Late-time transition of $M_B$ inferred via neural networks
Purba Mukherjee Konstantinos F. Dialektopoulos Jackson Levi Said Jurgen Mifsud
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 $z\approx
1$ region.