Towards automated modeling of group and cluster scale strong lenses in LSST
Year: 2024
Type: article
Abstract: The Legacy Survey of Space and Time (LSST) will vastly increase the number of known stronglenses, demanding scalable and automated modeling solutions. While most existing efforts focus ongalaxy-scale lenses, the LSST will reveal many group and cluster-scale lenses, which are morecomplex in their mass distribution, requiring novel modeling methods. This study aims to develop andvalidate an algorithm for the efficient modeling group and cluster-scale strong lens systems featuringextended arcs using ground-based data. Our algorithm builds on existing software for galaxy-scalelens modeling, incorporating halo and subhalo components to account for complex mass distributions.To enhance computational efficiency we employ GPU parallelization, an inference method thatcombines image position and pixelated data, and a Taylor expansion for the subhalo massdistribution. We validate our algorithm using both simulated and real lenses. Our method successfullymodels complex lens systems within a minute, accurately recovering lens parameters and theirassociated uncertainties.
Author: Urcelay, Felipe
Institution: Pontificia Universidad Católica de Chile
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6930447359
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