2020
DOI: 10.1093/mnras/stz3610
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Clusterix 2.0: a virtual observatory tool to estimate cluster membership probability

Abstract: Clusterix 2.0 is a web-based, Virtual Observatory-compliant, interactive tool for the determination of membership probabilities in stellar clusters based on proper motion data using a fully non-parametric method. In the area occupied by the cluster, the frequency function is made up of two contributions: cluster and field stars. The tool performs an empirical determination of the frequency functions from the Vector-Point Diagram without relying in any previous assumption about their profiles. Clusterix 2.0 all… Show more

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Cited by 20 publications
(8 citation statements)
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“…These parameters have then to be compared with the mean cluster ones and a color-magnitude diagram constructed. For this purpose, several, mostly independent, methods have been developed already in the pre-Gaia era and updated since then (for example, von Hippel et al, 2006;Krone-Martins and Moitinho, 2014;Perren et al, 2015;Balaguer-Núñez et al, 2020).…”
Section: Gaia Era-revisiting Open Clustersmentioning
confidence: 99%
“…These parameters have then to be compared with the mean cluster ones and a color-magnitude diagram constructed. For this purpose, several, mostly independent, methods have been developed already in the pre-Gaia era and updated since then (for example, von Hippel et al, 2006;Krone-Martins and Moitinho, 2014;Perren et al, 2015;Balaguer-Núñez et al, 2020).…”
Section: Gaia Era-revisiting Open Clustersmentioning
confidence: 99%
“…For this we used the Clusterix 2.0 (http://clusterix.cab.inta-csic.es/clusterix/) program. Clusterix is a web-based, interactive application that allows the computation of membership probabilities from proper motions through a fully non-parametric method and also allows the possibility of gathering physical parameters -parallaxes, radial velocities and so on (Balaguer-Nunez et al 2017).…”
Section: Possible Connection With An Open Clustermentioning
confidence: 99%
“…For example, Cantat-Gaudin et al (2018) used a modified version of UPMASK (Krone-Martins & Moitinho 2014) to identify members in 1229 clusters and Kounkel & Covey (2019) used HDB-SCAN to identify 1900 clusters and comoving groups within 1 kpc. Other popular algorithms include Clusterix (Balaguer-Núñez et al 2020) and ASteCA (Perren et al 2015). While the former is a fully nonparametric method that determines cluster membership probabilities based on proper motions, the latter is a fully automated software that obtains cluster parameters like center coordinates and radius, together with luminosity functions and membership probabilities.…”
Section: Introductionmentioning
confidence: 99%