Improving the open cluster census

Author:

Hunt Emily L.ORCID,Reffert SabineORCID

Abstract

Context. The census of open clusters has exploded in size thanks to data from the Gaia satellite. However, it is likely that many of these reported clusters are not gravitationally bound, making the open cluster census impractical for many scientific applications. Aims. We aim to test different physically motivated methods for distinguishing between bound and unbound clusters, using them to create a cleaned star cluster catalogue. Methods. We derived completeness-corrected photometric masses for 6956 clusters from our earlier work. Then, we used these masses to compute the size of the Roche surface of these clusters (their Jacobi radius) and distinguish between bound and unbound clusters. Results. We find that only 5647 (79%) of the clusters from our previous catalogue are compatible with bound open clusters, dropping to just 11% of clusters within 250 pc. Our catalogue contains 3530 open clusters in a more strongly cut high-quality sample of objects. The moving groups in our sample show different trends in their size as a function of age and mass, suggesting that they are unbound and undergoing different dynamical processes. Our cluster mass measurements constitute the largest catalogue of Milky Way cluster masses to date, which we also use for further science. Firstly, we inferred the mass-dependent completeness limit of the open cluster census, showing that the census is complete within 1.8 kpc only for objects heavier than 230 M. Next, we derived a completeness-corrected age and mass function for our open cluster catalogue, including estimating that the Milky Way contains a total of 1.3 × 105 open clusters, only ∼4% of which are currently known. Finally, we show that most open clusters have mass functions compatible with the Kroupa initial mass function. Conclusions. We demonstrate Jacobi radii for distinguishing between bound and unbound star clusters, and publish an updated star cluster catalogue with masses and improved cluster classifications.

Funder

DFG

Publisher

EDP Sciences

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