PINNferring the Hubble Function with Uncertainties
Cosmology and Nongalactic Astrophysics
2024-03-25 v1 Instrumentation and Methods for Astrophysics
High Energy Physics - Phenomenology
Abstract
The Hubble function characterizes a given Friedmann-Robertson-Walker spacetime and can be related to the densities of the cosmological fluids and their equations of state. We show how physics-informed neural networks (PINNs) emulate this dynamical system and provide fast predictions of the luminosity distance for a given choice of densities and equations of state, as needed for the analysis of supernova data. We use this emulator to perform a model-independent and parameter-free reconstruction of the Hubble function on the basis of supernova data. As part of this study, we develop and validate an uncertainty treatment for PINNs using a heteroscedastic loss and repulsive ensembles.
Cite
@article{arxiv.2403.13899,
title = {PINNferring the Hubble Function with Uncertainties},
author = {Lennart Röver and Björn Malte Schäfer and Tilman Plehn},
journal= {arXiv preprint arXiv:2403.13899},
year = {2024}
}
Comments
21 pages, 12 figures