GEPI

Advisor:

• Simona Mei (Observatoire de Paris / Université Paris 7)

This Ph.D relies on a unique imaging dataset (0.6-4.5µm; KPNO, Palomar, Keck, HST, Spitzer) of the Lynx supercluster. This structure, which lies at z≈1.3, is one of the most distant supercluster known to date and is constituted of two clusters and at least three groups, all of them spectroscopically confirmed. With the addition of the GOODS/CDFS data, we built a sample of 58 early-type galaxies at z≈1.3 belonging to three different environments (cluster, group and field). Thanks to this sample, we can probe the influence of the mass and of the environment on the evolution of galaxies at z≈1.3, together with their size evolution.

We first built a homogeneous photometric catalogue using corrected aperture photometry. We then fitted the spectral energy distributions with stellar population models (Bruzual & Charlot 2003, Maraston 2005, Charlot & Bruzual, in preparation) and thus derived stellar masses and ages.

We study the influence of the used model on the mass and age estimations and conclude that significant differences are still present, because of the different modeling of the TP-AGB phase. However, according to all models, the most massive galaxies are the oldest ones. According to Maraston 2005 and Charlot & Bruzual (in prep.) models, though that the mass distributions are similar in the clusters and in the groups, cluster galaxies are slightly older than the group ones. Concerning field galaxies, our sample is almost free of massive (M > 10¹¹ M_⊙) galaxies: they are on average younger than the cluster/group ones.

We used HST/ACS images and Galfit code to study the size of the galaxies. We study the Kormendy relation (size vs. surface brightness), which is already in place ate z≈1.3 and shows no dependence on the environment. In addition, numerous recent works mentioned the existence of very compact galaxies at z≈1-2. With the building of the mass-size diagram, we conclude that the model used and the age of the considered galaxies significantly affects the conclusions.