BayeSN-TD: Time Delay and H 0 Estimation for Lensed SN H0pe
Year: 2026
Type: article
Abstract: ABSTRACT We present BayeSN-TD, an enhanced implementation of the probabilistic Type Ia supernova (SN Ia) BayeSN spectral energy distribution (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 marginalizing 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 $\Delta T_{BA}=121.9^{+9.5}_{-7.5}$ d and $\Delta T_{BC}=63.2^{+3.2}_{-3.3}$ d along with absolute magnifications $\beta$ 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 $H_0$; BayeSN-TD will be a valuable tool for these analyses.
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,046
Awards: Next-Generation Data-Driven Probabilistic Modelling of Type Ia Supernova SEDs in the Optical to Near-Infrared for Robust Cosmological Inference, Collaborative Research: Community Planning for Scalable Cyberinfrastructure to Support Multi-Messenger Astrophysics, Discovering the Most Enigmatic Transient Phenomena in the Time-Domain, A Framework for Data Intensive Discovery in Multimessenger Astrophysics, NSF-Simons AI Institute for the Sky (SkAI Institute), RESEARCH IN THEORETICAL HIGH ENERGY PHYSICS, G4044828587, HEROIC - Hop Enabled Realtime Observatory Information and Coordination, G4591701419, Frameworks: SCiMMA: Real-time Orchestration of Multi-Messenger Astrophysical Observations, G58316358, CAREER: Understanding the Nature of Dark Energy with the Young Supernova Experiment and the Legacy Survey of Space and Time
OpenAlex ID: W7130330258
