分类: 天文学 >> 天文学 提交时间: 2023-02-19
摘要: We provide novel constraints on the parameters defining the universal
pressure profile (UPP) within clusters of galaxies, and explore their
dependence on the cluster mass and redshift, from measurements of
Sunyaev-Zel'dovich Compton-$y$ profiles. We employ both the $\textit{Planck}$
2015 MILCA and the ACT-DR4 $y$ maps over the common $\sim 2,100\,\text{deg}^2$
footprint. We combine existing cluster catalogs based on KiDS, SDSS and DESI
observations, for a total of 23,820 clusters spanning the mass range
$10^{14.0}\,\text{M}_{\odot}
分类: 天文学 >> 天文学 提交时间: 2023-02-19
摘要: The multi-band photometry of the VOICE imaging data, overlapping with 4.9 deg$^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.
分类: 天文学 >> 天文学 提交时间: 2023-02-19
摘要: This report is the result of a joint discussion between the Rubin and Euclid scientific communities. The work presented in this report was focused on designing and recommending an initial set of Derived Data products (DDPs) that could realize the science goals enabled by joint processing. All interested Rubin and Euclid data rights holders were invited to contribute via an online discussion forum and a series of virtual meetings. Strong interest in enhancing science with joint DDPs emerged from across a wide range of astrophysical domains: Solar System, the Galaxy, the Local Volume, from the nearby to the primaeval Universe, and cosmology.
分类: 天文学 >> 天文学 提交时间: 2023-02-19
摘要: In the era of large sky surveys, photometric redshifts (photo-z) represent crucial information for galaxy evolution and cosmology studies. In this work, we propose a new Machine Learning (ML) tool called Galaxy morphoto-Z with neural Networks (GaZNet-1), which uses both images and multi-band photometry measurements to predict galaxy redshifts, with accuracy, precision and outlier fraction superior to standard methods based on photometry only. As a first application of this tool, we estimate photo-z of a sample of galaxies in the Kilo-Degree Survey (KiDS). GaZNet-1 is trained and tested on $\sim140 000$ galaxies collected from KiDS Data Release 4 (DR4), for which spectroscopic redshifts are available from different surveys. This sample is dominated by bright (MAG$\_$AUTO$<21$) and low redshift ($z < 0.8$) systems, however, we could use $\sim$ 6500 galaxies in the range $0.8 < z < 3$ to effectively extend the training to higher redshift. The inputs are the r-band galaxy images plus the 9-band magnitudes and colours, from the combined catalogs of optical photometry from KiDS and near-infrared photometry from the VISTA Kilo-degree Infrared survey. By combining the images and catalogs, GaZNet-1 can achieve extremely high precision in normalized median absolute deviation (NMAD=0.014 for lower redshift and NMAD=0.041 for higher redshift galaxies) and low fraction of outliers ($0.4$\% for lower and $1.27$\% for higher redshift galaxies). Compared to ML codes using only photometry as input, GaZNet-1 also shows a $\sim 10-35$% improvement in precision at different redshifts and a $\sim$ 45% reduction in the fraction of outliers. We finally discuss that, by correctly separating galaxies from stars and active galactic nuclei, the overall photo-z outlier fraction of galaxies can be cut down to $0.3$\%.
分类: 天文学 >> 天文学 提交时间: 2023-02-19
摘要: The W-CDF-S and ELAIS-S1 fields will be two of the LSST Deep Drilling fields, but the availability of spectroscopic redshifts within these two fields is still limited on deg^2 scales. To prepare for future science, we use EAZY to estimate photometric redshifts (photo-zs) in these two fields based on forced-photometry catalogs. Our photo-z catalog consists of ~0.8 million sources covering 4.9 deg^2 in W-CDF-S and ~0.8 million sources covering 3.4 deg^2 in ELAIS-S1, among which there are ~0.6 (W-CDF-S) and ~0.4 (ELAIS-S1) million sources having signal-to-noise-ratio (SNR) > 5 detections in more than 5 bands. By comparing photo-zs and available spectroscopic redshifts, we demonstrate the general reliability of our photo-z measurements. Our photo-z catalog is publicly available at \doi{10.5281/zenodo.4603178}.