https://doi.org/10.1140/epjp/s13360-023-04333-2
Regular Article
A semi-model-independent approach for describing cosmological data
Department of Physics, Bu-Ali Sina University, 65178 016016, Hamedan, Iran
Received:
6
May
2023
Accepted:
29
July
2023
Published online:
14
August
2023
In order to investigate a dataset, a model-independent or non-parametric approach has been widely used in cosmology. In these scenarios, the data used directly to reconstruct an underlying function. In this work, we introduce a novel semi-model-independent method to accomplish this task. The new approach not only removes some drawbacks of previous methods but also has some remarkable advantages. We combine the well-known Gaussian linear model with a neural network and introduce a procedure for the reconstruction of an arbitrary function. In our scenario, the neural network produces some arbitrary base functions which are subsequently fed to the Gaussian linear model. Given a prior distribution on the free parameters, the Gaussian linear model provides a closed form for the posterior distribution. In addition, unlike other methods, it is straightforward to compute the uncertainty of the reconstructed function.
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© The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
