DOI: 10.58119/ulg/wzfxyd
title: Weights and training data for SKiNN (Stellar Kinematics Neural Network)

description: Weights and training data for the Stellar Kinematics Neural Network (SKiNN). The SKiNN software can be found at https://github.com/mattgomer/SKiNN and is also archived on Software Heritage The full usage of SKiNN is described in the article Accelerating galaxy dynamical modeling using a neural network for joint lensing and kinematic analyses Matthew R. Gomer, Sebastian Ertl, Luca Biggio, Han Wang, Aymeric Galan, Lyne Van de Vyvere, Dominique Sluse, Georgios Vernardos, Sherry H. Suyu A&A 679 A59 (2023) DOI: 10.1051/0004-6361/202347507. Here is the abstract of the paper explaining the scientific context : 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. More precisely, this dataset contains the training data described in section 3.1 of the paper and the resulting weights to use the SKiNN after training. The training data (4000 pairs) consist a parameters.npy file, that is sets of parameters - describing the PEMD mass, the elliptcial sersic light and the kinematics parameters - and a vrms_maps.npy file containing the corresponding kinematic map - created using the JAM software and the MGE method. The weights is a single file weights.ckpt which needs to be imported as described in the setup of SKiNN to enable the creation of a kinematic map from a set of parameters describing the galaxy properties. NB: the parameters are ordered as following: q_mass (axis ratio of the mass profile), q_light (axis ratio of the light profile), theta_E (Einstein radius), n_sersic (Sersic index of the light), R_sersic (Sersic radius of the light), r_core (Core radius set to 0.08 arcsec, see paper section 3.1 for more information), gamma (mass profile slope), b_ani (anisotropy), i (inclination).