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Fast kinematics modeling for conjunction with lens image modeling
Matthew Gomer · Luca Biggio · Sebastian Ertl · Han Wang · Aymeric Galan · Lyne Van de Vyvere · Dominique Sluse · Georgios Vernardos · Sherry Suyu

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in Machine Learning and the Physical Sciences »
Galaxy kinematics modeling is currently the computational bottleneck for a joint gravitational lensing+kinematics modeling procedure. We present as a proof of concept the Stellar Kinematics Neural Network (SKiNN), which emulates kinematics calculations for the context of gravitational lens modeling. After a one-time upfront training cost, SKiNN creates velocity dispersion images which are accurate to $\lesssim1\%$ within the region of interest at a speed $\mathcal{O}(10^2-10^3)$ times faster than existing kinematics modeling methods. This speedup makes it feasible to jointly model lensing data with spatially resolved kinematic data, which corrects for the largest source of uncertainty in the determination of the Hubble constant.
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Author Information

Matthew Gomer (Université de Liège)
Luca Biggio (ETH Zürich)
Sebastian Ertl (MPA Garching)
Han Wang (MPA Garching)
Aymeric Galan (EPFL)
Lyne Van de Vyvere (Université de Liège)
Dominique Sluse (Université de Liège)
Georgios Vernardos (EPFL)
Sherry Suyu (MPA Garching)

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