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A method is presented for automated photometric classification of supernovae(SNe) as Type-Ia or non-Ia. A two-step approach is adopted in which: (i) the SNlightcurve flux measurements in each observing filter are fitted separately;and (ii) the fitted function parameters and their associated uncertainties,along with the number of flux measurements, the maximum-likelihood value of thefit and Bayesian evidence for the model, are used as the input feature vectorto a classification neural network (NN) that outputs the probability that theSN under consideration is of Type-Ia. The method is trained and tested usingdata released following the SuperNova Photometric Classification Challenge(SNPCC). We consider several random divisions of the data into training andtesting sets: for instance, for our sample D_1 (D_4), a total of 10% (40%) ofthe data are involved in training the algorithm and the remainder used forblind testing of the resulting classifier; we make no selection cuts. Assigninga canonical threshold probability of p_th=0.5 on the NN output to classify a SNas Type-Ia, for the sample D_1 (D_4) we obtain a completeness of 0.78 (0.82),purity of 0.77 (0.82), and SNPCC figure-of-merit of 0.41 (0.50). Including theSN host-galaxy redshift and its uncertainty as additional inputs to the NNresults in a modest 5-10% increase in these values. We find that theclassification quality does not vary significantly with SN redshift. Moreover,our probabilistic classification method allows one to calculate the expectedcompleteness, purity or other measures of classification quality as a functionof the p_th, without knowing the true classes of the SNe in the testing sample.The method may thus be improved further by optimising p_th and can easily beextended to divide non-Ia SNe into their different classes.

A simple and robust method for automated photometric classification of supernovae using neural networks