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Accurate photometric redshift (photo- z) estimation requires support from multiband 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 per cent, 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. [ABSTRACT FROM AUTHOR]

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 colour 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 catalogue 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 |$\rm \eta =2.045\ \hbox{per cent}$|⁠ , significantly better than the values of |$\rm \sigma _{NMAD}=0.043$| and |$\rm \eta =6.45\ \hbox{per cent}$| 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. [ABSTRACT FROM AUTHOR]

The VLT Survey Telescope (VST) Optical Imaging of the CDFS and ES1 Fields Survey, in synergy with the SUDARE survey, is a deep optical ugri imaging of the CDFS and ES1 fields using the VST. The observations for the CDFS field comprise about 4.38 deg2 down to r ∼ 26 mag. The total on-sky time spans over 4 yr in this field, distributed over four adjacent sub-fields. In this paper, we use the multiepoch r -band imaging data to measure the variability of the detected objects and search for transients. We perform careful astrometric and photometric calibrations and point spread function modelling. A new method, referring to as differential running-average photometry, is proposed to measure the light curves of the detected objects. With the method, the difference of PSFs between different epochs can be reduced, and the background fluctuations are also suppressed. Detailed uncertainty analysis and detrending corrections on the light curves are performed. We visually inspect the light curves to select variable objects, and present some objects with interesting light curves. Further investigation of these objects in combination with multiband data will be presented in our forthcoming paper. [ABSTRACT FROM AUTHOR]

The VST Optical Imaging of the CDFS and ES1 Fields (VOICE) Survey is a Guaranteed Time programme carried out with the European Southern Observatory (ESO) VLT Survey Telescope (VST) telescope to provide deep optical imaging over two 4 deg2 patches of the sky centred on the Chandra Deep Field South (CDFS) and ES1 as part of the ESO-Spitzer Imaging Extragalactic Survey. We present the cosmic shear measurement over the 4 deg2 covering the CDFS region in the r band using LensFit. Each of the four tiles of 1 deg2 has more than 100 exposures, of which more than 50 exposures passed a series of image quality selection criteria for weak-lensing study. The 5σ limiting magnitude in r band is 26.1 for point sources, which is ≳1 mag deeper than other weak-lensing survey in the literature [e.g. the Kilo Degree Survey (KiDS) at VST]. The photometric redshifts are estimated using the VOICE u, g, r, i together with near-infrared VIDEO data Y, J, H, Ks. The mean redshift of the shear catalogue is 0.87, considering the shear weight. The effective galaxy number density is 16.35 gal arcmin−2, which is nearly twice the one of KiDS. The performance of LensFit on such a deep data set was calibrated using VOICE-like mock image simulations. Furthermore, we have analysed the reliability of the shear catalogue by calculating the star–galaxy cross-correlations, the tomographic shear correlations of two redshift bins and the contaminations of the blended galaxies. As a further sanity check, we have constrained cosmological parameters by exploring the parameter space with Population Monte Carlo sampling. For a flat Λ cold dark matter model, we have obtained $$\Sigma_8=\sigma_8(\Omega_{\rm m}/0.3)^{0.5}=0.68^{+0.11}_{-0.15}$$. [ABSTRACT FROM AUTHOR]