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In the era of large astronomical surveys, photometric classification ofsupernovae (SNe) has become an important research field due to limitedspectroscopic resources for candidate follow-up and classification. In thiswork, we present a method to photometrically classify type Ia supernovae basedon machine learning with redshifts that are derived from the SN light-curves.This method is implemented on real data from the SNLS deferred pipeline, apurely photometric pipeline that identifies SNe Ia at high-redshifts($0.2<z<1.1$).  Our method consists of two stages: feature extraction (obtaining the SNredshift from photometry and estimating light-curve shape parameters) andmachine learning classification. We study the performance of differentalgorithms such as Random Forest and Boosted Decision Trees. We evaluate theperformance using SN simulations and real data from the first 3 years of theSupernova Legacy Survey (SNLS), which contains large spectroscopically andphotometrically classified type Ia samples. Using the Area Under the Curve(AUC) metric, where perfect classification is given by 1, we find that ourbest-performing classifier (Extreme Gradient Boosting Decision Tree) has an AUCof $0.98$.  We show that it is possible to obtain a large photometrically selected typeIa SN sample with an estimated contamination of less than $5\%$. When appliedto data from the first three years of SNLS, we obtain 529 events. Weinvestigate the differences between classifying simulated SNe, and real SNsurvey data. In particular, we find that applying a thorough set of selectioncuts to the SN sample is essential for good classification. This workdemonstrates for the first time the feasibility of machine learningclassification in a high-$z$ SN survey with application to real SN data.

Photometric classification of type Ia supernovae in the SuperNova Legacy Survey with supervised learning