A joint microlensing analysis of lensing mass and accretion disc models

Microlensing of multiply imaged quasars is a unique probe of quasar structure, down to the size of the accretion disc and the central black hole. Flux ratios between close pairs of images of lensed quasars can be used to constrain the accretion disc size and temperature profile. The starting point of any microlensing model is the macromodel of the lens, which provides the convergence and shear values at the location of the multiple images. Here I present a new approach of microlensing modelling independently of the macromodel of the lens. The technique is applied to the close pair of images A(1) and A(2) of MG 0414+0534, for a set of flux ratios with large variation with respect to wavelength. The inferred accretion disc size and temperature profile measurements, as well as the smooth matter fraction at the location of the images, are quite robust under a wide range of macromodel variations. A case of using purely microlensing data (flux ratios) to constrain the macromodel is also presented. This is a first application of the technique on a fiducial system and set of flux ratios; the method is readily applicable to collections of such objects and can be extended to light-curve and/or imaging data

The very knotty lenser: Exploring the role of regularization in source and potential reconstructions using Gaussian process regression

Reconstructing lens potentials and lensed sources can easily become an underconstrained problem, even when the degrees of freedom are low, due to degeneracies, particularly when potential perturbations superimposed on a smooth lens are included. Regularization has traditionally been used to constrain the solutions where the data failed to do so, e.g. in unlensed parts of the source. In this exploratory work, we go beyond the usual choices of regularization and adopt observationally motivated priors for the source brightness. We also perform a similar comparison when reconstructing lens potential perturbations, which are assumed to be stationary, i.e. permeate the entire field of view. We find that physically motivated priors lead to lower residuals, avoid overfitting, and are decisively preferred within a Bayesian quantitative framework in all the examples considered. For the perturbations, choosing the wrong regularization can have a detrimental effect that even high-quality data cannot correct for, while using a purely smooth lens model can absorb them to a very high degree and lead to biased solutions. Finally, our new implementation of the semi-linear inversion technique provides the first quantitative framework for measuring degeneracies between the source and the potential perturbations

Testing Convolutional Neural Networks for finding strong gravitational lenses in KiDS

Convolutional Neural Networks (ConvNets) are one of the most promising methods for identifying strong gravitational lens candidates in survey data. We present two ConvNet lens-finders which we have trained with a dataset composed of real galaxies from the Kilo Degree Survey (KiDS) and simulated lensed sources. One ConvNet is trained with single \textit{r}-band galaxy images, hence basing the classification mostly on the morphology. While the other ConvNet is trained on \textit{g-r-i} composite images, relying mostly on colours and morphology. We have tested the ConvNet lens-finders on a sample of 21789 Luminous Red Galaxies (LRGs) selected from KiDS and we have analyzed and compared the results with our previous ConvNet lens-finder on the same sample. The new lens-finders achieve a higher accuracy and completeness in identifying gravitational lens candidates, especially the single-band ConvNet. Our analysis indicates that this is mainly due to improved simulations of the lensed sources. In particular, the single-band ConvNet can select a sample of lens candidates with $\sim40\%$ purity, retrieving 3 out of 4 of the confirmed gravitational lenses in the LRG sample. With this particular setup and limited human intervention, it will be possible to retrieve, in future surveys such as Euclid, a sample of lenses exceeding in size the total number of currently known gravitational lenses.Comment: 16 pages, 10 figures. Accepted for publication in MNRA

Finding Strong Gravitational Lenses in the Kilo Degree Survey with Convolutional Neural Networks

The volume of data that will be produced by new-generation surveys requires automatic classification methods to select and analyze sources. Indeed, this is the case for the search for strong gravitational lenses, where the population of the detectable lensed sources is only a very small fraction of the full source population. We apply for the first time a morphological classification method based on a Convolutional Neural Network (CNN) for recognizing strong gravitational lenses in $255$ square degrees of the Kilo Degree Survey (KiDS), one of the current-generation optical wide surveys. The CNN is currently optimized to recognize lenses with Einstein radii $\gtrsim 1.4$ arcsec, about twice the $r$-band seeing in KiDS. In a sample of $21789$ colour-magnitude selected Luminous Red Galaxies (LRG), of which three are known lenses, the CNN retrieves 761 strong-lens candidates and correctly classifies two out of three of the known lenses. The misclassified lens has an Einstein radius below the range on which the algorithm is trained. We down-select the most reliable 56 candidates by a joint visual inspection. This final sample is presented and discussed. A conservative estimate based on our results shows that with our proposed method it should be possible to find $\sim100$ massive LRG-galaxy lenses at z\lsim 0.4 in KiDS when completed. In the most optimistic scenario this number can grow considerably (to maximally $\sim$2400 lenses), when widening the colour-magnitude selection and training the CNN to recognize smaller image-separation lens systems.Comment: 24 pages, 17 figures. Published in MNRA

SEAGLE - III: Towards resolving the mismatch in the dark-matter fraction in early-type galaxies between silations and observations

