1. Automated Classification of X-ray Sources for Very Large Datasets Susan Hojnacki, Joel Kastner, Steven LaLonde Rochester Institute of Technology Giusi Micela INAF, Osservatorio Astronomico di Palermo April 2005

2. 2 Background Pervasive Problem: Like many current space science missions, the Chandra X-ray Observatory is generating data at a rate much faster than can be analyzed with current tools Enormous amount of data from Chandra: 6 CCD arrays x 1024 x 1024 = 6,291,456 pixels ~3.2 s exposure time  9000 frames generated in 8 hours of observing!

3. 3 Background X-ray images help astronomers study new star formation and galactic evolution X-ray sources are classified by visual inspection of individual spectra and a model-fitting approach; typically one source at a time Good for studying physics of bright, individual sources, but time consuming for analysis of rich stellar clusters Model-fitting approach difficult to use with faint sources or “new” sources that don’t fit any existing models

4. 4 Our existing semi-automated technique groups X-ray sources based on X-ray spectral attributes Uses a combination of techniques from the fields of multivariate statistics, remote sensing, and pattern recognition Objective, model-independent approach that requires no a priori assumption as to nature of X-ray source Allows for automated exploration to find “interesting” objects, or clusters of objects, for further study Background

5. 5 Algorithm Input Data High energy X-ray spectrum divided into 42 spectral bands Photon counts within the 42 spectral bands are used as the multivariate input variables Input data is from the Chandra X-ray Observatory, but can be from any X-ray observatory

6. 6 Chandra Deep Field Image Orion Nebula Cluster Energy Spectrum X-ray Light curve (Image: Garmire et al. 2000)

7. 7 Algorithm Details Multivariate techniques used:  Principal Component Analysis  Agglomerative Hierarchical Clustering  K-means Clustering All are “unsupervised” methods None require multivariate normal data Choice of number of resulting classes is heuristic

8. 8 Example Application Is X-ray emission from young stars derived from coronal activity, accretion, outflow activity, or some combination of these mechanisms ? The answer to this question will have an impact on studies of a wide variety of astrophysical phenomena that produce X-ray emission.

9. 9 Chandra Orion Ultradeep Project (COUP) COUP dataset compiled from ~850 ks Chandra observation of the Orion Nebula Cluster Represents most sensitive and comprehensive description of X-ray emission from a young star cluster (Getman et al. 2005) ~1616 X-ray sources detected, some of which can not be “seen” in visible or near-infrared wavelengths Spectral classification technique applied to sample of 444 sources selected from COUP image

10. Chandra X-ray image of the Orion Nebula Cluster (10-day integration; Feigelson et al. 2004)

11. 11 Principal Component Plot Plot of the first 2 principal components showing the source classes (4 components were retained) Progression of classes moving clockwise around the arch forms a sequence of decreasing spectral hardness Average spectra for some of the classes are shown Class numbers increase clockwise around the curve

12. 12

13. 13 Example Classes Class 2 Class 14

14. 14 Analysis of Results Class Sequences vs. Standard Measures of Xray / Visual / Near-IR Spectral Properties

15. 15 Analysis of Results For this sample of low-mass young stars: Classes form sequences in hydrogen column density, visual absorption, and near-IR K-band excess, demonstrating that the algorithm efficiently sorts young stars into physically meaningful groupsClasses form sequences in hydrogen column density, visual absorption, and near-IR K-band excess, demonstrating that the algorithm efficiently sorts young stars into physically meaningful groups Lack of correlation with effective temperature shows that stellar X-ray spectral properties are not well correlated with stellar photospheric propertiesLack of correlation with effective temperature shows that stellar X-ray spectral properties are not well correlated with stellar photospheric properties

16. 16 Knowledge Discovery Preliminary classification results reveal that our spectral clustering technique can be used to efficiently identify very young X-ray sources that: – lack optical and near-infrared counterparts – display strong Fe K  line emission – display large-amplitude, impulsive flares Within the COUP dataset, such sources likely represent the youngest protostars in the ONC

17. 17 Work To Do Phase I Classify X-ray sources in other star formation regions based on previous source groupings Compare expected vs actual results for known sources Extend algorithm for use with ‘unknown’ X-ray source datasets

18. 18 Work To Do Phase II Study X-ray source variability and add temporal inputs to the algorithm Apply new algorithm to datasets from Chandra Develop into a software tool for use by the X-ray astronomy community

19. 19 Space Science Goals Algorithm results aid in the study of physical conditions of X-ray plasmas surrounding young stars: Determine if young stars in other regions of the sky fit into previously established statistical groupings, helping to ascertain their evolutionary status Determine mechanisms underlying the bright X-ray emission that is a distinguishing feature among young stars Improve understanding of nature and timescale of accretion onto young, solar-mass stars from protoplanetary disks

20. 20 Background Material Algorithm Details

21. 21 Principal Component Analysis Goal is to identify a new, smaller set of uncorrelated variables, called the principal components, which explains all (or nearly all) of the total variance in the data set Each principal component is described by:  eigenvector: linear combination of original variables  eigenvalue: variance accounted for by that PC Number of principal components to retain is based on analysis of several stopping rules

22. 22 Agglomerative Hierarchical Clustering Attempts to find natural groupings of the detected X- ray sources Partitions set of X-ray sources into relatively homogeneous subsets based on inter-source distances Starts with 1 source in each cluster and successively merges them based on statistical distance measure Examines distance level at each merger step to determine the final number of clusters

23. 23 K-means Clustering Hierarchical clustering cannot transfer a source from one cluster to another if initially grouped incorrectly: K-means used for “fine-tuning” K-Means goal: arrive at clusters with small within- cluster variation and large between-cluster variation Start with cluster assignments from hierarchical clustering for initial partition of X-ray sources Iterative process  change X-ray source’s cluster membership if there is a cluster with a closer centroid