BayeSN-TD: Time Delay and <i>H</i> 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 $\Delta T_{BA}=121.9^{+9.5}_{-7.5}$ days and $\Delta T_{BC}=63.2^{+3.2}_{-3.3}$ days along with absolute magnifications β for each image, $\beta _A = 2.38^{+0.72}_{-0.54}$, $\beta _B=5.27^{+1.25}_{-1.02}$ and $\beta _C=3.93^{+1.00}_{-0.75}$. Combining our constraints on time-delays and magnifications with existing lens models of this system, we infer $H_0=69.3^{+12.6}_{-7.8}$ km s−1 Mpc−1, consistent with previous analysis of this system; incorporating additional constraints based on spectroscopy yields $H_0=66.8^{+13.4}_{-5.4}$ 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 H0; BayeSN-TD will be a valuable tool for these analyses. The BayeSN-TD code is available at https://github.com/bayesn/bayesn-td.