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We present the results of our search for a dynamical family around the active asteroid P/2012F5 (Gibbs). By applying the hierarchical clustering method, we discover an extremely compact 9-body cluster associated with P/2012F5. The statistical signifi cance of this newly discovered Gibbs cluster is estimated to be >99.9%, strongly suggesting that its members share a common origin. The cluster is located in a dynamically cold region of the outer main-belt at a proper semi-major axis of about 3.005 AU, and all members are found to be dynamically stable over very long time-scales. Backward numerical orbital integrations show that the age of the cluster is only 1.5 $pm$ 0.1 Myr. Taxonomic classifications are unavailable for most of the cluster members, but SDSS spectrophotometry available for two cluster members indicate that both appear to be $Q$-type objects. We also estimate a lower limit of the size of the parent body to be about 10 km, and find that the impact event which produced the Gibbs cluster is intermediate between a cratering and a catastrophic collision. In addition, we search for new main-belt comets in the region of the Gibbs cluster by observing seven asteroids either belonging to the cluster, or being very close in the space of orbital proper elements. However, we do not detect any convincing evidence of the presence of a tail or coma in any our targets. Finally, we obtain optical images of P/2012F5, and find absolute R-band and V-band magnitudes of $H_R$ = 17.0 $pm$ 0.1 mag and $H_V$ = 17.4 $pm$ 0.1 mag, respectively, corresponding to an upper limit on the diameter of the P/2012F5 nucleus of about 2 km.
We present a new classification of families identified among the population of high-inclination asteroids. We computed synthetic proper elements for a sample of 18,560 numbered and multi-opposition objects having sine of proper inclination greater th an 0.295. We considered three zones at different heliocentric distances (inner, intermediate and outer region) and used the standard approach based on the Hierarchical Clustering Method (HCM) to identify families in each zone. In doing so, we used slightly different approach with respect to previously published methodologies, to achieve a more reliable and robust classification. We also used available SDSS color data to improve membership and identify likely family interlopers. We found a total of 38 families, as well as a significant number of clumps and clusters deserving further investigation.
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