We consider a machine learning algorithm to detect and identify strong gravitational lenses on sky images. First, we simulate different artificial but very close to reality images of galaxies, stars and strong lenses, using six different methods, i.e
. two for each class. Then we deploy a convolutional neural network architecture to classify these simulated images. We show that after neural network training process one achieves about 93 percent accuracy. As a simple test for the efficiency of the convolutional neural network, we apply it on an real Einstein cross image. Deployed neural network classifies it as gravitational lens, thus opening a way for variety of lens search applications of the deployed machine learning scheme.
We consider the observational aspects of the value of dark energy density from quantum vacuum fluctuations based initially on the Gurzadyan-Xue model. We reduce the Djorgovski-Gurzadyan integral equation to a differential equation for the co-moving h
orizon and then, by means of the obtained explicit form for the luminosity distance, we construct the Hubble diagram for two classes of observational samples. For supernova and gamma-ray burst data we show that this approach provides viable predictions for distances up to $z simeq 9$, quantitatively at least as good as those provided by the lambda cold dark matter ($Lambda$CDM) model. The Hubble parameter dependence $H(z)$ of the two models also reveals mutual crossing at $z=0.4018$, the interpretation of which is less evident.
We use the Planck LFI 70GHz data to further probe point source detection technique in the sky maps of the cosmic microwave background (CMB) radiation. The method developed by Tegmark et al. for foreground reduced maps and the Kolmogorov parameter as
the descriptor are adopted for the analysis of Planck satellite CMB temperature data. Most of the detected points coincide with point sources already revealed by other methods. However, we have also found 9 source candidates for which still no counterparts are known.
The Kolmogorov stochasticity parameter is shown to act as a tool to detect point sources in the cosmic microwave background (CMB) radiation temperature maps. Kolmogorov CMB map constructed for the WMAPs 7-year datasets reveals tiny structures which i
n part coincide with point radio and Fermi/LAT gamma-ray sources. In the first application of this method, we identified several sources not present in the then available 0FGL Fermi catalog. Subsequently they were confirmed in the more recent and more complete 1FGL catalog, thus strengthening the evidence for the power of this methodology.
The power spectrum is obtained for the Kolmogorov stochasticity parameter map for WMAPs cosmic microwave background (CMB) radiation temperature datasets. The interest for CMB Kolmogorov map is that it can carry direct information about voids in the m
atter distribution, so that the correlations in the distribution of voids have to be reflected in the power spectrum. Although limited by the angular resolution of the WMAP, this analysis shows the possibility of acquiring this crucial information via CMB maps. Even the already obtained behavior, some of which is absent in the simulated maps, can influence the development of views on the void correlations at the large-scale web formation.
