A possible late-time transition of $M_B$ inferred via neural networks
This work tackles the Hubble constant tension in cosmology, which is a foundational issue for understanding the universe's expansion, but it appears incremental as it applies neural networks to existing data without introducing a new paradigm.
The study addressed the tension in cosmological parameters by using neural networks to model-independently constrain the absolute magnitude M_B of Type Ia supernovae from the Pantheon+ compilation, finding an indication of a possible transition redshift around z≈1.
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.