arXiv subjects

Georgios Vernardos

Publications and source records attributed to Georgios Vernardos.

At least 19 recordsLinked to original sources

arXiv2025

Simulating quasar microlensing light curves: High magnification events

Quasar microlensing can be used to constrain important astrophysical properties, such as the accretion disk size and the amount of stars in the lensing galaxy. The associated brightness variations over time, in particular high magnification events (HMEs) and caustic crossings, can yield precise constraints due to their strong dependence on the relative projected velocities of the components and accretion disk size. The next generation of large sky area surveys, such as The Vera Rubin Observatory (LSST) and Euclid, are expected to find and follow-up thousands of lensed quasars from which such events could be identified and observed. In this work we present a characterization and estimation of all HMEs that could potentially be observed, focusing on systems that could be identified by ground based telescopes. From systems whose minimum image separation is at least 1 arcsec, and their second dimmest image is at least 21.5 magnitudes in the i-band ($\sim560$ in the southern or northern sky), we estimate $\sim60$ HMEs with amplitudes $>0.3$ [mag] in the r-band per year. We find that on average, saddle images are approximately four times more likely to host events than minima, and $\sim10\%$ ($\sim50\%$) of events are caustic crossings for saddles (minima). We also find that HMEs in saddle images can have amplitudes $\sim1-2$ [mag] larger than minima.

astro-ph.GA↗

arXiv2025

Constraining compact dark matter with time-varying quasar equivalent widths

One of the possible explanations for dark matter is that of compact dark objects of baryonic origin, such as black holes or even planets. Accumulating evidence, including the discovery of merging stellar mass black holes through gravitational waves, point to a population of such objects making up at least some fraction of dark matter. We revisit a historically heavily used probe, quasar spectra, from the new perspective of time variability and gravitational lensing. From a sample of 777 quasars selected from archival data we identify 19 that show decisive evidence of lensing by compact objects with masses measured in the range $5\times 10^{-5} < M/\mathrm{M}_{\odot} < 2\times 10^{-2}$ with 99\% confidence. This is much lower than what is hoped to be detected by even the most futuristic gravitational wave detectors and analysis strategies, but is crucial for theories of compact dark matter, such as primordial black holes predicted from quantum phase transitions in the early Universe.

astro-ph.CO↗

arXiv2025

LTDE: The Lens Time Delay Experiment I. From pixels to light curves

Gravitationally lensed quasars offer a unique opportunity to study cosmological and extragalactic phenomena, using reliable light curves of the lensed images. This requires accurate deblending of the quasar images, which is not trivial due to the small separation between the lensed images (typically $\sim1$ arcsec) and because there is light contamination by the lensing galaxy and the quasar host galaxy. We propose a series of experiments aimed at testing our ability to extract precise and accurate photometry of lensed quasars. In this first paper, we focus on evaluating our ability to extract light curves from simulated CCD images of lensed quasars spanning a broad range of configurations and assuming different observational/instrumental conditions. Specifically, the experiment proposes to go from pixels to light curves and to evaluate the limits of current photometric algorithms. Our experiment has several steps, from data with known point spread function (PSF), to an unknown spatially-variable PSF field that the user has to take into account. This paper is the release of our simulated images. Anyone can extract the light curves and submit their results by the deadline. These will be evaluated with the metrics described below. Our set of simulations will be public and it is meant to be a benchmark for time-domain surveys like Rubin-LSST or other follow-up time-domain observations at higher temporal cadence. It is also meant to be a test set to help develop new algorithms in the future.

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arXiv2025

Predicting High-magnification Events in Microlensed Quasars in the Era of LSST using Recurrent Neural Networks

Upcoming widefield surveys, such as the Rubin Observatory's Legacy Survey of Space and Time (LSST), will monitor thousands of strongly lensed quasars over a 10 yr period. Many of these monitored quasars will undergo high-magnification events (HMEs) through microlensing, as the accretion disk crosses a caustic, places of infinite magnification. Microlensing allows us to map the inner regions of the accretion disk as it crosses a caustic, even at large cosmological distances. The observational cadences of LSST are not ideal for probing the inner regions of the accretion disk, so there is a need to predict HMEs as early as possible, to trigger high-cadence multiband or spectroscopic follow-up observations. Here, we simulate a diverse and realistic sample of 10 yr quasar microlensing light curves to train a recurrent neural network to predict HMEs before they occur, by classifying the locations of the peaks at each time step. This is the first deep-learning approach for predicting HMEs. We give estimates of how well we expect to predict HME peaks during LSST and benchmark how our metrics change with different cadence strategies. With LSST-like observations, we can predict approximately 55% of HME peaks, corresponding to tens to hundreds per year and a false-positive rate of around 20% compared to the total number of HMEs. Our network can be continuously applied throughout the LSST survey, providing crucial alerts for optimizing follow-up resources.

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arXiv2024

Autoencoder Reconstruction of Cosmological Microlensing Magnification Maps

Enhanced modeling of microlensing variations in light curves of strongly lensed quasars improves measurements of cosmological time delays, the Hubble Constant, and quasar structure. Traditional methods for modeling extra-galactic microlensing rely on computationally expensive magnification map generation. With large datasets expected from wide-field surveys like the Vera C. Rubin Legacy Survey of Space and Time, including thousands of lensed quasars and hundreds of multiply imaged supernovae, faster approaches become essential. We introduce a deep-learning model that is trained on pre-computed magnification maps covering the parameter space on a grid of k, g, and s. Our autoencoder creates a low-dimensional latent space representation of these maps, enabling efficient map generation. Quantifying the performance of magnification map generation from a low dimensional space is an essential step in the roadmap to develop neural network-based models that can replace traditional feed-forward simulation at much lower computational costs. We develop metrics to study various aspects of the autoencoder generated maps and show that the reconstruction is reliable. Even though we observe a mild loss of resolution in the generated maps, we find this effect to be smaller than the smoothing effect of convolving the original map with a source of a plausible size for its accretion disk in the red end of the optical spectrum and larger wavelengths and particularly one suitable for studying the Broad-Line Region of quasars. Used to generate large samples of on-demand magnification maps, our model can enable fast modeling of microlensing variability in lensed quasars and supernovae.

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arXiv2024

Measuring the Substructure Mass Power Spectrum of 23 SLACS Strong Galaxy-Galaxy Lenses with Convolutional Neural Networks

Strong gravitational lensing can be used as a tool for constraining the substructure in the mass distribution of galaxies. In this study we investigate the power spectrum of dark matter perturbations in a population of 23 Hubble Space Telescope images of strong galaxy-galaxy lenses selected from The Sloan Lens ACS (SLACS) survey. We model the dark matter substructure as a Gaussian Random Field perturbation on a smooth lens mass potential, characterized by power-law statistics. We expand upon the previously developed machine learning framework to predict the power-law statistics by using a convolutional neural network (CNN) that accounts for both epistemic and aleatoric uncertainties. For the training sets, we use the smooth lens mass potentials and reconstructed source galaxies that have been previously modelled through traditional fits of analytical and shapelet profiles as a starting point. We train three CNNs with different training set: the first using standard data augmentation on the best-fitting reconstructed sources, the second using different reconstructed sources spaced throughout the posterior distribution, and the third using a combination of the two data sets. We apply the trained CNNs to the SLACS data and find agreement in their predictions. Our results suggest a significant substructure perturbation favoring a high frequency power spectrum across our lens population.

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arXiv2024

Resolving the Vicinity of Supermassive Black Holes with Gravitational Microlensing

In the near future, wide field surveys will discover 1000's of new strongly lensed quasars, and these will be monitored with unprecedented cadence by the Legacy Survey of Space and Time (LSST). Many of these will undergo caustic-crossing microlensing events over the 10-year LSST survey, in which a sharp caustic feature from a stellar body in the lensing galaxy crosses the inner accretion disk. Caustic-crossing events offer the unique opportunity to probe the vicinity of the central supermassive black hole for 100s of quasars with multi-platform follow-up triggered by LSST monitoring. To prepare for these observations, we have developed detailed simulations of caustic-crossing light curves. These employ a realistic analytic model of the inner accretion disk that reveals the strong surface brightness asymmetries introduced when fully accounting for both special- and general-relativistic effects. We demonstrate that an inflection in the caustic-crossing light curve due to the innermost stable circular orbit (ISCO) can be detected in reasonable follow-up observations and can be analyzed to constrain ISCO size. We also demonstrate that a convolutional neural network can be trained to predict ISCO size more reliably than traditional approaches and can also recover source orientation with high accuracy.

