X-ray classification of young star clusters

To make progress analyzing this data, it is essential to develop methods to sort, classify, and characterize the vast library of X-ray spectra in a nonparametric fashion (complementary to current parametric model fits). We have developed a spectral classification algorithm that handles large volumes of data and operates independently of the requirement of spectral model fits.

We use proven multivariate statistical techniques including principal component analysis and an ensemble classifier consisting of agglomerative hierarchical clustering and K-means clustering applied for the first time for spectral classification. The algorithm positions the sources in a multidimensional spectral sequence and then groups the ordered sources into clusters based on their spectra. These clusters appear more distinct for sources with harder observed spectra.