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.