Identification of Spectro-dynamical Asteroid Families using a Shared Nearest Neighbor Clustering Algorithm

Details Identification of Spectro-dynamical Asteroid Families using a Shared Nearest Neighbor Clustering Algorithm Identification of Spectro-dynamical Asteroid Families using a Shared Nearest Neighbor Clustering Algorithm

Sep 2006

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2006dps....38.5910b&link_type=abstract

American Astronomical Society, DPS meeting #38, #59.10; Bulletin of the American Astronomical Society, Vol. 38, p.594

Astronomy and Astrophysics

Astronomy

Scientific paper

Dynamical asteroid families are defined as groupings of objects in proper orbital element space that are believed to result from the collisional disruptions of once larger parent bodies. Spectroscopic studies have been used to test the genetic relationships that should exist between family members, and have revealed a high degree of spectral homogeneity within each family. Given this finding, a method of searching for asteroid families was proposed by Bus (1999, MIT Ph.D. thesis) that combines both orbital and spectral parameters in the clustering process. Here I describe an improved search for spectro-dynamical asteroid families that utilizes a shared nearest neighbor (SNN) clustering algorithm. This algorithm is more robust for family identification than the Hierarchical Clustering Method (HCM) used in earlier stages of this study. Orbital elements are combined with spectral data from three major surveys, the first and second phases of the Small Main-belt Asteroid Spectroscopic Survey (SMASS, Xu et al. 1995, Icarus 115:1-16, Bus and Binzel 2002, Icarus 158:106-145) and the Small Solar System Objects Spectroscopic Survey (S3OS2, Lazzaro et al. 2004, Icarus 172:179-220), resulting in an analysis of 2074 asteroids and a search for families across the entire main belt. This new search for asteroid families has many advantages over previous studies that have relied solely on the distributions of orbital elements. By including spectral parameters in the clustering analysis, boundaries of known families can be more accurately determined, close or overlapping families in orbital space can be separated, non-family members (interlopers) can be identified, and older, more diffuse families can be more easily recognized. Family memberships will be presented, along with details of the clustering procedure.

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