# A possible late-time transition of MB inferred via neural networks

arXiv: https://arxiv.org/abs/2402.10502 · HTML: https://mlsanity.com/paper/2402.10502/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 164 references that are in the map.
- First posted on arXiv 16 Feb 2024.

## Builds on (most cited first, 6 of 6)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)](https://mlsanity.com/paper/1511.07289/) (2015)
- [Neural network reconstruction of cosmology using the Pantheon compilation](https://mlsanity.com/paper/2305.15499/) (2023)
- [LADDER: Revisiting the Cosmic Distance Ladder with Deep Learning Approaches and Exploring its Applications](https://mlsanity.com/paper/2401.17029/) (2024)
- [Reconstructing the Hubble parameter with future Gravitational Wave missions using Machine Learning](https://mlsanity.com/paper/2303.05169/) (2023)

## Built on by (most cited first, 2 of 2)

- [Model-independent cosmological inference post DESI DR1 BAO measurements](https://mlsanity.com/paper/2405.19178/) (2024)
- [What can we learn about Reionization astrophysical parameters using Gaussian Process Regression?](https://mlsanity.com/paper/2407.19481/) (2024)

## Abstract (arXiv)

The strengthening of tensions in the cosmological parameters has led to a reconsideration of fundamental aspects of standard cosmology. The tension in the Hubble constant can also be viewed as a tension between local and early Universe constraints on the absolute magnitude MB of Type Ia supernova. In this work, we reconsider the possibility of a variation of this parameter in a model-independent way. We employ neural networks to agnostically constrain the value of the absolute magnitude as well as assess the impact and statistical significance of a variation in MB with redshift from the Pantheon+ compilation, together with a thorough analysis of the neural network architecture. We find an indication for a possible transition redshift at the z≈ 1 region.