Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userComparison between optical and X-ray cluster detection methods
399
0
0.0
(
0
)
Authors
S. Basilakos
Ask ChatGPT about the research
No Arabic abstract
In this work we present combined optical and X-ray cluster detection methods in an area near the North Galactic Pole area, previously covered by the SDSS and 2dF optical surveys. The same area has been covered by shallow ($sim 1.8$ deg$^{2}$) XMM-{em Newton} observations. The optical cluster detection procedure is based on merging two independent selection methods - a smoothing+percolation technique, and a Matched Filter Algorithm. The X-ray cluster detection is based on a wavelet-based algorithm, incorporated in the SAS v.5.2 package. The final optical sample counts 9 candidate clusters with richness of more than 20 galaxies, corresponding roughly to APM richness class. Three, of our optically detected clusters are also detected in our X-ray survey.
rate research
Read More
We present a comparison of X-ray and optical luminosities and luminosity functions of cluster candidates from a joint optical/X-ray survey, the ROSAT Optical X-Ray Survey. Completely independent X-ray and optical catalogs of 23 ROSAT fields (4.8 deg2) were created by a matched-filter optical algorithm and by a wavelet technique in the X-ray. We directly compare the results of the optical and X-ray selection techniques. The matched-filter technique detected 74% (26 out of 35) of the most reliable cluster candidates in the X-ray-selected sample; the remainder could be either constellations of X-ray point sources or z>1 clusters. The matched-filter technique identified approximately 3 times the number of candidates (152 candidates) found in the X-ray survey of nearly the same sky (57 candidates). While the estimated optical and X-ray luminosities of clusters of galaxies are correlated, the intrinsic scatter in this relationship is very large. We can reproduce the number and distribution of optical clusters with a model defined by the X-ray luminosity function and by an LX Lambda cl relation if H0=75 km s-1 Mpc-1 and if the LX Lambda cl relation is steeper than the expected LX Lambda 2cl. On statistical grounds, a bimodal distribution of X-ray luminous and X-ray faint clusters is unnecessary to explain our observations. Follow-up work is required to confirm whether the clusters without bright X-ray counterparts are simply X-ray faint for their optical luminosity because of their low mass or youth or are a distinct population of clusters that do not, for some reason, have dense intracluster media. We suspect that these optical clusters are low-mass systems, with correspondingly low X-ray temperatures and luminosities, or that they are not yet completely virialized systems.
We present and analyze the optical and X-ray catalogs of moderate-redshift cluster candidates from the ROSAT Optical X-ray Survey, or ROXS. The survey covers 4.8 square degrees (23 ROSAT PSPC pointings). The cross-correlated cluster catalogs were constructed by comparing two independent catalogs extracted from the optical and X-ray bandpasses, using a matched filter technique for the optical data and a wavelet technique for the X-ray data. We cross-id cluster candidates in each catalog. In Paper II we present the cluster catalogs and a numerical simulation of ROXS. We also present color-magnitude plots for several cluster candidates, and examine the prominence of the red sequence in each. We find that the X-ray clusters analyzed in this way do not all have a prominent red sequence. We conclude that while the red sequence may be distinct for massive, virialized clusters, it may be less so for lower-mass clusters at even moderate redshifts. Multiple, complementary methods of selecting and defining clusters may be essential, particularly at high redshift where all methods run into completeness limits, incomplete understanding of physical evolution, and projection effects.
A sample of 103 quasars from the Large Bright Quasar Survey (LBQS) has been observed with the VLA at 8.4 GHz to study the evolution of the radio luminosity distribution and its dependence on absolute magnitude. Radio data from pointed observations are now available for 359 of the 1055 LBQS quasars. The radio-loud fraction is constant at ~10% over the absolute magnitude range -28 <= MB <= -23, and it rises to ~20% (log R > 1) or ~35% (log L > 25) at the brightest absolute magnitudes in the sample. This nearly flat distribution differs markedly from those of the optically selected Palomar-Green (PG) Bright Quasar Survey and the X-ray selected Extended Medium Sensitivity Survey (EMSS), both of which have lower radio-loud fractions for absolute magnitudes fainter than MB = -24 and higher fractions at brighter magnitudes. The reason for the high radio-loud fraction at bright absolute magnitudes in the PG, compared to the LBQS and other optically selected quasar surveys, is unknown. The trend of increasing radio-loud fraction with absolute magnitude in the EMSS is due at least in part to a correlation between X-ray and radio luminosity. Combining the LBQS data with radio studies of high-redshift quasars leads to the conclusion that the radio-loud fraction in optically selected quasars does not appear to evolve significantly, aside from a modest increase at z ~1, from z = 0.2 to redshifts approaching 5, a result that is contrary to previous studies which found a decrease in radio-loud fraction with increasing redshift by comparing the low-z fraction in the PG to higher redshift samples.
In order to process efficiently ever-higher dimensional data such as images, sentences, or audio recordings, one needs to find a proper way to reduce the dimensionality of such data. In this regard, SVD-based methods including PCA and Isomap have been extensively used. Recently, a neural network alternative called autoencoder has been proposed and is often preferred for its higher flexibility. This work aims to show that PCA is still a relevant technique for dimensionality reduction in the context of classification. To this purpose, we evaluated the performance of PCA compared to Isomap, a deep autoencoder, and a variational autoencoder. Experiments were conducted on three commonly used image datasets: MNIST, Fashion-MNIST, and CIFAR-10. The four different dimensionality reduction techniques were separately employed on each dataset to project data into a low-dimensional space. Then a k-NN classifier was trained on each projection with a cross-validated random search over the number of neighbours. Interestingly, our experiments revealed that k-NN achieved comparable accuracy on PCA and both autoencoders projections provided a big enough dimension. However, PCA computation time was two orders of magnitude faster than its neural network counterparts.
To investigate the relationships between dynamical status and other important characteristics of galaxy clusters, we conducted a study of X-ray cluster morphology using a sample of 101 clusters at redshift z=0.05-1 taken from the Chandra archive. The X-ray morphology is quantitatively characterized by a series of objectively measured simple statistics of the X-ray surface brightness distribution, which are designed to be robust against variations of image quality caused by various exposure times and various cluster redshifts. We found: (1) The distorted and non-distorted clusters occupy well-defined loci in the L-T plane, demonstrating the measurements of the global luminosity and temperature for distorted clusters should be interpreted with caution, or alternatively, a rigorous morphological characterization is necessary when we use a sample of clusters with heterogeneous morphological characteristics to investigate the L-T or other related scaling relations. (2) Ellipticity and Off-center show no evolutionary effects between high and low redshift cluster subsets, while there may be a hint of weak evolutions for the Concentration and Asymmetry, in such a way that high-z clusters show more distorted morphology. (3) No correlation is found between X-ray morphology and X-ray luminosity or X-ray morphology and X-ray temperature of clusters, implying that interaction of clusters may not enhance or decrease the luminosity or temperature of clusters for extended period of time.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
