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

Purba Mukherjee, Konstantinos F. Dialektopoulos, Jackson Levi Said, Jurgen Mifsud

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.

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

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 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 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 region.
Paper Structure (11 sections, 10 equations, 9 figures, 2 tables)

This paper contains 11 sections, 10 equations, 9 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Artificial Neural Networks
  3. Reconstruction Methodology
  4. Observational Data sets
  5. ANN Training and Validation
  6. Theoretical Framework
  7. Results and Discussions
  8. Constraints on
  9. Data-driven transition of
  10. Conclusion
  11. Method Validation with Mock Simulation

Figures (9)

  • Figure 1: The adopted ANN architecture is shown, where the input is the redshift of a cosmological parameter $\Upsilon(z)$, and the outputs are the corresponding value and error of $\Upsilon(z)$.
  • Figure 2: Plots illustrating the evolution of training loss and validation loss across different epochs (left panel). ANN reconstruction of the Pantheon+ SNIa apparent magnitudes $m(z)$ as a function of the redshift $(z)$.
  • Figure 3: ANN reconstruction of the Pantheon+ apparent magnitudes $m(z)$ (left panel) and its corresponding derivatives $m^{\prime}(z)$ [right panel] at the CC redshifts $(z_{\text{CC}})$.
  • Figure 4: Normalized covariance matrices between ANN reconstruction of the Pantheon+ SN-Ia apparent magnitudes $m(z)$ and its corresponding derivatives $m^{\prime}(z)$ at the CC redshifts $(z_{\text{CC}})$.
  • Figure 5: Marginalized posteriors of supernovae apparent magnitude $M_B$ as a function of the number of considered ANN reconstructions. The obtained constraints obtained are $M_B$ = $-19.352^{+0.073}_{-0.079}$, $-19.352^{+0.073}_{-0.079}$, $-19.353^{+0.073}_{-0.078}$, $-19.353^{+0.073}_{-0.078}$, and $-19.353^{+0.073}_{-0.078}$, corresponding to 100, 200, 300, 400, and 500 ANN reconstructions.
  • ...and 4 more figures