A Novel Lensed Point Source Modeling Pipeline using GIGA-Lens with Application to SN Zwicky and SN iPTF16geu

TL;DR

This paper presents a new pipeline for modeling strongly lensed point sources using GIGA-Lens, demonstrating accurate parameter recovery and application to real supernova data, achieving competitive H0 precision.

Contribution

The novel pipeline enables simultaneous inference of lens parameters and H0 from lensed point sources using GIGA-Lens on GPUs, with successful application to real supernova data.

Findings

Accurate recovery of image positions, fluxes, and time delays.

Achieved ~3.6% uncertainty on H0 from a single system.

Consistent mass parameter inference for SN iPTF16geu, with some differences for SN Zwicky.

Abstract

We introduce a novel modeling pipeline for strongly lensed point sources, using the GIGA-Lens framework, running on four A100 GPUs via the JAX platform. Using simulations, we demonstrate accurate and precise recovery of image positions, fluxes, and time delays, together with inference of complex lens mass distributions -- including the mass density slope, $\gamma$ -- from images of lensed point sources alone. We further show that we can achieve statistical uncertainty of $\sim 3.6\%$ ($\sim 2.5\, \mathrm{km\, s^{-1}/Mpc}$) on $H_0$ from a single system, with full forward modeling, i.e., simultaneous inference of all lens model parameters together with $H_0$. We apply our pipeline to two well-studied lensed SNe Ia, Zwicky and iPTF16geu. For SN iPTF16geu, unlike previous modeling efforts, we model only the images of the lensed point source (the SN) and do not use the lensed images of the extended host-galaxy. Nevertheless, we are able to infer all of the mass parameters modeled in earlier studies, and our best-fit values, including $\gamma$, are fully consistent with published results. In the case of SN Zwicky, taking the same approach, however, we obtain an alternative best-fit model compared to published results, underscoring the importance of fully exploring the model parameter space.