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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 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 catalogues, considering the CSST instrumental effects. Galaxy flux data are then measured directly from these images by aperture photometry. The multilayer 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 the MLP and CNN, by employing the 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 σNMAD = 0.023 and |$\eta = 1.43{{\ \rm per\ cent}}$| for the MLP using flux data only, and σNMAD = 0.025 and |$\eta = 1.21{{\ \rm per\ cent}}$| for the CNN using image data only. The result can be further improved in high efficiency as σNMAD = 0.020 and |$\eta = 0.90{{\ \rm per\ cent}}$| 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. [ABSTRACT FROM AUTHOR]

The question of radio dichotomy in active galactic nuclei (AGNs) is still in debate, even though it has been discussed for more than 40 years. In order to solve this old riddle, we collect a sample of AGNs with optical B band and radio 6 cm wavelength data to analyze the radio loudness log  R. Our results indicate a separation of log  R = 〈1.37 ± 0.02〉 between radio-loud (RL) AGNs and radio-quiet (RQ) AGNs, suggesting the existence of an RL/RQ dichotomy. For the first time, we suggest combining radio luminosity and radio loudness as a double criterion to divide AGNs into RLs and RQs to avoid misclassification problems that may happen in the single-criterion scenario; we propose the double-criterion dividing line log  L 6 cm = −2.7log  R + 44.3 by using a machine-learning method. In addition, the key point of the RL/RQ dichotomy is the origin of radio emission for the two classes, and we suggest the radio emission from RLs and RQs share the same origin, e.g. jets and mini-jets (aborted-jet or outflow), through a correlation study between radio 6 cm luminosity and optical B -band luminosity. [ABSTRACT FROM AUTHOR]

We present a sample of 16 likely strong gravitational lenses identified in the VST Optical Imaging of the CDFS and ES1 fields (VOICE survey) using convolutional neural networks (CNNs). We train two different CNNs on composite images produced by superimposing simulated gravitational arcs on real Luminous Red Galaxies observed in VOICE. Specifically, the first CNN is trained on single-band images and more easily identifies systems with large Einstein radii, while the second one, trained on composite RGB images, is more accurate in retrieving systems with smaller Einstein radii. We apply both networks to real data from the VOICE survey, taking advantage of the high limiting magnitude (26.1 in the r band) and low PSF FWHM (0.8 arcsec in the r band) of this deep survey. We analyse ∼21 200 images with magr < 21.5, identifying 257 lens candidates. To retrieve a high-confidence sample and to assess the accuracy of our technique, nine of the authors perform a visual inspection. Roughly 75 per cent of the systems are classified as likely lenses by at least one of the authors. Finally, we assemble the LIVE sample (Lenses In VoicE) composed by the 16 systems passing the chosen grading threshold. Three of these candidates show likely lensing features when observed by the Hubble Space Telescope. This work represents a further confirmation of the ability of CNNs to inspect large samples of galaxies searching for gravitational lenses. These algorithms will be crucial to exploit the full scientific potential of forthcoming surveys with the Euclid satellite and the Vera Rubin Observatory. [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]

We present the cluster mass-richness scaling relation calibrated by a weak lensing analysis of ≿ 18 000 galaxy cluster candidates in the Canada-France-Hawaii Telescope Lensing Survey (CFHTLenS). Detected using the 3D-Matched-Filter (MF) cluster-finder of Milkeraitis et al., these cluster candidates span a wide range of masses, from the small group scale up to ~1015M⊙, andredshifts 0.2 ≲ z ≲ <0.9. The total significance of the stacked shear measurement amounts to 54σ. We compare cluster masses determined using weak lensing shear and magnification, finding the measurements in individual richness bins to yield 1σ compatibility, but with magnification estimates biased low. This first direct mass comparison yields important insights for improving the systematics handling of future lensing magnification work. In addition, we confirm analyses that suggest cluster miscentring has an important effect on the observed 3D-MF halo profiles, and we quantify this by fitting for projected cluster centroid offsets, which are typically ~0.4 arcmin. We bin the cluster candidates as a function of redshift, finding similar cluster masses and richness across the full range up to z ~ 0.9. We measure the 3D-MF mass-richness scaling relation M200 = M0 (N200/20)β. We find a normalization M0 ~ (2.7-0.4+0.5) x 1013M⊙, and a logarithmic slope of β ~ 1.4 ± 0.1, both of which are in 1σ agreement with results from the magnification analysis. We find no evidence for a redshift dependence of the normalization. The CFHTLenS 3D-MF cluster catalogue is now available at cfhtlens.org. [ABSTRACT FROM AUTHOR]

Higher order, non-Gaussian aspects of the large-scale structure carry valuable information on structure formation and cosmology, which is complementary to second-order statistics. In this work, we measure second- and third-order weak-lensing aperture-mass moments from the Canada–France–Hawaii Lensing Survey (CFHTLenS) and combine those with cosmic microwave background (CMB) anisotropy probes. The third moment is measured with a significance of 2σ. The combined constraint on Σ8 = σ8(Ωm/0.27)α is improved by 10 per cent, in comparison to the second-order only, and the allowed ranges for Ωm and σ8 are substantially reduced. Including general triangles of the lensing bispectrum yields tighter constraints compared to probing mainly equilateral triangles. Second- and third-order CFHTLenS lensing measurements improve Planck CMB constraints on Ωm and σ8 by 26 per cent for flat Λ cold dark matter. For a model with free curvature, the joint CFHTLenS–Planck result is Ωm = 0.28 ± 0.02 (68 per cent confidence), which is an improvement of 43 per cent compared to Planck alone. We test how our results are potentially subject to three astrophysical sources of contamination: source-lens clustering, the intrinsic alignment of galaxy shapes, and baryonic effects. We explore future limitations of the cosmological use of third-order weak lensing, such as the non-linear model and the Gaussianity of the likelihood function. [ABSTRACT FROM AUTHOR]