111 research outputs found
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 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 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 and we estimated the best-fit parameters for a single power-law model: , where 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 1.2. Green valley represents the "intermediate" population and blue cloud shows the weakest clustering strength. We also compared the shape of 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 Mpc their redshift space autocorrelation function
can be approximated by the power law with the correlation length Mpc and slope . 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 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 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
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 score and the TPR 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 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
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