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China 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 redshift, compared to broadband photometry. However, due to low-resolution and signal-to-noise ratio of slitless spectra, measurement of these properties 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 slitless spectra are simulated based on real data from the early data release of the Dark Energy Spectroscopic Instrument (DESI-EDR) and the 16th data release of the Baryon Oscillaton Spectroscopic Survey (BOSS-DR16), combining the 9th data release of the DESI Legacy Survey (DESI LS DR9). The BNN provides redshifts estimates along with corresponding uncertainties, achieving an accuracy of $\sigma_{\rm NMAD} = 0.00063$, outlier percentage $\eta=0.92\%$ and weighted mean uncertainty $\bar{E} = 0.00228$. These results successfully meet the requirement for cosmological studies using slitless spectra from CSST., Comment: 12 pages, 12 figures, submitted to ApJ, comments are welcome

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 ($\rm {\sigma_{NMAD}}$), 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.

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 $\rm \sigma_{NMAD}=0.025$ and an outlier fraction of $\rm \eta=2.045\%$, significantly better than the values of $\rm \sigma_{NMAD}=0.043$ and $\rm \eta=6.45\%$ 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.

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 $\sigma_{\rm NMAD}=0.022$ and $\eta=2.35\%$ for the B-MLP using flux data only, and $\sigma_{\rm NMAD}=0.022$ and $\eta=1.32\%$ for the B-CNN using image data only. The Bayesian hybrid network can achieve $\sigma_{\rm NMAD}=0.021$ and $\eta=1.23\%$, and utilizing transfer learning technique can improve results to $\sigma_{\rm NMAD}=0.019$ and $\eta=1.17\%$, which can provide the most confident predictions with the lowest average uncertainty., Comment: 22 pages, 12 figures, 3 tables, accepted for publication in RAA

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 $\sigma_{\rm NMAD} = 0.023$ and $\eta = 1.43\%$ for the MLP using flux data only, and $\sigma_{\rm NMAD} = 0.025$ and $\eta = 1.21\%$ for the CNN using image data only. The result can be further improved in high efficiency as $\sigma_{\rm NMAD} = 0.020$ and $\eta = 0.90\%$ 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., Comment: 11 pages, 8 figures, 2 tables. Accepted for publication in MNRAS

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., Comment: 14 pages, 13 figures, 2 tables. Accepted for publication in ApJ

This paper presents the result of $H\alpha$ 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 $H\alpha$ 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 $H\alpha$ 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.

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.

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%., Comment: 14 pages, 12 figures, 3 tables. Accepted for publication in MNRAS

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., Comment: 13 pages, 8 figures, 3 tables, accepted for publication in Research in Astronomy and Astrophysics (RAA)

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 5{\sigma} detection limit of LUT., Comment: 10 pages, 7 figures, 4 tables. Minor changes and some expounding words added. Version accepted for publication in Astrophysics and Space Science (Ap&SS)

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., Comment: 33 pages, 10 figures, 4 tables; accepted for publication in AJ, 142, 38

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] {\l}5007/H/alpha versus [N II] {\l}6584/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., Comment: 18 pages, 9 figures, 7 tables, Accepted by ApJS

Brightest Cluster Galaxies (BCGs) are mostly elliptical galaxies and very rarely have prominent star formation. We found that five out of 8,812 BCGs are E+A (i.e. post-starburst) galaxies, having the H$\delta$~absorption line with an equivalent width $>2.5\AA$ and no distinct emission lines in [O II] and H$\alpha$. The E+A features we identified from the BCGs for the first time are not as significant as those in general galaxies, indicating that historically the star formation were not very violent., Comment: 8 pages, 2 figures and 1 table; A Brief Report in SCIENCE CHINA Physics, Mechanics & Astronomy (ScChG)

We present a stellar population synthesis study of a type I luminous infrared galaxy (LIRG): IRAS F13308+5946. It is a quasar with absolute magnitude Mi = -22.56 and has a spectral feature of a Seyfert 1.5 galaxy. Optical images show characteristics of later stages of a merger. With the help of the stellar synthesis code STARLIGHT (Cid Fernandes et al. 2005) and both Calzetti et al. (2000) and Leitherer et al.'s (2002) extinction curves, we estimate the past infrared (IR) luminosities of the host galaxy and find it may have experienced an ultraluminous infrared galaxy (ULIRG) phase for nearly 300 Myr, so this galaxy has probably experienced a type I ULIRG phase. Both nuclear starburst and active galactic nuclei (AGN) contribute to the present IR luminosity budget, and starburst contributes ~70%. The mass of supermassive black-hole (SMBH) is M_BH = 1.8*10^8 M_sun and the Eddington ratio L_bol/L_Edd is 0.12, which both approximate to typical values of PG QSOs. These results indicate that IRAS F13308+5946 is probably at the transitional phase from a type I ULIRG to a classical QSO., Comment: 11 pages, 10 figures, Accepted for publication in ApJ