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TDCOSMO - X. Automated modeling of nine strongly lensed quasars and comparison between lens-modeling software

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Ertl,  S.
Physical Cosmology, MPI for Astrophysics, Max Planck Society;

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Schuldt,  S.
Physical Cosmology, MPI for Astrophysics, Max Planck Society;

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Suyu,  S. H.
Physical Cosmology, MPI for Astrophysics, Max Planck Society;

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引用

Ertl, S., Schuldt, S., Suyu, S. H., Schmidt, T., Treu, T., Birrer, S., Shajib, A. J., & Sluse, D. (2023). TDCOSMO - X. Automated modeling of nine strongly lensed quasars and comparison between lens-modeling software. Astronomy and Astrophysics, 672:. doi:10.1051/0004-6361/202244909.


引用: https://hdl.handle.net/21.11116/0000-000D-A77F-9
要旨
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 h to about one hour. The active input time during the modeling process for the user is reduced by a factor of 10 from ~ 10 h 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.