Galaxy properties

We discuss the basic properties of galaxies, our present view of relations between them and their evolution

Evolution of colour-dependence of galaxy clustering up to z ∼\sim 1.2 based on the data from the VVDS-Wide survey

We discuss the dependence of galaxy clustering according to their colours up to z $\sim$ 1.2. For that purpose we used one of the wide fields (F22) from the VIMOS-VLT Deep Survey (VVDS). For galaxies with absolute luminosities close to the characteristic Schechter luminosities M* at a given redshift, we measured the projected two-point correlation function $w_{p}(r_{p})$ and we estimated the best-fit parameters for a single power-law model: $\xi (r) = (r/r_{0})^{-\gamma }$, where $r_{0}$ is the correlation length and \gamma is the slope of correlation function. Our results show that red galaxies exhibit the strongest clustering in all epochs up to z $\sim$ 1.2. Green valley represents the "intermediate" population and blue cloud shows the weakest clustering strength. We also compared the shape of $w_{p}(r_{p})$ for different galaxy populations. All three populations have different clustering properties on the small scales, similarly to the behaviour observed in the local catalogues

Problems of Clustering of Radiogalaxies

We present the preliminary analysis of clustering of a sample of 1157 radio-identified galaxies from Machalski & Condon (1999). We found that for separations $2-15 h^{-1}$Mpc their redshift space autocorrelation function $\xi(s)$ can be approximated by the power law with the correlation length $\sim 3.75h^{-1}$Mpc and slope $\gamma \sim 1.8$. The correlation length for radiogalaxies is found to be lower and the slope steeper than the corresponding parameters of the control sample of optically observed galaxies. Analysis the projected correlation function $\Xi(r)$ displays possible differences in the clustering properties between active galactic nuclei (AGN) and starburst (SB) galaxies.Comment: Submitted: Proceedings of IAUS 290 "Feeding Compact Objects: Accretion on All Scales", C. M. Zhang, T. Belloni, M. Mendez & S. N. Zhang (eds.

Total infrared luminosity estimation from local galaxies in AKARI all sky survey

We aim to use the a new and improved version of AKARI all sky survey catalogue of far-infrared sources to recalibrate the formula to derive the total infrared luminosity. We cross-match the faint source catalogue (FSC) of IRAS with the new AKARI-FIS and obtained a sample of 2430 objects. Then we calculate the total infrared (TIR) luminosity $L_{\textrm{TIR}}$ from the Sanders at al. (1996) formula and compare it with total infrared luminosity from AKARI FIS bands to obtain new coefficients for the general relation to convert FIR luminosity from AKARI bands to the TIR luminosity.Comment: 4 pages, 4 figure

VIPERS : in search for the solution of the riddle of dark energy (and many others)

We present the "VIMOS Public Extragalactic Redshift Survey" (VIPERS). We discuss the present status of the survey, the data which are already open to the public, and review first scientific results of the project

Recovery of the Cosmological Peculiar Velocity from the Density Field in the Weakly Nonlinear Regime

Using third-order perturbation theory, we derive a relation between the mean divergence of the peculiar velocity given density and the density itself. Our calculations assume Gaussian initial conditions and are valid for Gaussian filtering of the evolved density and velocity fields. The mean velocity divergence turns out to be a third-order polynomial in the density contrast. We test the power spectrum dependence of the coefficients of the polynomial for scale-free and standard CDM spectra and find it rather weak. Over scales larger than about 5 megaparsecs, the scatter in the relation is small compared to that introduced by random errors in the observed density and velocity fields. The relation can be useful for recovering the peculiar velocity from the associated density field, and also for non-linear analyses of the anisotropies of structure in redshift surveys.Comment: 8 pages, 1 figure, uses mn.sty and epsf.tex, slightly amended abstract and summary, accepted for publication in MNRA

Finding Strong Gravitational Lenses Through Self-Attention

The upcoming large scale surveys like LSST are expected to find approximately $10^5$ strong gravitational lenses by analysing data of many orders of magnitude larger than those in contemporary astronomical surveys. In this case, non-automated techniques will be highly challenging and time-consuming, even if they are possible at all. We propose a new automated architecture based on the principle of self-attention to find strong gravitational lenses. The advantages of self-attention-based encoder models over convolution neural networks are investigated, and ways to optimise the outcome of encoder models are analysed. We constructed and trained 21 self-attention based encoder models and five convolution neural networks to identify gravitational lenses from the Bologna Lens Challenge. Each model was trained separately using 18,000 simulated images, cross-validated using 2,000 images, and then applied to a test set with 100,000 images. We used four different metrics for evaluation: classification accuracy, area under the receiver operating characteristic curve (AUROC), the TPR$_0$ score and the TPR$_{10}$ score. The performances of self-attention-based encoder models and CNNs participating in the challenge are compared. They were able to surpass the CNN models that participated in the Bologna Lens Challenge by a high margin for the $TPR_0$ and TPR_{10}$. Self-Attention based models have clear advantages compared to simpler CNNs. They have highly competing performance in comparison to the currently used residual neural networks. Compared to CNNs, self-attention based models can identify highly confident lensing candidates and will be able to filter out potential candidates from real data. Moreover, introducing the encoder layers can also tackle the over-fitting problem present in the CNNs by acting as effective filters.Comment: 18 Pages, 4 tables and 19 Figure