Emille E. O. Ishida, Rafael S. de Souza
The problem of photometric identification will be extremely important for
large surveys in the next decade. In this work, we propose the use of KPCA
combined with k = 1 nearest neighbor algorithm (KPCA+1NN) as a framework for
SNe photometric classification. The method does not rely on information about
redshift or local enviromental variables, so it is less sensitive to bias than
its template fitting counterparts. We applied the method to $\approx$ 20000 SNe
light curve released after the \textit{Supernova Photometric Classification
Challenge} (SNPCC). Results for the photometric sample achieved up to 89%
efficiency (eff), 97% purity (pur), 96% successful classification (SC) rates
and figure of merit (FoM) of 0.79 (SNR$\geq$5). If we impose no SNR cuts, we
obtain up to 64% eff, 43% pur, 46% SC and FoM of 0.10. We also present the
classification results using only pre-maximum epoches, obtaining 80% eff, 73%
pur, 84% SC and FoM of 0.32 (SNR$\geq$5). Comparing the performance of our
classifier with MLCS2k2 fit probability, we demonstrate that KPCA+1NN is able
to improve the classification results of MLCS2k up to $\approx 15%$ without the
need of redshift information. Results are sensitive to the information
contained in each light curve, as a consequence, higher quality the data points
lead to higher successfull classification rates. The method is flexible enough
to be applied to other astrophysical transients, as long as a training and a
template sample are provided.
View original:
http://arxiv.org/abs/1201.6676
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