We develop a new framework for use in exploring Type Ia Supernova (SN Ia) spectra. Combining Principal Component Analysis (PCA) and Partial Least Square analysis (PLS) we are able to establish correlations between the Principal Components (PCs) and s
pectroscopic/photometric SNe Ia features. The technique was applied to ~120 supernova and ~800 spectra from the Nearby Supernova Factory. The ability of PCA to group together SNe Ia with similar spectral features, already explored in previous studies, is greatly enhanced by two important modifications: (1) the initial data matrix is built using derivatives of spectra over the wavelength, which increases the weight of weak lines and discards extinction, and (2) we extract time evolution information through the use of entire spectral sequences concatenated in each line of the input data matrix. These allow us to define a stable PC parameter space which can be used to characterize synthetic SN Ia spectra by means of real SN features. Using PLS, we demonstrate that the information from important previously known spectral indicators (namely the pseudo-equivalent width (pEW) of Si II 5972 / Si II 6355 and the line velocity of S II 5640 / Si II 6355) at a given epoch, is contained within the PC space and can be determined through a linear combination of the most important PCs. We also show that the PC space encompasses photometric features like B or V magnitudes, B-V color and SALT2 parameters c and x1. The observed colors and magnitudes, that are heavily affected by extinction, cannot be reconstructed using this technique alone. All the above mentioned applications allowed us to construct a metric space for comparing synthetic SN Ia spectra with observations.
We explore SNIa as p-process sources in the framework of two-dimensional SNIa models using enhanced s-seed distributions as directly obtained from a sequence of thermal pulse instabilities. The SNIa WD precursor is assumed to have reached the Chandra
sekhar mass limit in a binary system by mass accretion from a giant/main sequence companion. We apply the tracer-particle method to reconstruct the nucleosynthesis from the thermal histories of Lagrangian particles, passively advected in the hydrodynamic calculations. For each particle we follow the explosive nucleosynthesis with a detailed nuclear reaction network. We select tracers within the typical temperature range for p-process production, 1.5-3.7 109K, and analyse in detail their behaviour, exploring the influence of different s-process distributions on the p-process nucleosynthesis. We find that SNIa contribute to a large fraction of p-nuclei, both the light p-nuclei and the heavy-p nuclei at a quite flat average production factor. For the first time, the very abundant Ru and Mo p-isotopes are reproduced at the same level as the heavy p-nuclei. We investigate the metallicity effect on the p-process production. Starting with a range of s-seeds distributions obtained for different metallicities, running SNIa two-dimensional models and using a simple chemical evolution code, we give estimates of the SNIa contribution to the solar p-process composition. We find that SNIa contribute for at least 50% at the solar p-nuclei composition, in a primary way.
Despite the importance of Type Ia supernovae as standard candles for cosmology and to the chemical evolution of the Universe, we still have no consistent picture of the nature of these events. Much progress has been made in the hydrodynamical explosi
on modelling of supernovae Ia in the last few years and fully 3-D explosion models are now available. However those simulations are not directly comparable to observations: to constrain explosion models, radiative transfer calculations must be carried out. We present a new 3-D Monte Carlo radiative transfer code which allows forward modelling of the spectral evolution of Type Ia supernovae from first principles, using hydrodynamical explosion models as input. Here, as a first application, we calculate line-of-sight dependent colour light curves for a toy model of an off-centre explosion.
