  Supernova (SN) classification and redshift estimation using photometric data
only have become very important for the Large Synoptic Survey Telescope (LSST),
given the large number of SNe that LSST will observe and the impossibility of
spectroscopically following up all the SNe. We investigate the performance of a
SN classifier that uses SN colors to classify LSST SNe with the Random Forest
classification algorithm. Our classifier results in an AUC of 0.98 which
represents excellent classification. We are able to obtain a photometric SN
sample containing 99$\%$ SNe Ia by choosing a probability threshold. We
estimate the photometric redshifts (photo-z) of SNe in our sample by fitting
the SN light curves using the SALT2 model with nested sampling. We obtain a
mean bias ($\left<z_\mathrm{phot}-z_\mathrm{spec}\right>$) of 0.012 with
$\sigma\left( \frac{z_\mathrm{phot}-z_\mathrm{spec}}{1+z_\mathrm{spec}}\right)
= 0.0294$ without using a host-galaxy photo-z prior, and a mean bias
($\left<z_\mathrm{phot}-z_\mathrm{spec}\right>$) of 0.0017 with $\sigma\left(
\frac{z_\mathrm{phot}-z_\mathrm{spec}}{1+z_\mathrm{spec}}\right) = 0.0116$
using a host-galaxy photo-z prior. Assuming a flat $\Lambda CDM$ model with
$\Omega_m=0.3$, we obtain $\Omega_m$ of $0.305\pm0.008$ (statistical errors
only), using the simulated LSST sample of photometric SNe Ia (with intrinsic
scatter $\sigma_\mathrm{int}=0.11$) derived using our methodology without using
host-galaxy photo-z prior. Our method will help boost the power of SNe from the
LSST as cosmological probes.
