Tdcosmo

Abstract

When strong gravitational lenses are to be used as an astrophysical or cosmological probe, models of their mass distributions are often needed. We present a new, time-efficient automation code for the uniform modeling of strongly lensed quasars with GLEE, a lens-modeling software for multiband data. By using the observed positions of the lensed quasars and the spatially extended surface brightness distribution of the host galaxy of the lensed quasar, we obtain a model of the mass distribution of the lens galaxy. We applied this uniform modeling pipeline to a sample of nine strongly lensed quasars for which images were obtained with the Wide Field Camera 3 of the Hubble Space Telescope. The models show well-reconstructed light components and a good alignment between mass and light centroids in most cases. We find that the automated modeling code significantly reduces the input time during the modeling process for the user. The time for preparing the required input files is reduced by a factor of 3 from ∼3 hours to about one hour. The active input time during the modeling process for the user is reduced by a factor of 10 from ∼10 hours to about one hour per lens system. This automated uniform modeling pipeline can efficiently produce uniform models of extensive lens-system samples that can be used for further cosmological analysis. A blind test that compared our results with those of an independent automated modeling pipeline based on the modeling software Lenstronomy revealed important lessons. Quantities such as Einstein radius, astrometry, mass flattening, and position angle are generally robustly determined. Other quantities, such as the radial slope of the mass density profile and predicted time delays, depend crucially on the quality of the data and on the accuracy with which the point spread function is reconstructed. Better data and/or a more detailed analysis are necessary to elevate our automated models to cosmography grade. Nevertheless, our pipeline enables the quick selection of lenses for follow-up and further modeling, which significantly speeds up the construction of cosmography-grade models. This important step forward will help us to take advantage of the increase in the number of lenses that is expected in the coming decade, which is an increase of several orders of magnitude.

LensWatch. I. Resolved HST Observations and Constraints on the Strongly Lensed Type Ia Supernova 2022qmx (“SN Zwicky”)

“…First, we model the light of the lens galaxy with two Sérsic profiles, and the light of multiple lensed SN images by fitting a PSF model constructed from multiple stars in the field of the drizzled data. Ertl et al (2023) showed that for lensed quasars we can achieve astrometric accuracy of 2 mas from the surface brightness (SB) fit, by comparing the modeled image positions to those measured by the Gaia satellite. We use our SN image positions (from PSF fitting) to constrain the mass parameters, since we did not find (and do not expect) any substantial lensed arc light (from the SN host galaxy) in the modeling residuals of the three UVIS bands.…”

“…For each band, we use the image positions reported in Table 7 and adopt an uncertainty on the image positions of 4 mas to constrain the lens mass parameters. The 4 mas uncertainty is an estimate based on the astrometric accuracy of 2 mas (Ertl et al 2023) and to account for substructure lensing, which can perturb the image positions at the few milliarcseconds level, as shown by Chen et al (2007). We impose uniform priors on all eight lens mass parameters that are tabulated in the leftmost column of Table 9.…”

“…We modeled SN Zwicky with an automated modeling pipeline that is based on the modeling software GLEE (Suyu & Halkola 2010;Suyu et al 2012;Ertl et al 2023), where we adopt the SPEMD (Barkana 1998) profile whose dimensionless surface mass density (or convergence) is given by…”

“…Given these different potential methodologies, we used three independent software packages and five total methods to carry out independent analyses of SN Zwicky, which provide an examination of the potential modeling systematics and allow us to marginalize over them (e.g., Ertl et al 2023;Shajib et al 2022). The three software packages are lfit_gui (Shu et al 2016), LENSTRONOMY (Birrer et al 2015;Birrer & Amara 2018;Birrer et al 2021), and the Gravitational Lens Efficient Explorer (GLEE; Suyu & Halkola 2010;Suyu et al 2012;Ertl et al 2023), and the five methods explored here are summarized by Table 2. Using the first four models reported in Table 2, we found a significantly higher reduced-χ 2 when fitting both image positions and fluxes compared to fitting only image positions, indicating the presence of substructure and/or microlensing not captured by the lens modeling process.…”

Holismokes

“…The latter is the best match to our networks, and is comparable in performance, as mentioned in S21a. There is also currently a lot of work going into automated modeling without machine learning (e.g., Nightingale et al 2018Nightingale et al , 2021Rojas et al 2022;Savary et al 2022;Ertl et al 2023;Etherington et al 2022;Gu et al 2022;Schmidt et al 2023), which typically performs better than neural networks but requires significantly longer run times of hours to days. We refer to Schuldt et al (2022) for a direct comparison between the network presented here and traditionally obtained models for real HSC lenses.…”