BayeSN-TD: Time Delay and $H_0$ Estimation for Lensed SN H0pe

Abstract

We present BayeSN-TD, an enhanced implementation of the probabilistic type Ia supernova (SN Ia) BayeSN SED model, designed for fitting multiply-imaged, gravitationally lensed type Ia supernovae (glSNe Ia). BayeSN-TD fits for magnifications and time-delays across multiple images while marginalising over an achromatic, Gaussian process-based treatment of microlensing, to allow for time-dependent deviations from a typical SN Ia SED caused by gravitational lensing by stars in the lensing system. BayeSN-TD is able to robustly infer time delays and produce well-calibrated uncertainties, even when applied to simulations based on a different SED model and incorporating chromatic microlensing, strongly validating its suitability for time-delay cosmography. We then apply BayeSN-TD to publicly available photometry of the glSN Ia SN H0pe, inferring time delays between images BA and BC of $\hat{I}”T_{BA}=121.9_{-7.5}^{+9.5}$ days and $\hat{I}”T_{BC}=63.2_{-3.3}^{+3.2}$ days along with absolute magnifications ${\hat{I}}^2$ for each image, ${\hat{I}}_A^2=2.38_{-0.54}^{+0.72}$, ${\hat{I}}_B^2=5.27_{-1.02}^{+1.25}$ and ${\hat{I}}_C^2=3.93_{-0.75}^{+1.00}$. Combining our constraints on time-delays and magnifications with existing lens models of this system, we infer $H_0=69.3_{-7.8}^{+12.6}$ km s${}^{-1}$ Mpc${}^{-1}$, consistent with previous analysis of this system; incorporating additional constraints based on spectroscopy yields $H_0=66.8_{-5.4}^{+13.4}$ km s${}^{-1}$ Mpc${}^{-1}$. While this is not yet precise enough to draw a meaningful conclusion with regard to the `Hubble tension', upcoming analysis of SN H0pe with more accurate photometry enabled by template images, and other glSNe, will provide stronger constraints on $H_0$; BayeSN-TD will be a valuable tool for these analyses.

21 pages, 11 figures, 4 tables. Accepted by MNRAS. BayeSN-TD code available at github.com/bayesn/bayesn-td

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