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

¹ STAR Institute, Quartier Agora, Allée du Six Août 19c, 4000 Liège, Belgium
e-mail: mgomer@uliege.be
² Max-Planck-Institut für Astrophysik, Karl-Schwarzschild Str. 1, 85748 Garching, Germany
e-mail: ertlseb@mpa-garching.mpg.de
³ Technical University of Munich, TUM School of Natural Sciences, Department of Physics, James-Franck-Straße 1, 85748 Garching, Germany
⁴ Eidgenössische Technische Hochschule Zürich, Rämistrasse 101, 8092 Zürich, Switzerland
⁵ Institute of Physics, Laboratory of Astrophysics – École Polytechnique Fédérale de Lausanne (EPFL), 1290 Versoix, Switzerland
⁶ Department of Astrophysics, American Museum of Natural History, Central Park West and 79th Street, NY 10024, USA
⁷ Department of Physics and Astronomy, Lehman College of the City University of New York, 250 Bedford Park Boulevard, West Bronx, NY 10468, USA
⁸ Academia Sinica Institute of Astronomy and Astrophysics (ASIAA), 11F of ASMAB, No. 1, Section 4, Roosevelt Road, Taipei 10617, Taiwan

Received: 19 July 2023
Accepted: 16 September 2023

Abstract

Strong gravitational lensing is a powerful tool to provide constraints on galaxy mass distributions and cosmological parameters, such as the Hubble constant, H₀. 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 that 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 that 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 a 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 ~200. This speedup makes dynamical modeling economical, and enables lens modelers to make effective use of modern data quality in the JWST era.