Photometric classification of type Ia supernovae in the SuperNova Legacy
  Survey with supervised learning
  In the era of large astronomical surveys, photometric classification of
supernovae (SNe) has become an important research field due to limited
spectroscopic resources for candidate follow-up and classification. In this
work, we present a method to photometrically classify type Ia supernovae based
on machine learning with redshifts that are derived from the SN light-curves.
This method is implemented on real data from the SNLS deferred pipeline, a
purely photometric pipeline that identifies SNe Ia at high-redshifts
($0.2<z<1.1$).
  Our method consists of two stages: feature extraction (obtaining the SN
redshift from photometry and estimating light-curve shape parameters) and
machine learning classification. We study the performance of different
algorithms such as Random Forest and Boosted Decision Trees. We evaluate the
performance using SN simulations and real data from the first 3 years of the
Supernova Legacy Survey (SNLS), which contains large spectroscopically and
photometrically classified type Ia samples. Using the Area Under the Curve
(AUC) metric, where perfect classification is given by 1, we find that our
best-performing classifier (Extreme Gradient Boosting Decision Tree) has an AUC
of $0.98$.
  We show that it is possible to obtain a large photometrically selected type
Ia SN sample with an estimated contamination of less than $5\%$. When applied
to data from the first three years of SNLS, we obtain 529 events. We
investigate the differences between classifying simulated SNe, and real SN
survey data. In particular, we find that applying a thorough set of selection
cuts to the SN sample is essential for good classification. This work
demonstrates for the first time the feasibility of machine learning
classification in a high-$z$ SN survey with application to real SN data.
