Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userAuto-identification of unphysical source reconstructions in strong gravitational lens modelling
86
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
With the advent of next-generation surveys and the expectation of discovering huge numbers of strong gravitational lens systems, much effort is being invested into developing automated procedures for handling the data. The several orders of magnitude increase in the number of strong galaxy-galaxy lens systems is an insurmountable challenge for traditional modelling techniques. Whilst machine learning techniques have dramatically improved the efficiency of lens modelling, parametric modelling of the lens mass profile remains an important tool for dealing with complex lensing systems. In particular, source reconstruction methods are necessary to cope with the irregular structure of high-redshift sources. In this paper, we consider a Convolutional Neural Network (CNN) that analyses the outputs of semi-analytic methods which parametrically model the lens mass and linearly reconstruct the source surface brightness distribution. We show the unphysical source reconstructions that arise as a result of incorrectly initialised lens models can be effectively caught by our CNN. Furthermore, the CNN predictions can be used to automatically re-initialise the parametric lens model, avoiding unphysical source reconstructions. The CNN, trained on reconstructions of lensed Sersic sources, accurately classifies source reconstructions of the same type with a precision $P > 0.99$ and recall $R > 0.99$. The same CNN, without re-training, achieves $P=0.89$ and $R=0.89$ when classifying source reconstructions of more complex lensed HUDF sources. Using the CNN predictions to re-initialise the lens modelling procedure, we achieve a 69 per cent decrease in the occurrence of unphysical source reconstructions. This combined CNN and parametric modelling approach can greatly improve the automation of lens modelling.
rate research
Read More
Large scale imaging surveys will increase the number of galaxy-scale strong lensing candidates by maybe three orders of magnitudes beyond the number known today. Finding these rare objects will require picking them out of at least tens of millions of images and deriving scientific results from them will require quantifying the efficiency and bias of any search method. To achieve these objectives automated methods must be developed. Because gravitational lenses are rare objects reducing false positives will be particularly important. We present a description and results of an open gravitational lens finding challenge. Participants were asked to classify 100,000 candidate objects as to whether they were gravitational lenses or not with the goal of developing better automated methods for finding lenses in large data sets. A variety of methods were used including visual inspection, arc and ring finders, support vector machines (SVM) and convolutional neural networks (CNN). We find that many of the methods will be easily fast enough to analyse the anticipated data flow. In test data, several methods are able to identify upwards of half the lenses after applying some thresholds on the lens characteristics such as lensed image brightness, size or contrast with the lens galaxy without making a single false-positive identification. This is significantly better than direct inspection by humans was able to do. (abridged)
Strong gravitationally lensed quasars provide powerful means to study galaxy evolution and cosmology. Current and upcoming imaging surveys will contain thousands of new lensed quasars, augmenting the existing sample by at least two orders of magnitude. To find such lens systems, we built a robot, CHITAH, that hunts for lensed quasars by modeling the configuration of the multiple quasar images. Specifically, given an image of an object that might be a lensed quasar, CHITAH first disentangles the light from the supposed lens galaxy and the light from the multiple quasar images based on color information. A simple rule is designed to categorize the given object as a potential four-image (quad) or two-image (double) lensed quasar system. The configuration of the identified quasar images is subsequently modeled to classify whether the object is a lensed quasar system. We test the performance of CHITAH using simulated lens systems based on the Canada-France-Hawaii Telescope Legacy Survey. For bright quads with large image separations (with Einstein radius $r_{rm ein}>1.1$) simulated using Gaussian point-spread functions, a high true-positive rate (TPR) of $sim$90% and a low false-positive rate of $sim$$3%$ show that this is a promising approach to search for new lens systems. We obtain high TPR for lens systems with $r_{rm ein}gtrsim0.5$, so the performance of CHITAH is set by the seeing. We further feed a known gravitational lens system, COSMOS 5921$+$0638, to CHITAH, and demonstrate that CHITAH is able to classify this real gravitational lens system successfully. Our newly built CHITAH is omnivorous and can hunt in any ground-based imaging surveys.
