On the robustness of the constancy of the Supernova absolute magnitude: Non-parametric reconstruction & Bayesian approaches

David Benisty, Jurgen Mifsud, Jackson Levi Said, Denitsa Staicova

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

In this work, we test the robustness of the constancy of the Supernova absolute magnitude MB using Non-parametric Reconstruction Techniques (NRT). We isolate the luminosity distance parameter dL(z) from the Baryon Acoustic Oscillations (BAO) data set and cancel the expansion part from the observed distance modulus μ(z). Consequently, the degeneracy between the absolute magnitude and the Hubble constant H0, is replaced by a degeneracy between MB and the sound horizon at drag epoch rd. When imposing the rd value, this yields the MB(z)=MB+δMB(z) value from NRT. We perform the respective reconstructions using the model independent Artificial Neural Network (ANN) technique and Gaussian processes (GP) regression. For the ANN we infer MB=−19.22±0.20, and for the GP we get MB=−19.25±0.39 as a mean for the full distribution when using the sound horizon from late time measurements. These estimations provide a 1σ possibility of a nuisance parameter presence δMB(z) at higher redshifts. We also tested different known nuisance models with the Markov Chain Monte Carlo (MCMC) technique which showed a strong preference for the constant model, but it was not possible not single out a best fit nuisance model.

Original languageEnglish
Article number101160
JournalPhysics of the Dark Universe
Volume39
DOIs
StatePublished - 1 Feb 2023
Externally publishedYes

Keywords

  • Absolute magnitude variation
  • Dark energy
  • Machine learning in cosmology

ASJC Scopus subject areas

  • Astronomy and Astrophysics
  • Space and Planetary Science

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