分类： 天文学 >> 天文学 提交时间： 2023-02-19
摘要： Anisotropies of the cosmic optical background (COB) and cosmic near-IR background (CNIRB) are capable of addressing some of the key questions in cosmology and astrophysics. In this work, we measure and analyze the angular power spectra of the simulated COB and CNIRB in the ultra-deep field of the China Space Station Telescope (CSST-UDF). The CSST-UDF covers about 9 square degrees, with magnitude limits ~28.3, 28.2, 27.6, 26.7 AB mag for point sources with 5-sigma detection in the r (0.620 um), i (0.760 um), z (0.915 um), and y (0.965 um) bands, respectively. According to the design parameters and scanning pattern of the CSST, we generate mock data, merge images and mask the bright sources in the four bands. We obtain four angular power spectra from l=200 to 2,000,000 (from arcsecond to degree), and fit them with a multi-component model including intrahalo light (IHL) using the Markov chain Monte Carlo (MCMC) method. We find that the signal-to-noise ratio (SNR) of the IHL is larger than 8 over the range of angular scales that are useful for astrophysical studies (l~10,000-400,000). Comparing to previous works, the constraints on the model parameters are improved by factors of 3~4 in this study, which indicates that the CSST-UDF survey can be a powerful probe on the cosmic optical and near-IR backgrounds.
分类： 天文学 >> 天文学 提交时间： 2023-02-19
摘要： We propose a novel method to detect cosmic magnification signals by cross-correlating foreground convergence fields constructed from galaxy shear measurements with background galaxy positional distributions, namely shear-number density correlation. We apply it to the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) survey data. With 27 non-independent data points and their full covariance, $\chi_0^2\approx 34.1$ and $\chi_T^2\approx 24.0$ with respect to the null and the cosmological model with the parameters from HSC shear correlation analyses in Hamana et al. 2020 (arXiv:1906.06041), respectively. The Bayes factor of the two is $\log_{10}B_{T0}\approx 2.2$ assuming equal model probabilities of null and HSC cosmology, showing a clear detection of the magnification signals. Theoretically, the ratio of the shear-number density and shear-shear correlations can provide a constraint on the effective multiplicative shear bias $\bar m$ using internal data themselves. We demonstrate the idea with the signals from our HSC-SSP mock simulations and rescaling the statistical uncertainties to a survey of $15000\deg^2$. For two-bin analyses with background galaxies brighter than $m_{lim}=23$, the combined analyses lead to a forecasted constraint of $\sigma(\bar m) \sim 0.032$, $2.3$ times tighter than that of using the shear-shear correlation alone. Correspondingly, $\sigma(S_8)$ with $S_8=\sigma_8(\Omega_\mathrm{m}/0.3)^{0.5}$ is tightened by $\sim 2.1$ times. Importantly, the joint constraint on $\bar m$ is nearly independent of cosmological parameters. Our studies therefore point to the importance of including the shear-number density correlation in weak lensing analyses, which can provide valuable consistency tests of observational data, and thus to solidify the derived cosmological constraints.
分类： 天文学 >> 天文学 提交时间： 2023-02-19
Shoucheng Wang
Bingqiu Chen
Jun Ma
Qian Long
Haibo Yuan
Dezi Liu
Zhimin Zhou
Wei Liu
Jiamin Chen
Zizhao He
摘要： Context.Identification of new star cluster candidates in M31 is fundamental for the study of the M31 stellar cluster system. The machine-learning method convolutional neural network (CNN) is an efficient algorithm for searching for new M31 star cluster candidates from tens of millions of images from wide-field photometric surveys. Aims.We search for new M31 cluster candidates from the high-quality $g$- and $i$-band images of 21,245,632 sources obtained from the Pan-Andromeda Archaeological Survey (PAndAS) through a CNN. Methods.We collected confirmed M31 clusters and noncluster objects from the literature as our training sample. Accurate double-channel CNNs were constructed and trained using the training samples. We applied the CNN classification models to the PAndAS $g$- and $i$-band images of over 21 million sources to search new M31 cluster candidates. The CNN predictions were finally checked by five experienced human inspectors to obtain high-confidence M31 star cluster candidates. Results.After the inspection, we identified a catalogue of 117 new M31 cluster candidates. Most of the new candidates are young clusters that are located in the M31 disk. Their morphology, colours, and magnitudes are similar to those of the confirmed young disk clusters. We also identified eight globular cluster candidates that are located in the M31 halo and exhibit features similar to those of confirmed halo globular clusters. The projected distances to the M31 centre for three of them are larger than 100\,kpc.
分类： 天文学 >> 天文学 提交时间： 2024-05-10 合作期刊: 《Research in Astronomy and Astrophysics》
摘要： We have developed a novel method for co-adding multiple under-sampled images that combines the iteratively reweighted least squares and divide-and-conquer algorithms. Our approach not only allows for the anti-aliasing of the images but also enables Point-Spread Function (PSF) deconvolution, resulting in enhanced restoration of extended sources, the highest peak signal-to-noise ratio, and reduced ringing artefacts. To test our method, we conducted numerical simulations that replicated observation runs of the China Space Station Telescope/ the VLT Survey Telescope (VST) and compared our results to those obtained using previous algorithms. The simulation showed that our method outperforms previous approaches in several ways, such as restoring the profile of extended sources and minimizing ringing artefacts. Additionally, because our method relies on the inherent advantages of least squares fitting, it is more versatile and does not depend on the local uniformity hypothesis for the PSF. However, the new method consumes much more computation than the other approaches.
分类： 天文学 >> 天文学 提交时间： 2023-02-19
Ruibiao Luo
Liping Fu
Wentao Luo
Nicola R. Napolitano
Linghua Xie
Mario Radovich
Jing Liu
Rui Li
Valeria Amaro
Zhu Chen
Dezi Liu
Zuhui Fan
Giovanni Covone
Mattia Vaccari
摘要： The multi-band photometry of the VOICE imaging data, overlapping with 4.9 deg$^2$ of the Chandra Deep Field South (CDFS) area, enables both shape measurement and photometric redshift estimation to be the two essential quantities for weak lensing analysis. The depth of $mag_{AB}$ is up to 26.1 (5$\sigma$ limiting) in $r$-band. We estimate the Excess Surface Density (ESD; $\Delta\Sigma$) based on galaxy-galaxy measurements around galaxies at lower redshift (0.10<$z_l$<0.35) while we select the background sources to be at higher redshift ranging from 0.3 to 1.5. The foreground galaxies are divided into two major categories according to their colour (blue/red), each of which has been further divided into high/low stellar mass bins. Then the halo masses of the samples are estimated by modelling the signals, and the posterior of the parameters are samples via Mote Carlo Markov Chain (MCMC) process. We compare our results with the existing Stellar-to-Halo Mass Relation (SHMR) and find that the blue low stellar mass bin (median $M_*=10^{8.31}M_\odot$) deviates from the SHMR relation whereas all other three samples agrees well with empirical curves. We interpret this discrepancy as the effect of a low star formation efficiency of the low-mass blue dwarf galaxy population dominated in the VOICE-CDFS area.
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