The BANYAN Σ tool

by Jonathan Gagné

BANYAN Σ: Bayesian Analysis for Nearby Young AssociatioNs Σ

Membership probability without photometric information

(Version 1.0)

BANYAN Σ is a Bayesian analysis tool to determine the membership probability of stars or other astrophysical objects to nearby young associations. This algorithm is based on a comparison of Galactic position (XYZ) and space velocity (UVW), using a Bayesian classifier. The tool currently includes all 27 known and well-characterized young associations within 150 pc. When radial velocity and/or distance measurements are not available, Bayesian inference has the advantage of being able to marginalize over them and still compute membership probabilities. When this is the case, BANYAN Σ produces statistical predictions for these quantities, assuming membership to each young association.

The information entered on this web page is not stored on our server.

The details of this method are described in Gagné et al. (2018a; ApJ, 856, 23).

Version history.

v1.0 30/01/2018
v1.1 02/02/2018 Fixed issues with the name resolver and now uses Simbad to collect data.
v1.2 06/11/2018 Added 2 new groups: Volans-Carina (VCA) and Argus (ARG).
Please cite Zuckerman et al. 2018 if you use Argus (; and cite Gagné et al. (2018f; ApJ, 865, 136) if you use Volans-Carina

The results of this tool should be interpreted with caution.

1) A high membership probability in a young association does not guarantee that the star is a true member, or young. Instead it should be regarded as a star that deserves further follow-up: before it is considered as a bona fide member, it must be demonstrated that its full 6D kinematics match that of the young association, and that it displays independent signs of youth.

2) The Bayesian probabilities reported by BANYAN Σ are designed to generate recovery rates of : (1) 50%, (2) 68%, (3) 82%, and (4) 90% respectively when (1) only proper motion is used, (2) proper motion and radial velocity are used, (3) proper motion and distance are used, and (4) proper motion, radial velocity and distance are used.


  • You can enter the name of a star, as resolved by Simbad or Vizier.

  • Press the RESOLVE button. BANYAN Σ will seek all the information it can find from a limited number of online catalogs. Missing information will be reported as « NaN ». Be patient, this may take several tens of seconds.

  • You can modify and/or remove some information as you please but you need minimally a sky position, proper motion and a measurement error on proper motion to proceed.

  • Proper motions are in units of mas/yr, and the proper motion in the RA direction is implicitly understood as the usual mu_ra * cos(delta), where delta is the declination of the star.

  • Press SUBMIT and read the cautionary note above (if not already done) before interpreting the results.

  • Please be reminded that this tool does not use photometry as input observables. In order to use color-magnitude diagram sequences you must download the full Python or IDL code on GitHub (URL PENDING).

  • Acknowledgement : if your paper uses results obtained with BANYAN Σ, please cite Gagné et al. (2018a).

  • If you have any questions and/or comments, contact me at jgagne (@) carnegiescience (dot) edu.

  • NAME of your star: PRESS:

    Right ascension (degrees) :

    Proper motion in right ascension (mas/yr) :

    Proper motion in declination (mas/yr) :

    Radial velocity (km/s) :

    Parallax (mas) :

    STEP 2 :

    Declination (degrees) :

    Error on Proper motion in right ascension (mas/yr) :

    Error on Proper motion in declination (mas/yr) :

    Error on radial velocity (km/s) :

    Error on parallax (mas) :

    Name of your star: