Late-time transition of M_B inferred via neural networks

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 transition redshift at the z≈ 1 region.