Future large surveys like the Large Synoptic Survey Telescope (LSST) aim to increase the precision and accuracy of observational cosmology. In particular, LSST will observe a large quantity of well-sampled type Ia supernovae (SNIa) that will be one of the major probes of dark energy. However the spectroscopic follow-up for the identification of SN and the redshift estimation of their host galaxy will be limited. Therefore new automatic classification and regression methods, that exploit the photometric information only, become indispensable.
I will present two separate deep convolutional architectures to classify SN light curves and estimate photometric redshifts. PELICAN (deeP architecturE for the LIght Curve ANalysis) is designed to characterize and classify light curves from multi-band light curves only. Despite using a small and non-representative spectroscopic training dataset (2,000 LSST simulated light curves) PELICAN is able to detect 85% of SNIa with a precision higher than 98%.
The second Convolutional Neural Network (CNN) was developed to estimate galaxy photometric redshifts and associated probability distribution functions. It was tested on the Main Galaxy Sample of the Sloan Digital Sky Survey (DR12). The input consisted of 64x64 ugriz images and the CNN was trained with 80% of the statistics. I obtained a standard deviation σ(Delta z) of 0.0091 (Delta z=(zspec-zphot)/(1+zspec)) with an outlier fraction of 0.3%. This is a significant improvement over the current state-of-the-art value (σ ~ 0.0120, Beck et al. 2016). Using SNIa candidates that were well-classified by PELICAN and whose host galaxy photometric redshifts were estimated by the CNN, we are able to construct a Hubble Diagram from photometric information only.