The central dark-matter fraction of galaxies is sensitive to feedback processes during galaxy foation. Strong gravitational lensing has been effective in the precise measurement of the dark-matter fraction inside massive early-type galaxies. Here, we compare the projected dark-matter fraction of early-type galaxies inferred from the SLACS (Sloan Lens ACS Survey) strong-lens survey with those obtained from the Evolution and Assembly of GaLaxies and their Environment (EAGLE), Illustris, and IllustrisTNG hydrodynamical silations. Previous comparisons with some silations revealed a large discrepancy, with considerably higher inferred dark-matter fractions - by factors of ≈2-3 - inside half of the effective radius in observed strong-lens galaxies as compared to silated galaxies. Here, we report good agreement between EAGLE and SLACS for the dark-matter fractions inside both half of the effective radius and the effective radius as a function of the galaxy's stellar mass, effective radius, and total mass-density slope. However, for IllustrisTNG and Illustris, the dark-matter fractions are lower than observed. This work consistently assumes a Chabrier initial mass function (IMF), which suggests that a different IMF (although not excluded) is not necessary to resolve this mismatch. The differences in the stellar feedback model between EAGLE and Illustris and IllustrisTNG are likely the dominant cause of the difference in their dark-matter fraction and density slope

KiDS-SQuaD: The KiDS Strongly lensed Quasar Detection project

New methods have been recently developed to search for strong gravitational lenses, in particular lensed quasars, in wide-field imaging surveys. Here, we compare the performance of three different, morphology- and photometry- based methods to find lens candidates over the Kilo-Degree Survey (KiDS) DR3 footprint (440 deg$^2$). The three methods are: i) a multiplet detection in KiDS-DR3 and/or Gaia-DR1, ii) direct modeling of KiDS cutouts and iii) positional offsets between different surveys (KiDS-vs-Gaia, Gaia-vs-2MASS), with purpose-built astrometric recalibrations. The first benchmark for the methods has been set by the recovery of known lenses. We are able to recover seven out of ten known lenses and pairs of quasars observed in the KiDS DR3 footprint, or eight out of ten with improved selection criteria and looser colour pre-selection. This success rate reflects the combination of all methods together, which, taken individually, performed significantly worse (four lenses each). One movelty of our analysis is that the comparison of the performances of the different methods has revealed the pros and cons of the approaches and, most of all, the complementarities. We finally provide a list of high-grade candidates found by one or more methods, awaiting spectroscopic follow-up for confirmation. Of these, KiDS 1042+0023 is to our knowledge the first confirmed lensed quasar from KiDS, exhibiting two quasar spectra at the same source redshift at either sides of a red galaxy, with uniform flux-ratio $f\approx1.25$ over the wavelength range $0.45\mu\mathrm{m}<\lambda<0.75\mu\mathrm{m}.$Comment: 12 pages, 4 figures, 4 tables, accepted for publication in MNRA

SEAGLE - III: towards resolving the mismatch in the dark-matter fraction in early-type galaxies between simulations and observations

Galaxie

Constraining quasar structure using high-frequency microlensing variations and continuum reverberation

peer reviewedGravitational microlensing is a powerful tool for probing the inner structure of strongly lensed quasars and for constraining parameters of the stellar mass function of lens galaxies. This is achieved by analysing microlensing light curves between the multiple images of strongly lensed quasars and accounting for the effects of three main variable components: (1) the continuum flux of the source, (2) microlensing by stars in the lens galaxy, and (3) reverberation of the continuum by the broad line region (BLR). The latter, ignored by state-of-the-art microlensing techniques, can introduce high-frequency variations which we show carry information on the BLR size. We present a new method that includes all these components simultaneously and fits the power spectrum of the data in the Fourier space rather than the observed light curve itself. In this new framework, we analyse COSMOGRAIL light curves of the two-image system QJ 0158-4325 known to display high-frequency variations. Using exclusively the low-frequency part of the power spectrum, our constraint on the accretion disk radius agrees with the thin-disk model estimate and the results of previous work where the microlensing light curves were fit in real space. However, if we also take into account the high-frequency variations, the data favour significantly smaller disk sizes than previous microlensing measurements. In this case, our results are only in agreement with the thin-disk model prediction only if we assume very low mean masses for the microlens population, i.e

New High-quality Strong Lens Candidates with Deep Learning in the Kilo-Degree Survey

We report new high-quality galaxy scale strong lens candidates found in the Kilo Degree Survey data release 4 using Machine Learning. We have developed a new Convolutional Neural Network (CNN) classifier to search for gravitational arcs, following the prescription by \cite{2019MNRAS.484.3879P} and using only $r-$band images. We have applied the CNN to two "predictive samples": a Luminous red galaxy (LRG) and a "bright galaxy" (BG) sample ($r<21$). We have found 286 new high probability candidates, 133 from the LRG sample and 153 from the BG sample. We have then ranked these candidates based on a value that combines the CNN likelihood to be a lens and the human score resulting from visual inspection (P-value) and we present here the highest 82 ranked candidates with P-values $\ge 0.5$. All these high-quality candidates have obvious arc or point-like features around the central red defector. Moreover, we define the best 26 objects, all with scores P-values $\ge 0.7$ as a "golden sample" of candidates. This sample is expected to contain very few false positives and thus it is suitable for follow-up observations. The new lens candidates come partially from the the more extended footprint adopted here with respect to the previous analyses, partially from a larger predictive sample (also including the BG sample). These results show that machine learning tools are very promising to find strong lenses in large surveys and more candidates that can be found by enlarging the predictive samples beyond the standard assumption of LRGs. In the future, we plan to apply our CNN to the data from next-generation surveys such as the Large Synoptic Survey Telescope, Euclid, and the Chinese Space Station Optical Survey.Comment: Accepted by AP