Membership module

Membership module#

The asteca.Membership class allows estimating the membership probabilities for all the stars in a given observed field. There are currently two methods included in this class: asteca.Membership.bayesian() and asteca.Membership.fastmp().

The bayesian() method was described in detail in the article where ASteCA was originally introduced. The method requires (ra, dec) data and will use any extra data dimensions stored in the Cluster object, i.e.: photometry, proper motions, and parallax. A minimum of two data dimensions are required, in addition to (ra, dec). This method can produce membership probabilities on photometric data alone.

The fastmp() method was described in detail in the article where the Unified Cluster Catalogue (UCC) was introduced. The method requires proper motions, and parallax data dimensions stored in the Cluster object. Photometric data is not employed.

Important

The only advantage of the bayesian() method over the fastmp() method is that the former works with photometric data. Hence it should only be used in cases were only photometric data is available, as fastmp() is not only much faster but also more precise in those cases where proper motions and/or parallax data is available.

To use these methods we need to estimate the cluster’s number of members as described in the Number of members section, which is done by calling the asteca.Cluster.get_nmembers() method.

With the N_cluster attribute in place in a Cluster object, here called my_field, you can define a Membership object, here called memb:

# Define a `membership` object
memb = asteca.Membership(my_field)

and apply either the bayesian() or the fastmp() method:

# Run `fastmp` method
probs_fastmp = memb.fastmp()

# Run `bayesian` method
probs_bayes = memb.bayesian()

The arrays stored in the probs_fastmp or probs_bayes variables are the per-star membership probabilities. The results will naturally not be equivalent as both algorithms are rather different. The bayesian() algorithm for example tends to assign lower probabilities than the fastmp() algorithm.

A step-by-step example is shown in the Membership probabilities tutorial.