A simple and robust method for automated photometric classification of
  supernovae using neural networks
  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 SN
lightcurve 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 the
fit and Bayesian evidence for the model, are used as the input feature vector
to a classification neural network (NN) that outputs the probability that the
SN under consideration is of Type-Ia. The method is trained and tested using
data released following the SuperNova Photometric Classification Challenge
(SNPCC). We consider several random divisions of the data into training and
testing sets: for instance, for our sample D_1 (D_4), a total of 10% (40%) of
the data are involved in training the algorithm and the remainder used for
blind testing of the resulting classifier; we make no selection cuts. Assigning
a canonical threshold probability of p_th=0.5 on the NN output to classify a SN
as 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 the
SN host-galaxy redshift and its uncertainty as additional inputs to the NN
results in a modest 5-10% increase in these values. We find that the
classification quality does not vary significantly with SN redshift. Moreover,
our probabilistic classification method allows one to calculate the expected
completeness, purity or other measures of classification quality as a function
of 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 be
extended to divide non-Ia SNe into their different classes.