We present the analysis of a sample of twenty-four SLACS-like galaxy-galaxy strong gravitational lens systems with a background source and deflectors from the Illustris-1 simulation. We study the degeneracy between the complex mass distribution of the lenses, substructures, the surface brightness distribution of the sources, and the time delays. Using a novel inference framework based on Approximate Bayesian Computation, we find that for all the considered lens systems, an elliptical and cored power-law mass density distribution provides a good fit to the data. However, the presence of cores in the simulated lenses affects most reconstructions in the form of a Source Position Transformation. The latter leads to a systematic underestimation of the source sizes by 50 per cent on average, and a fractional error in $H_{0}$ of around $25_{-19}^{+37}$ per cent. The analysis of a control sample of twenty-four lens systems, for which we have perfect knowledge about the shape of the lensing potential, leads to a fractional error on $H_{0}$ of $12_{-3}^{+6}$ per cent. We find no degeneracy between complexity in the lensing potential and the inferred amount of substructures. We recover an average total projected mass fraction in substructures of $f_{rm sub}<1.7-2.0times10^{-3}$ at the 68 per cent confidence level in agreement with zero and the fact that all substructures had been removed from the simulation. Our work highlights the need for higher-resolution simulations to quantify the lensing effect of more realistic galactic potentials better, and that additional observational constraint may be required to break existing degeneracies.
Galaxies and galaxy groups located along the line of sight towards gravitationally lensed quasars produce high-order perturbations of the gravitational potential at the lens position. When these perturbation are too large, they can induce a systematic error on $H_0$ of a few-percent if the lens system is used for cosmological inference and the perturbers are not explicitly accounted for in the lens model. In this work, we present a detailed characterization of the environment of the lens system WFI2033-4723 ($z_{rm src} = 1.662$, $z_{rm lens}$ = 0.6575), one of the core targets of the H0LICOW project for which we present cosmological inferences in a companion paper (Rusu et al. 2019). We use the Gemini and ESO-Very Large telescopes to measure the spectroscopic redshifts of the brightest galaxies towards the lens, and use the ESO-MUSE integral field spectrograph to measure the velocity-dispersion of the lens ($sigma_{rm {los}}= 250^{+15}_{-21}$ km/s) and of several nearby galaxies. In addition, we measure photometric redshifts and stellar masses of all galaxies down to $i < 23$ mag, mainly based on Dark Energy Survey imaging (DR1). Our new catalog, complemented with literature data, more than doubles the number of known galaxy spectroscopic redshifts in the direct vicinity of the lens, expanding to 116 (64) the number of spectroscopic redshifts for galaxies separated by less than 3 arcmin (2 arcmin) from the lens. Using the flexion-shift as a measure of the amplitude of the gravitational perturbation, we identify 2 galaxy groups and 3 galaxies that require specific attention in the lens models. The ESO MUSE data enable us to measure the velocity-dispersions of three of these galaxies. These results are essential for the cosmological inference analysis presented in Rusu et al. (2019).
Galaxies located in the environment or on the line of sight towards gravitational lenses can significantly affect lensing observables, and can lead to systematic errors on the measurement of $H_0$ from the time-delay technique. We present the results of a systematic spectroscopic identification of the galaxies in the field of view of the lensed quasar HE0435-1223, using the W. M. Keck, Gemini and ESO-Very Large telescopes. Our new catalog triples the number of known galaxy redshifts in the vicinity of the lens, expanding to 102 the number of measured redshifts for galaxies separated by less than 3 arcmin from the lens. We complement our catalog with literature data to gather redshifts up to 15 arcmin from the lens, and search for galaxy groups or clusters projected towards HE0435-1223. We confirm that the lens is a member of a small group that includes at least 12 galaxies, and find 8 other group candidates near the line of sight of the lens. The flexion shift, namely the shift of lensed images produced by high order perturbation of the lens potential, is calculated for each galaxy/group and used to identify which objects produce the largest perturbation of the lens potential. This analysis demonstrates that i) at most three of the five brightest galaxies projected within 12 arcsec of the lens need to be explicitly used in the lens models, and ii) the groups can be treated in the lens model as an external tidal field (shear) contribution.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
