# Neural network reconstruction of cosmology using the Pantheon compilation

arXiv: https://arxiv.org/abs/2305.15499 · HTML: https://mlsanity.com/paper/2305.15499/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 103 references that are in the map.
- First posted on arXiv 24 May 2023.

## Builds on (most cited first, 2 of 2)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)](https://mlsanity.com/paper/1511.07289/) (2015)

## Built on by (most cited first, 4 of 4)

- [LADDER: Revisiting the Cosmic Distance Ladder with Deep Learning Approaches and Exploring its Applications](https://mlsanity.com/paper/2401.17029/) (2024)
- [A possible late-time transition of MB inferred via neural networks](https://mlsanity.com/paper/2402.10502/) (2024)
- [Model-independent cosmological inference post DESI DR1 BAO measurements](https://mlsanity.com/paper/2405.19178/) (2024)
- [Dark energy reconstruction analysis with artificial neural networks: Application on simulated Supernova Ia data from Rubin Observatory](https://mlsanity.com/paper/2402.18124/) (2024)

## 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.