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Register a new userSystematic errors in strong gravitational lensing reconstructions, a numerical simulation perspective
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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.
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Automated searches for strong gravitational lensing in optical imaging survey datasets often employ machine learning and deep learning approaches. These techniques require more example systems to train the algorithms than have presently been discovered, which creates a need for simulated images as training dataset supplements. This work introduces and summarizes deeplenstronomy, an open-source Python package that enables efficient, large-scale, and reproducible simulation of images of astronomical systems. A full suite of unit tests, documentation, and example notebooks are available at https://deepskies.github.io/deeplenstronomy/ .
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.
We study how well halo properties of galaxy clusters, like mass and concentration, are recovered using lensing data. In order to generate a large sample of systems at different redshifts we use the code MOKA. We measure halo mass and concentration using weak lensing data alone (WL), fitting to an NFW profile the reduced tangential shear profile, or by combining weak and strong lensing data, by adding information about the size of the Einstein radius (WL+SL). For different redshifts, we measure the mass and the concentration biases and find that these are mainly caused by the random orientation of the halo ellipsoid with respect to the line-of-sight. Since our simulations account for the presence of a bright central galaxy, we perform mass and concentration measurements using a generalized NFW profile which allows for a free inner slope. This reduces both the mass and the concentration biases. We discuss how the mass function and the concentration mass relation change when using WL and WL+SL estimates. We investigate how selection effects impact the measured concentration-mass relation showing that strong lens clusters may have a concentration 20-30% higher than the average, at fixed mass, considering also the particular case of strong lensing selected samples of relaxed clusters. Finally, we notice that selecting a sample of relaxed galaxy clusters, as is done in some cluster surveys, explain the concentration-mass relation biases.
Strong gravitational lenses provide an important tool to measure masses in the distant Universe, thus testing models for galaxy formation and dark matter; to investigate structure at the Epoch of Reionization; and to measure the Hubble constant and possibly w as a function of redshift. However, the limiting factor in all of these studies has been the currently small samples of known gravitational lenses (~10^2). The era of the SKA will transform our understanding of the Universe with gravitational lensing, particularly at radio wavelengths where the number of known gravitational lenses will increase to ~10^5. Here we discuss the technical requirements, expected outcomes and main scientific goals of a survey for strong gravitational lensing with the SKA. We find that an all-sky (3pi sr) survey carried out with the SKA1-MID array at an angular resolution of 0.25-0.5 arcsec and to a depth of 3 microJy / beam is required for studies of galaxy formation and cosmology with gravitational lensing. In addition, the capability to carryout VLBI with the SKA1 is required for tests of dark matter and studies of supermassive black holes at high redshift to be made using gravitational lensing.
Weak lensing is emerging as a powerful observational tool to constrain cosmological models, but is at present limited by an incomplete understanding of many sources of systematic error. Many of these errors are multiplicative and depend on the population of background galaxies. We show how the commonly cited geometric test, which is rather insensitive to cosmology, can be used as a ratio test of systematics in the lensing signal at the 1 per cent level. We apply this test to the galaxy-galaxy lensing analysis of the Sloan Digital Sky Survey (SDSS), which at present is the sample with the highest weak lensing signal to noise and has the additional advantage of spectroscopic redshifts for lenses. This allows one to perform meaningful geometric tests of systematics for different subsamples of galaxies at different mean redshifts, such as brighter galaxies, fainter galaxies and high-redshift luminous red galaxies, both with and without photometric redshift estimates. We use overlapping objects between SDSS and the DEEP2 and 2SLAQ spectroscopic surveys to establish accurate calibration of photometric redshifts and to determine the redshift distributions for SDSS. We use these redshift results to compute the projected surface density contrast DeltaSigma around 259 609 spectroscopic galaxies in the SDSS; by measuring DeltaSigma with different source samples we establish consistency of the results at the 10 per cent level (1-sigma). We also use the ratio test to constrain shear calibration biases and other systematics in the SDSS survey data to determine the overall galaxy-galaxy weak lensing signal calibration uncertainty. We find no evidence of any inconsistency among many subsamples of the data.
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