Constraining dark energy parameters or probing new cosmology with supernova dataset

Stage numéro : M2-2021-RE-03
Laboratoire :Centre de Physique des Particules de Marseille Case 902
 163 avenue de Luminy - 13288 Marseille Cedex 9
Directeur :Cristinel Diaconu - 04.91.82.72.01 - diaconu@cppm.in2p3.fr
Correspondant :William Gillard - 04.91.82.72.67 - gillard@cppm.in2p3.fr
Groupe d'accueil :Renoir
Chef de groupe :Dominique Fouchez - 04.91.82.72.49 - fouchez@cppm.in2p3.fr
Responsable de stage :Dominique Fouchez - 04.91.82.72.49 - fouchez@cppm.in2p3.fr

Thématique : Cosmologie observationnelle

Twenty years after the discovery of the current acceleration of the expansion of the universe by supernova measurements, the supernova probe remains the most accurate way to measure the parameters of this recent period in the history of our universe dominated by the so-called dark energy.

The precision measurements that can be performed by the supenova probe will be a crucial element that, in combination with other probes (LSS, weak lenses, CMB, etc.), will put strong constraints on the nature of dark energy. This will be made possible by the exceptional Supernova data set to be provided by LSST, with a combination of huge statistics and extreme calibration accuracy.

The Rubin observatory with the Large Survey of Space and Time (Rubin/LSST) project will be commissioned in 2022 and will run at full speed by the end of 2023. It is an 8.4-metre telescope with a 3.2 billion pixel camera, the most powerful ever built.

This telescope will take a picture of half the sky every three nights for ten years. This survey will make it possible to measure billions of galaxies with great accuracy and to track the variation over time of all transient objects. With many other astrophysical studies, it will be a very powerful machine for determining cosmological parameters using many different probes and, in particular, it will impose strong constraints on the nature of dark energy. The LSST project aims to discover up to half a million supernovae. This two to three orders of magnitude improvement in statistics over the current data set will allow accurate testing of dark energy parameters and will also impose new constraints on the universe's isotropy.

In this Master 2 internship we propose to prepare the first analysis of LSST supernova data by performing an analysis using LSST software and our deep learning method for identifying supernova on existing HSC/Subsaru data. Indeed, the HSC data has characteristics that are very close to what we expect with Rubin/LSST. The CPPM LSST group is already engaged in precision photometry work for LSST with direct involvement in algorithm validation within DESC/LSST [1][2][3] and has proposed a new deep learning method to improve the photometric identification of supernovae [4] and photometric redshifts [5].

[1] https://www.lsst.org/content/lsst-science-drivers-reference-design-and-anticipated-data-products

[2] https://arxiv.org/abs/1211.0310

[3] https://www.lsst.org/about/dm

[4] https://arxiv.org/abs/1901.01298

[5] https://arxiv.org/abs/1806.06607

[6] https://arxiv.org/abs/1401.4064