RENOIR - Research for dark energy

Supernovae

It was in 1999 that the observation of supernovae made it possible for the first time to demonstrate the phenomenon of accelerated expansion of the Universe. Since then, this discovery remains unexplained and opens one of the most intriguing questions of modern cosmology. Major projects have since confirmed this result, in particular, follow-up projects of hundreds of distant supernovae.

In this framework, the CPPM has developed a supernovae observation activity for cosmology since 2004. The RENOIR group thus participated actively in the SNLS experiment in Hawaii at the CFHT. The SNLS is a project dedicated to observing several hundred distant supernovae to constrain cosmological models. The CPPM participated in photometric aspects with the development of an automatic and optimized search code for SN candidates. The CPPM also participated in the spectroscopic analysis of these objects for their identification and distance measurement. Scientific studies have focused on explosion rate measurements, and on studies of the properties of supernovae and their host galaxy.

Beyond the cosmological aspect and to improve the potential of this cosmological probe, the CPPM also works on aspects standardization and classification of supernovae with close supernovae. The CPPM participates in the collaboration between the Supernova Factory (SNFACTORY), which has already observed more than one thousand spectra in the form of spectral series and which starts a new phase of observation by privileging the discovered supernovae very soon after the explosion.

The CPPM is responsible for the production of SN Factory's reduced data. It sets up procedures to automate this production to allow the day-to-day reduction and the possibility to quickly re-process all the data.

The CPPM also works on the astrophysical nature of supernovae by simulations of radiative transfer (photon / matter interaction) that allow to calculate observables such as spectra or light curves and to compare them to the real data to constrain the models. This work is part of an ANR (RTCCSN) in collaboration with the LAM.

By Dominique Fouchez