A possible late-time transition of $M_B$ inferred via neural networks

TL;DR

This paper uses neural networks to investigate potential variations in the supernova absolute magnitude $M_B$ over redshift, suggesting a possible transition around redshift 1, which could impact cosmological parameter tensions.

Contribution

It introduces a model-independent neural network approach to constrain $M_B$ variation with redshift, providing new insights into late-time cosmological transitions.

Findings

Indication of a transition in $M_B$ at redshift ~1

Neural networks effectively constrain $M_B$ variation

Supports the possibility of late-time cosmological changes

Abstract

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 $M_B$ 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 $M_B$ 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 $z\approx 1$ region.