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Register a new userThe PCA Lens-Finder: application to CFHTLS
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We present the results of a new search for galaxy-scale strong lensing systems in CFHTLS Wide. Our lens-finding technique involves a preselection of potential lens galaxies, applying simple cuts in size and magnitude. We then perform a Principal Component Analysis of the galaxy images, ensuring a clean removal of the light profile. Lensed features are searched for in the residual images using the clustering topometric algorithm DBSCAN. We find 1098 lens candidates that we inspect visually, leading to a cleaned sample of 109 new lens candidates. Using realistic image simulations we estimate the completeness of our sample and show that it is independent of source surface brightness, Einstein ring size (image separation) or lens redshift. We compare the properties of our sample to previous lens searches in CFHTLS. Including the present search, the total number of lenses found in CFHTLS amounts to 678, which corresponds to ~4 lenses per square degree down to i=24.8. This is equivalent to ~ 60.000 lenses in total in a survey as wide as Euclid, but at the CFHTLS resolution and depth.
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We extend the halo-based group finder developed by Yang et al. (2005b) to use data {it simultaneously} with either photometric or spectroscopic redshifts. A mock galaxy redshift surveys constructed from a high-resolution N-body simulation is used to evaluate the performance of this extended group finder. For galaxies with magnitude ${rm zle 21}$ and redshift $0<zle 1.0$ in the DESI legacy imaging surveys (The Legacy Surveys), our group finder successfully identifies more than 60% of the members in about $90%$ of halos with mass $ga 10^{12.5}msunh$. Detected groups with mass $ga 10^{12.0}msunh$ have a purity (the fraction of true groups) greater than 90%. The halo mass assigned to each group has an uncertainty of about 0.2 dex at the high mass end $ga 10^{13.5}msunh$ and 0.45 dex at the low mass end. Groups with more than 10 members have a redshift accuracy of $sim 0.008$. We apply this group finder to the Legacy Surveys DR8 and find 6.4 Million groups with at least 3 members. About 500,000 of these groups have at least 10 members. The resulting catalog containing 3D coordinates, richness, halo masses, and total group luminosities, is made publicly available.
We present an algorithm using Principal Component Analysis (PCA) to subtract galaxies from imaging data, and also two algorithms to find strong, galaxy-scale gravitational lenses in the resulting residual image. The combined method is optimized to find full or partial Einstein rings. Starting from a pre-selection of potential massive galaxies, we first perform a PCA to build a set of basis vectors. The galaxy images are reconstructed using the PCA basis and subtracted from the data. We then filter the residual image with two different methods. The first uses a curvelet (curved wavelets) filter of the residual images to enhance any curved/ring feature. The resulting image is transformed in polar coordinates, centered on the lens galaxy center. In these coordinates, a ring is turned into a line, allowing us to detect very faint rings by taking advantage of the integrated signal-to-noise in the ring (a line in polar coordinates). The second way of analysing the PCA-subtracted images identifies structures in the residual images and assesses whether they are lensed images according to their orientation, multiplicity and elongation. We apply the two methods to a sample of simulated Einstein rings, as they would be observed with the ESA Euclid satellite in the VIS band. The polar coordinates transform allows us to reach a completeness of 90% and a purity of 86%, as soon as the signal-to-noise integrated in the ring is higher than 30, and almost independent of the size of the Einstein ring. Finally, we show with real data that our PCA-based galaxy subtraction scheme performs better than traditional subtraction based on model fitting to the data. Our algorithm can be developed and improved further using machine learning and dictionary learning methods, which would extend the capabilities of the method to more complex and diverse galaxy shapes.
With the development of modern technologies such as IFUs, it is possible to obtain data cubes in which one produces images with spectral resolution. To extract information from them can be quite complex, and hence the development of new methods of data analysis is desirable. We briefly describe a method of analysis of data cubes (data from single field observations, containing two spatial and one spectral dimension) that uses Principal Component Analysis (PCA) to express the data in the form of reduced dimensionality, facilitating efficient information extraction from very large data sets. We applied the method, for illustration purpose, to the central region of the low ionization nuclear emission region (LINER) galaxy NGC 4736, and demonstrate that it has a type 1 active nucleus, not known before. Furthermore, we show that it is displaced from the centre of its stellar bulge.
Identifying galaxy groups from redshift surveys of galaxies plays an important role in connecting galaxies with the underlying dark matter distribution. Current and future high-$z$ spectroscopic surveys, usually incomplete in redshift sampling, present both opportunities and challenges to identifying groups in the high-$z$ Universe. We develop a group finder that is based on incomplete redshift samples combined with photometric data, using a machine learning method to assign halo masses to identified groups. Test using realistic mock catalogs shows that $gtrsim 90%$ of true groups with halo masses $rm M_h gtrsim 10^{12} M_{odot}/h$ are successfully identified, and that the fraction of contaminants is smaller than $10%$. The standard deviation in the halo mass estimation is smaller than 0.25 dex at all masses. We apply our group finder to zCOSMOS-bright and describe basic properties of the group catalog obtained.
Fossil groups (FGs) have been discovered twenty-five years ago, and are now defined as galaxy groups with an X-ray luminosity higher than $10^{42} h_{50}^{-2}$ erg s$^{-1}$ and a brightest group galaxy brighter than the other group members by at least 2 magnitudes. However, the scenario of their formation remains controversial. We propose here a probabilistic analysis of FGs, extracted from the large catalogue of candidate groups and clusters detected by Sarron et al. (2018) in the CFHTLS survey, based on photometric redshifts, to investigate their position in the cosmic web and probe their environment. Based on spectroscopic and photometric redshifts, we estimate the probability of galaxies to belong to a galaxy structure, and by imposing the condition that the brightest group galaxy is at least brighter than the others by 2 magnitudes, we compute the probability for a given galaxy structure to be a FG. We analyse the mass distribution of these candidate FGs, and estimate their distance to the filaments and nodes of the cosmic web in which they are embedded. We find that the structures with masses lower than $2.4times 10^{14}$ M$_odot$ have the highest probabilities of being fossil groups (PFG). Overall, structures with PFG$geq$50% are located close to the cosmic web filaments (87% are located at less than 1 Mpc from their nearest filament). They are preferentially four times more distant from their nearest node than from their nearest filament. We confirm that FGs have small masses and are rare. They seem to reside closeby cosmic filaments and do not survive in nodes. Being in a poor environment could therefore be the driver of FG formation, the number of nearby galaxies not being sufficient to compensate for the cannibalism of the central group galaxy.
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