Kavli Affiliate: Joshua Frieman
| First 5 Authors: Anowar J. Shajib, Nafis Sadik Nihal, Chin Yi Tan, Vedant Sahu, Simon Birrer
| Summary:
Strong gravitational lensing is a powerful tool for probing the internal
structure and evolution of galaxies, the nature of dark matter, and the
expansion history of the Universe, among many other scientific applications.
For almost all of these science cases, modeling the lensing mass distribution
is essential. For that, forward modeling of imaging data to the pixel level is
the standard method used for galaxy-scale lenses. However, the traditional
workflow of forward lens modeling necessitates a significant amount of human
investigator time, requiring iterative tweaking and tuning of the model
settings through trial and error. An automated lens modeling pipeline can
substantially reduce the need for human investigator time. In this paper, we
present textsc{dolphin}, an automated lens modeling pipeline that combines
artificial intelligence with the traditional forward modeling framework to
enable full automation of the modeling workflow. textsc{dolphin} uses a neural
network model to perform visual recognition of the strong lens components, then
autonomously sets up a lens model with appropriate complexity, and fits the
model with the modeling engine, lenstronomy. Thanks to the versatility of
lenstronomy, dolphin can autonomously model both galaxy-galaxy and
galaxy-quasar strong lenses.
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