Subclassification of supernova light curves using similarity analysis

Similarity analysis is used to sub-classify supernovae light curves. This statistical method classifies time series by the shape of the light curve. A distance measure is calculated indicating the difference in form between two ordered sequences, such as are found in time series data. The mutual differences between many sequences or time series can by calculated and then compiled in a similarity matrix, similar in form to a correlation matrix. In this study, the similarity distance metric is calculated using the SIMILARITY procedure in SAS. When standard clustering methods are applied to a similarity matrix, time series with similar patterns are grouped together and placed into clusters distinct from others containing times series with different patterns. I this study, type Ia supernova in the SDSS Supernova Survey are analyzed, with two sub-types being identified. Among 1a supernovae, one group is found to have a second peak a secondary peak in the data captured by the infrared z filter and a distinctly higher peak in the u ultraviolet filter, compared to a second group with no second z filter peak and less radiation in the u filter range.