fitters, in the case of the Sloan Digital Sky Survey (SDSS) first year compilation [@Kessler2009]. Finally, in Section \[sec:conclusions\], we end up with some discussions and conclusions.

Light-curve fitting {#sec:light_curve_fitting}
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The “primary” data of any SN Ia survey are apparent magnitudes (or fluxes) in a given set of filters, at a series of epochs (phases), for each supernova. This constitutes an array $m_{i,Y,\gamma}$ ($f_{i,Y,\gamma}$), where $i$ labels the supernova, $Y$ labels the filter (or band) and $\gamma$ labels the epoch in the time series. For a given supernova $i$, observed in a given filter $Y$, the scatterplot of the points $(t_{\gamma},m_{i,Y,\gamma})$, where $t_\gamma$ is the observed time, is what we will call (a sampling of) the raw light curve.

As mentioned in Section \[sec:introduction\], SNe Ia are *standardizable* candles; this means there is a phenomenological recipe whereby the raw light curves, after being subjected to a transformation by a $N_{\rm st}$-parameter function, furnish a new set of so-called *standardized* light curves; this means the dispersion in the new magnitudes is considerably smaller than in the original input set. The aforementioned phenomenological recipe is not unique at all: there are several light-curve fitters in the literature [@Jha2007; @Wang2003; @Wang2005a; @Guy2005; 