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arXiv2024

How to Break the Mass Sheet Degeneracy with the Lightcurves of Microlensed Type Ia Supernovae

The standardizable nature of gravitationally lensed Type Ia supernovae (glSNe Ia) makes them an attractive target for time delay cosmography, since a source with known luminosity breaks the mass sheet degeneracy. It is known that microlensing by stars in the lensing galaxy can add significant stochastic uncertainty to the unlensed luminosity which is often much larger than the intrinsic scatter of the Ia population. In this work, we show how the temporal microlensing variations as the supernova disc expands can be used to improve the standardization of glSNe Ia. We find that SNe are standardizable if they do not cross caustics as they expand. We estimate that this will be the case for $\approx$6 doubly imaged systems and $\approx$0.3 quadruply imaged systems per year in LSST. At the end of the ten year LSST survey, these systems should enable us to test for systematics in $H_0$ due to the mass sheet degeneracy at the $1.00^{+0.07}_{-0.06}$\% level, or $1.8\pm0.2$\% if we can only extract time delays from the third of systems with counter images brighter than $i=24$ mag.

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arXiv2023

Accelerating galaxy dynamical modeling using a neural network for joint lensing and kinematics analyses

Strong gravitational lensing is a powerful tool to provide constraints on galaxy mass distributions and cosmological parameters, such as the Hubble constant, $H_0$. Nevertheless, inference of such parameters from images of lensing systems is not trivial as parameter degeneracies can limit the precision in the measured lens mass and cosmological results. External information on the mass of the lens, in the form of kinematic measurements, is needed to ensure a precise and unbiased inference. Traditionally, such kinematic information has been included in the inference after the image modeling, using spherical Jeans approximations to match the measured velocity dispersion integrated within an aperture. However, as spatially resolved kinematic measurements become available via IFU data, more sophisticated dynamical modeling is necessary. Such kinematic modeling is expensive, and constitutes a computational bottleneck which we aim to overcome with our Stellar Kinematics Neural Network (SKiNN). SKiNN emulates axisymmetric modeling using a neural network, quickly synthesizing from a given mass model a kinematic map which can be compared to the observations to evaluate a likelihood. With a joint lensing plus kinematic framework, this likelihood constrains the mass model at the same time as the imaging data. We show that SKiNN's emulation of a kinematic map is accurate to considerably better precision than can be measured (better than $1\%$ in almost all cases). Using SKiNN speeds up the likelihood evaluation by a factor of $\sim 200$. This speedup makes dynamical modeling economical, and enables lens modelers to make effective use of modern data quality in the JWST era.

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arXiv2022

Using wavelets to capture deviations from smoothness in galaxy-scale strong lenses

Modeling the mass distribution of galaxy-scale strong gravitational lenses is a task of increasing difficulty. The high-resolution and depth of imaging data now available render simple analytical forms ineffective at capturing lens structures spanning a large range in spatial scale, mass scale, and morphology. In this work, we address the problem with a novel multiscale method based on wavelets. We tested our method on simulated Hubble Space Telescope (HST) imaging data of strong lenses containing the following different types of mass substructures making them deviate from smooth models: (1) a localized small dark matter subhalo, (2) a Gaussian random field (GRF) that mimics a nonlocalized population of subhalos along the line of sight, and (3) galaxy-scale multipoles that break elliptical symmetry. We show that wavelets are able to recover all of these structures accurately. This is made technically possible by using gradient-informed optimization based on automatic differentiation over thousands of parameters, which also allow us to sample the posterior distributions of all model parameters simultaneously. By construction, our method merges the two main modeling paradigms - analytical and pixelated - with machine-learning optimization techniques into a single modular framework. It is also well-suited for the fast modeling of large samples of lenses. All methods presented here are publicly available in our new Herculens package.

