mlsanityData to 7 Oct 2026

Neural network reconstruction of cosmology using the Pantheon compilation

arXiv 2305.15499 ↗First posted 24 May 2023gr-qc astro-ph.CO cs.LGcited by 4

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Abstract (arXiv)

In this work, we reconstruct the Hubble diagram using various data sets, including correlated ones, in Artificial Neural Networks (ANN). Using ReFANN, that was built for data sets with independent uncertainties, we expand it to include non-Guassian data points, as well as data sets with covariance matrices among others. Furthermore, we compare our results with the existing ones derived from Gaussian processes and we also perform null tests in order to test the validity of the concordance model of cosmology.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 2 of its 103 references that are in the map. First posted on arXiv 24 May 2023.

Builds on

2 of 103 references.

References · 2

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2015Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)cited by 1,061

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Citing papers per year: 2024: 4.

4 papers.

  1. 2024LADDER: Revisiting the Cosmic Distance Ladder with Deep Learning Approaches and Exploring its Applicationscited by 4
  2. 2024A possible late-time transition of MB inferred via neural networkscited by 2
  3. 2024Model-independent cosmological inference post DESI DR1 BAO measurementscited by 1
  4. 2024Dark energy reconstruction analysis with artificial neural networks: Application on simulated Supernova Ia data from Rubin Observatorycited by 0