An X-Ray Spectral Classification Algorithm with Application to Young Stellar Clusters

A large volume of low signal-to-noise, multidimensional data is available from the CCD imaging spectrometers aboard the Chandra X-Ray Observatory and the X-Ray Multimirror Mission (XMM-Newton). To make progress analyzing this data,itisessentialtodevelopmethods tosort,classify,and characterize thevastlibrary of X-rayspectrain 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.Weuseprovenmultivariatestatisticaltechniquesincludingprincipalcomponentanalysisandanensembleclassifier consistingofagglomerativehierarchicalclusteringandK-meansclusteringappliedforthefirsttimeforspectralclassification.Thealgorithmpositionsthesourcesinamultidimensionalspectralsequenceandthengroupstheorderedsources into clusters based on their spectra. These clusters appear more distinct for sources with harder observed spectra. The apparent diversity ofsource spectra isreduced toa three-dimensional locus inprincipal component space,withspectral outliers falling outside this locus. The algorithm was applied to a sample of 444 strong sources selected from the 1616 X-ray emitting sources detected in deep Chandra imaging spectroscopy of the Orion Nebula Cluster. Classes form sequencesinNH,AV,andaccretionactivityindicators,demonstratingthatthealgorithmefficientlysortstheX-raysources into a physically meaningful sequence. The algorithm also isolates important classes of very deeply embedded, active young stellar objects, and yields trends between X-ray spectral parameters and stellar parameters for the lowest mass, pre‐main-sequence stars.

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