astro-ph.IM↗

arXiv2022

Modeling lens potentials with continuous neural fields in galaxy-scale strong lenses

Strong gravitational lensing is a unique observational tool for studying the dark and luminous mass distribution both within and between galaxies. Given the presence of substructures, current strong lensing observations demand more complex mass models than smooth analytical profiles, such as power-law ellipsoids. In this work, we introduce a continuous neural field to predict the lensing potential at any position throughout the image plane, allowing for a nearly model-independent description of the lensing mass. We apply our method on simulated Hubble Space Telescope imaging data containing different types of perturbations to a smooth mass distribution: a localized dark subhalo, a population of subhalos, and an external shear perturbation. Assuming knowledge of the source surface brightness, we use the continuous neural field to model either the perturbations alone or the full lensing potential. In both cases, the resulting model is able to fit the imaging data, and we are able to accurately recover the properties of both the smooth potential and of the perturbations. Unlike many other deep learning methods, ours explicitly retains lensing physics (i.e., the lens equation) and introduces high flexibility in the model only where required, namely, in the lens potential. Moreover, the neural network does not require pre-training on large sets of labelled data and predicts the potential from the single observed lensing image. Our model is implemented in the fully differentiable lens modeling code Herculens.

astro-ph.IM↗

arXiv2022

TDCOSMO. VII. Boxyness/discyness in lensing galaxies : Detectability and impact on $H_0$

In the context of gravitational lensing, the density profile of lensing galaxies is often considered to be perfectly elliptical. Potential angular structures are generally ignored, except to explain flux ratios anomalies. Surprisingly, the impact of azimuthal structures on extended images of the source has not been characterized, nor its impact on the H0 inference. We address this task by creating mock images of a point source embedded in an extended source, lensed by an elliptical galaxy on which multipolar components are added to emulate boxy/discy isodensity contours. Modeling such images with a density profile free of angular structure allow us to explore the detectability of image deformation induced by the multipoles in the residual frame. Multipole deformations are almost always detectable for our highest signal-to-noise mock data. However the detectability depends on the lens ellipticity and Einstein radius, on the S/N of the data, and on the specific lens modeling strategy. Multipoles also introduce small changes to the time delays. We therefore quantify how undetected multipoles would impact H0 inference. When no multipoles are detected in the residuals, the impact on H0 for a given lens is in general less than a few km/s/Mpc, but in the worst case scenario, combining low S/N in the ring and large intrinsic boxyness/discyness, the bias on H0 can reach 10-12 km/s/Mpc. If we now look at the inference on H0 from a population of lensing galaxies, having a distribution of multipoles representative of what is found in the light-profile of elliptical galaxies, we then find a systematic bias on H0 < 1%. The comparison of our mock systems to the state-of-the-art time delay lens sample studied by the H0LiCOW and TDCOSMO collaborations, indicates that multipoles are currently unlikely to be a source of substantial systematic bias on the inferred value of H0 from time-delay lenses.

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arXiv2022

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.

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arXiv2021

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

The central dark-matter fraction of galaxies is sensitive to feedback processes during galaxy formation. 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 strong-lens survey, with those obtained from the EAGLE, Illustris, and IllustrisTNG hydro-dynamical simulations. Previous comparisons with some simulations revealed a large discrepancy, with considerably higher inferred dark-matter fractions -- by factors 2-3 -- inside half of the effective radius in observed strong-lens galaxies as compared to simulated 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 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.

