Spatial Statistics in Star Forming Regions: Testing the Limits of Randomness

Abstract

Observational studies of star formation reveal spatial distributions of Young Stellar Objects (YSOs) that are `snapshots' of an ongoing star formation process. Using methods from spatial statistics it is possible to test the likelihood that a given distribution process could produce the observed patterns of YSOs. The aim of this paper is to determine the usefulness of the spatial statistics tests Diggle's G function (G), the `free-space' function (F), Ripley's K and O-ring for application to astrophysical data. The spatial statistics tests were applied to simulated data containing 2D Gaussian clusters projected on random distributions of stars. The number of stars within the Gaussian cluster and number of background stars were varied to determine the tests' ability to reject complete spatial randomness (CSR) with changing signal-to-noise. The best performing test was O-ring optimised with overlapping logarithmic bins, closely followed by Ripley's K. The O-ring test is equivalent to the 2-point correlation function. Both F and G (and the minimum spanning tree, of which G is a subset) performed significantly less well, requiring a cluster with a factor of two higher signal-to-noise in order to reject CSR consistently. We demonstrate the tests on example astrophysical datasets drawn from the Spitzer catalogue.

arXiv comment15 pages, 14 figures, 2 tables, accepted for publication in MNRAS

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Cited by in corpus (4)

• S2D2: Small-scale Significant substructure DBSCAN Detection I. NESTs detection in 2D star-forming regions
• Interplay between Young Stars and Molecular Clouds in the Ophiuchus Star-forming Complex
• Spatial Statistics in Star Forming Regions: Is Star Formation Driven By Column Density Alone?
• Observational Bias and Young Massive Cluster Characterisation I. 2D Perspective Effects