SciPost Submission Page
PINNferring the Hubble Function with Uncertainties
by Lennart Röver, Björn Malte Schäfer, Tilman Plehn
Submission summary
| Authors (as registered SciPost users): | Tilman Plehn · Lennart Röver |
| Submission information | |
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| Preprint Link: | https://arxiv.org/abs/2403.13899v1 (pdf) |
| Date submitted: | 2024-04-15 15:46 |
| Submitted by: | Röver, Lennart |
| Submitted to: | SciPost Physics |
| Ontological classification | |
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| Academic field: | Physics |
| Specialties: |
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| Approaches: | Computational, Phenomenological |
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.
Current status:
In refereeing