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DC Field | Value | Language |
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dc.contributor.author | Benisty, David | - |
dc.contributor.author | Mifsud, Jurgen | - |
dc.contributor.author | Said, Jackson | - |
dc.contributor.author | Staicova, Denitsa | - |
dc.date.accessioned | 2024-04-22T14:54:18Z | - |
dc.date.available | 2024-04-22T14:54:18Z | - |
dc.date.issued | 2023 | - |
dc.identifier.citation | Benisty, D., Mifsud, J., Said, J. L., & Staicova, D. (2023). On the robustness of the constancy of the Supernova absolute magnitude: non-parametric reconstruction & Bayesian approaches. Physics of the Dark Universe, 39, 101160. | en_GB |
dc.identifier.issn | 22126864 | - |
dc.identifier.uri | https://www.um.edu.mt/library/oar/handle/123456789/121145 | - |
dc.description.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 1 σ 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. | en_GB |
dc.language.iso | en | en_GB |
dc.publisher | Elsevier BV | en_GB |
dc.rights | info:eu-repo/semantics/restrictedAccess | en_GB |
dc.subject | Gravitation | en_GB |
dc.subject | Cosmology -- Observations | en_GB |
dc.subject | Dark energy (Astronomy) | en_GB |
dc.subject | Astrophysics -- Mathematical models | en_GB |
dc.subject | Cosmological constants | en_GB |
dc.subject | Astrophysics -- Data processing | en_GB |
dc.subject | Machine learning | en_GB |
dc.title | On the robustness of the constancy of the supernova absolute magnitude : nonparametric reconstruction & Bayesian approaches | en_GB |
dc.type | article | en_GB |
dc.rights.holder | The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder. | en_GB |
dc.description.reviewed | peer-reviewed | en_GB |
dc.identifier.doi | 10.1016/j.dark.2022.101160 | - |
dc.publication.title | Physics of the Dark Universe | en_GB |
Appears in Collections: | Scholarly Works - InsSSA |
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