The discovery of the accelerating expansion of the Universe though the observation of distant type Ia supernovae (SNIa) showed that the Universe consists of 70% of "dark energy" whose nature remains unknown.

At present, one of the best approaches for determining the properties of dark energy is to measure its equation of state parameter from mapping the history of cosmic expansion using SNe Ia. However, SNe Ia measurements are dominated by systematic uncertainties, mainly related to photometric calibration procedures, as well as systematic uncertainties that comes from the remaining variability of SNe Ia. These different sources of systematics limit the accuracy of the measurement of dark energy properties. It is therefore necessary to understand and correct these sources of systematics in order to improve distance measurement on SNe Ia and consequently constraint on dark energy. This is one of the main objectives of upcoming ground based surveys such as LSST.

During this seminar I will focus on the unmodeled physical properties of SNIa and on how spectral information can help improve the precision of distances of SNe Ia. I will first present the current cosmological context, focusing on the state of the art of using SNe Ia in cosmology. I will then discuss the limitations of the current method which is based on light curves properties (stretch, color). Then, I will introduce the new SNIa model called the SUpernova Generator and Reconstructor (SUGAR) which I developed within The Nearby Supernova Factory collaboration and which improves significantly the spectrophotometric description of SNe Ia, hence the distance inference . Finally, I will talk about the implication of the use of SUGAR in the context of LSST and how it will help to understand properties of dark energy in the context of the LSST ground based survey.