star-forming \textbf{regions} in these galaxies is related to this classification. As the merging process advanced, these galaxies tend to have a more compact distribution of star-forming region, higher \textbf{$L_{IR}$} and warmer IR-color ($f_{60}/f_{100}$). \textbf{These} results imply that the degree of dynamical disturbance plays an important role in determining the distribution of star-forming region.

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arXiv2019

A Systematic Analysis of Stellar Populations in the Host Galaxies of SDSS Type I QSOs

We investigate the relationship between host galaxies' stellar content and active galactic nuclei (AGN) for optically selected QSOs with z$<$0.5. There are total 82 QSOs we select from Sloan Digital Sky Survey (SDSS) . These 82 QSOs both have Wide-field Infrared Survey Explorer (WISE) data and measurable stellar content. With the help of the stellar population synthesis code STARLIGHT, we determine the luminosity fraction of AGN ,stellar population ages and star-formation history (SFH) of host galaxies. We find out there is a correlation between the star formation history and AGN property which suggests a possible delay from star formation to AGN. This probably indicates that the AGN activity correlate with the star formation activity which consistent with a co-evolution scheme for black hole and host galaxies.

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arXiv2018

Testing photometric redshift measurements with filter definition of the Chinese Space Station Optical Survey (CSS-OS)

The Chinese Space Station Optical Survey (CSS-OS) is a major science project of the Space Application System of the China Manned Space Program. This survey is planned to perform both photometric imaging and slitless spectroscopic observations, and it will focus on different cosmological and astronomical goals. Most of these goals are tightly dependent on the accuracy of photometric redshift (photo-z) measurement, especially for the weak gravitational lensing survey as a main science driver. In this work, we assess if the current filter definition can provide accurate photo-z measurement to meet the science requirement. We use the COSMOS galaxy catalog to create a mock catalog for the CSS-OS. We compare different photo-z codes and fitting methods that using the spectral energy distribution (SED) template-fitting technique, and choose to use a modified LePhare code in photo-z fitting process. Then we investigate the CSS-OS photo-z accuracy in certain ranges of filter parameters, such as band position, width, and slope. We find that the current CSS-OS filter definition can achieve reasonably good photo-z results with sigma_z~0.02 and outlier fraction ~3%.

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arXiv2016

NUV Star Catalogue from the Lunar-based Ultraviolet Telescope Survey. First Release

We present a star catalogue extracted from the Lunar-based Ultraviolet Telescope (LUT) survey program. LUT's observable sky area is a circular belt around the Moon's north pole, and the survey program covers a preferred area for about 2400 deg$^2$ which includes a region of the Galactic plane. The data is processed with an automatic pipeline which copes with stray light contamination, artificial sources, cosmic rays, flat field calibration, photometry and so on. In the first release version, the catalogue provides high confidence sources which have been cross-identified with Tycho-2 catalogue. All the sources have signal-to-noise ratio larger than 5, and the corresponding magnitude limit is typically 14.4 mag, which can be deeper as ~16 mag if the stray light contamination is in the lowest level. A total number of 86,467 stars are recorded in the catalogue. The full catalogue in electronic form is available on line.

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arXiv2015

Data Processing Pipeline for Pointing Observations of Lunar-based Ultraviolet Telescope

We describe the data processing pipeline developed to reduce the pointing observation data of Lunar-based Ultraviolet Telescope (LUT), which belongs to the Chang'e-3 mission of the Chinese Lunar Exploration Program. The pointing observation program of LUT is dedicated to monitor variable objects in a near-ultraviolet (245-345 nm) band. LUT works in lunar daytime for sufficient power supply, so some special data processing strategies have been developed for the pipeline. The procedures of the pipeline include stray light removing, astrometry, flat fielding employing superflat technique, source extraction and cosmic rays rejection, aperture and PSF photometry, aperture correction, and catalogues archiving, etc. It has been intensively tested and works smoothly with observation data. The photometric accuracy is typically ~0.02 mag for LUT 10 mag stars (30 s exposure), with errors come from background noises, residuals of stray light removing, and flat fielding related errors. The accuracy degrades to be ~0.2 mag for stars of 13.5 mag which is the 5σ detection limit of LUT.

astro-ph.IM↗

arXiv2011

Star Formation Properties in Barred Galaxies(SFB). I. Ultraviolet-to-Infrared Imaging and Spectroscopic Studies of NGC 7479

Large-scale bars and minor mergers are important drivers for the secular evolution of galaxies. Based on ground-based optical images and spectra as well as ultraviolet data from the Galaxy Evolution Explorer and infrared data from the Spitzer Space Telescope, we present a multi-wavelength study of star formation properties in the barred galaxy NGC 7479, which also has obvious features of a minor merger. Using various tracers of star formation, we find that under the effects of both a stellar bar and a minor merger, star formation activity mainly takes place along the galactic bar and arms, while the star formation rate changes from the bar to the disk. With the help of spectral synthesis, we find that strong star formation took place in the bar region about 100 Myr ago, and the stellar bar might have been $\sim$10 Gyr old. By comparing our results with the secular evolutionary scenario from Jogee et al., we suggest that NGC 7479 is possibly in a transitional stage of secular evolution at present, and it may eventually become an earlier type galaxy or a luminous infrared galaxy. We also note that the probable minor merger event happened recently in NGC 7479, and we find two candidates for minor merger remnants.

astro-ph.CO↗

arXiv2011

The Diagnostics and Possible Evolution in Active Galactic Nuclei Associated With Starburst Galaxies

We present a large sample which contains 45 Seyfert 1 galaxies (Sy1s), 46 hidden broad-line region (HBLR) Seyfert 2 galaxies (Sy2s), 57 non-HBLR Sy2s, and 22 starburst galaxies to distinguish their properties and to seek the possible evolution of active galactic nuclei (AGNs) and starburst galaxies. We show that (1) using a plot of [O III] ł5007/H/alpha versus [N II] ł6584/H/alpha of standard optical spectral diagnostic diagrams, we find that a equation can separate well non-HBLR Sy2s and HBLR Sy2s; (2) the emission-line ratios and both the combination of the ratios and polycyclic aromatic hydrocarbon (PAH) strength are utilized effectively to separate starburst galaxies and Seyfert galaxies; (3) we compare a number of quantities from the data and confirm well the separations with statistics; (4) on the basis of statistics, we suggest that HBLR Sy2s may be the counterparts of Sy1s at edge-on orientation. In addition, we discuss the possibility of starburst galaxies evolving to non-HBLR Sy2s and HBLR Sy2s and then evolving to Sy1s based on the statistical analysis.

astro-ph.HE↗