Constraining dark energy parameters or probing new cosmology with the LSST supernova dataset

Stage numéro : Doctorat-2023-RE-02
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
Directeur de thèse :Dominique Fouchez - 04 91 82 76 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 Large Synoptic Telescope (LSST) project will be launched in 2021 and will be commissioned in 2021. at full speed by the end of 2022. 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 PhD, we propose to prepare and participate in the first analysis of the data of the LSST supernova. The preparation will be carried out by working on the precise photometric measurement and photometric selection of the type Ia supernova. These two points are among the most important sources of systematic errors and all work to reduce and mitigate these sources of error will have a significant impact on the final measurement. 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]. The doctoral student will work within this framework by applying a complete analysis pipeline built with these tools, which he/she will contribute to improving, to the precursor data currently available: SNLS, SDSS, DES and HSC to validate his/her work, and will then have access to the first LSST images and supernova detections to participate in the first analysis of the LSST supernova data set.

The CPPM cosmology group is also involved in the DESI and Euclid surveys and collaborates with theorists to study alternative cosmological models, so that extensions of doctoral candidates' work can be found by combining the data with these other surveys and/or by testing a new cosmology through these new supernova data measurements.

[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