BibTeX

@article{2504.10553v1,
Author        = {Gonçalo Gonçalves and Nikki Arendse and Doogesh Kodi Ramanah and Radosław Wojtak},
Title         = {Inferring the Hubble Constant Using Simulated Strongly Lensed Supernovae
  and Neural Network Ensembles},
Eprint        = {2504.10553v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {astro-ph.IM},
Abstract      = {Strongly lensed supernovae are a promising new probe to obtain independent
measurements of the Hubble constant (${H_0}$). In this work, we employ
simulated gravitationally lensed Type Ia supernovae (glSNe Ia) to train our
machine learning (ML) pipeline to constrain $H_0$. We simulate image
time-series of glSNIa, as observed with the upcoming Nancy Grace Roman Space
Telescope, that we employ for training an ensemble of five convolutional neural
networks (CNNs). The outputs of this ensemble network are combined with a
simulation-based inference (SBI) framework to quantify the uncertainties on the
network predictions and infer full posteriors for the $H_0$ estimates. We
illustrate that the combination of multiple glSN systems enhances constraint
precision, providing a $4.4\%$ estimate of $H_0$ based on 100 simulated
systems, which is in agreement with the ground truth. This research highlights
the potential of leveraging the capabilities of ML with glSNe systems to obtain
a pipeline capable of fast and automated $H_0$ measurements.},
Year          = {2025},
Month         = {Apr},
Url           = {http://arxiv.org/abs/2504.10553v1},
File          = {2504.10553v1.pdf}
}