astro-ph.CO↗

arXiv2021

Simulating time-varying strong lenses

We present a self-consistent and versatile forward modelling software package that can produce time series and pixel-level simulations of time-varying strongly lensed systems. The time dimension, which needs to take into account different physical mechanisms for variability such as microlensing, has been missing from existing approaches and it is of direct relevance to time delay, and consequently H0, measurements and caustic crossing event predictions. Such experiments are becoming more streamlined, especially with the advent of time domain surveys, and understanding their systematic and statistical uncertainties in a model-aware and physics-driven way can help improve their accuracy and precision. Here we demonstrate the software's capabilities by exploring the effect of measuring time delays from lensed quasars and supernovae in many wavelengths and under different microlensing and intrinsic variability assumptions. In this initial application, we find that the cadence of the observations and combining information from different wavelengths plays an important role in the correct recovery of the time delays. The Mock Lenses in Time (MOLET) software package is available at: \url{https://github.com/gvernard/molet}

astro-ph.IM↗

arXiv2020

A Quasar Microlensing Light Curve Generator for LSST

We present a tool to generate mock quasar microlensing light curves and sample them according to any observing strategy. An updated treatment of the fixed and random velocity components of observer, lens, and source is used, together with a proper alignment with the external shear defining the magnification map caustic orientation. Our tool produces quantitative results on high magnification events and caustic crossings, which we use to study three lensed quasars known to display microlensing, viz. RX J1131-1231, HE 0230-2130, and Q 2237+0305, as they would be monitored by The Rubin Observatory Legacy Survey of Space and Time (LSST). We conclude that depending on the location on the sky, the lens and source redshift, and the caustic network density, the microlensing variability may deviate significantly than the expected $\sim$20-year average time scale (Mosquera & Kochanek 2011, arXiv:1104.2356). We estimate that $\sim300$ high magnification events with $Δ$mag$>1$ mag could potentially be observed by LSST each year. The duration of the majority of high magnification events is between 10 and 100 days, requiring a very high cadence to capture and resolve them. Uniform LSST observing strategies perform the best in recovering microlensing high magnification events. Our web tool can be extended to any instrument and observing strategy, and is freely available as a service at http://gerlumph.swin.edu.au/tools/lsst_generator/, along with all the related code.

astro-ph.GA↗

arXiv2020

Quantifying the structure of strong gravitational lens potentials with uncertainty-aware deep neural networks

Gravitational lensing is a powerful tool for constraining substructure in the mass distribution of galaxies, be it from the presence of dark matter sub-halos or due to physical mechanisms affecting the baryons throughout galaxy evolution. Such substructure is hard to model and is either ignored by traditional, smooth modelling, approaches, or treated as well-localized massive perturbers. In this work, we propose a deep learning approach to quantify the statistical properties of such perturbations directly from images, where only the extended lensed source features within a mask are considered, without the need of any lens modelling. Our training data consist of mock lensed images assuming perturbing Gaussian Random Fields permeating the smooth overall lens potential, and, for the first time, using images of real galaxies as the lensed source. We employ a novel deep neural network that can handle arbitrary uncertainty intervals associated with the training dataset labels as input, provides probability distributions as output, and adopts a composite loss function. The method succeeds not only in accurately estimating the actual parameter values, but also reduces the predicted confidence intervals by 10 per cent in an unsupervised manner, i.e., without having access to the actual ground truth values. Our results are invariant to the inherent degeneracy between mass perturbations in the lens and complex brightness profiles for the source. Hence, we can quantitatively and robustly quantify the smoothness of the mass density of thousands of lenses, including confidence intervals, and provide a consistent ranking for follow-up science.

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arXiv2019

Astro2020 Science White Paper - Quasar Microlensing: Revolutionizing our Understanding of Quasar Structure and Dynamics

Microlensing by stars within distant galaxies acting as strong gravitational lenses of multiply-imaged quasars, provides a unique and direct measurement of the internal structure of the lensed quasar on nano-arcsecond scales. The measurement relies on the temporal variation of high-magnification caustic crossings which vary on timescales of days to years. Multiwavelength observations provide information from distinct emission regions in the quasar. Through monitoring of these strong gravitational lenses, a full tomographic view can emerge with Astronomical-Unit scale resolution. Work to date has demonstrated the potential of this technique in about a dozen systems. In the 2020s there will be orders of magnitude more systems to work with. Monitoring of lens systems for caustic-crossing events to enable triggering of multi-platform, multi-wavelength observations in the 2020s will fulfill the potential of quasar microlensing as a unique and comprehensive probe of active black hole structure and dynamics.

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