Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userA method to search for strong galaxy-galaxy lenses in optical imaging surveys
143
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
We present a semi-automated method to search for strong galaxy-galaxy lenses in optical imaging surveys. Our search technique constrains the shape of strongly lensed galaxies (or arcs) in a multi-parameter space, which includes the third order (octopole) moments of objects. This method is applied to the Deep Lens Survey (DLS), a deep ground based weak lensing survey imaging to $Rsim26$. The parameter space of arcs in the DLS is simulated using real galaxies extracted from deep HST fields in order to more accurately reproduce the properties of arcs. Arcs are detected in the DLS using a pixel thresholding method and candidate arcs are selected within this multi-parameter space. Examples of strong galaxy-galaxy lens candidates discovered in the DLS F2 field (4 square degrees) are presented.
rate research
Read More
We present a spectroscopic survey for strong galaxy-galaxy lenses. Exploiting optimal sight-lines to massive, bulge-dominated galaxies at redshifts $z sim 0.4$ with wide-field, multifibre spectroscopy, we anticipate the detection of 10-20 lensed Lyman-$alpha$ emitting galaxies at redshifts $z simgreat 3$ from a sample of 2000 deflectors. Initial spectroscopic observations are described and the prospects for constraining the emission-line luminosity function of the Lyman-$alpha$ emitting population are outlined.
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.
We have conducted a search for new strong gravitational lensing systems in the Dark Energy Spectroscopic Instrument Legacy Imaging Surveys Data Release 8. We use deep residual neural networks, building on previous work presented in Huang et al. (2020). These surveys together cover approximately one third of the sky visible from the northern hemisphere, reaching a z band AB magnitude of ~22.5. We compile a training sample that consists of known lensing systems as well as non-lenses in the Legacy Surveys and the Dark Energy Survey. After applying our trained neural networks to the survey data, we visually inspect and rank images with probabilities above a threshold. Here we present 1210 new strong lens candidates.
We present the first galaxy scale lens catalog from the second Red-Sequence Cluster Survey. The catalog contains 60 lensing system candidates comprised of Luminous Red Galaxy (LRG) lenses at 0.2 < z < 0.5 surrounded by blue arcs or apparent multiple images of background sources. The catalog is a valuable complement to previous galaxy-galaxy lens catalogs as it samples an intermediate lens redshift range and is composed of bright sources and lenses that allow easy follow-up for detailed analysis. Mass and mass-to-light ratio estimates reveal that the lens galaxies are massive (<M>~5.5x10e11 M_sun/h) and rich in dark matter (<M/L>~14 M_sun/L_sun,B*h). Even though a slight increasing trend in the mass-to-light ratio is observed from z=0.2 to z=0.5, current redshift and light profile measurements do not allow stringent constraints on the mass-to-light ratio evolution of LRGs.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
