Tutorials

Tutorials#

This section contains a set of tutorials designed to guide the user through different types of analyses, e.g.: analyzing the structure of the cluster, estimating the cluster’s membership probabilities, generating synthetic clusters, and estimating a cluster’s fundamental parameters.

The Structure analysis notebook serves as a guide to the initial structural characterization of a stellar cluster field. It covers loading data, visualizing distributions, and applying built-in methods to estimate the cluster center and the number of likely members, while also pointing out parameters like the radius that currently require manual input or external estimation.

The Membership probabilities notebook serves as a practical guide to applying the asteca.Membership.fastmp() and asteca.Membership.bayesian() membership probability estimation methods. It highlights the different data requirements (5D for fastmp, photometric for bayesian) and necessary preliminary parameter estimations (center, N_cluster, radius for bayesian) for each method, concluding with a visual comparison of their results.

The Generate synthetic clusters notebook demonstrates the process of generating synthetic star clusters via the asteca.Synthetic.generate() method using specified fundamental parameters. Optionally, the generator can be calibrated with observed cluster data using the asteca.Synthetic.calibrate() method to enhance realism in terms of star count, magnitude limits, and errors. The process culminates in visualizing the resulting synthetic Color-Magnitude Diagrams (CMDs), often distinguishing stellar systems and potentially overlaying the underlying isochrone.

The Parameters fitting notebook provides a step-by-step guide to estimating the fundamental parameters of a stellar cluster using ASteCA and the pyABC library. The tutorial shows how to set up the fitting process, run it, and interpret the results, including the final parameter estimates (medians and standard deviations) along with pyABC visualizations and a comparison Color-Magnitude Diagram (CMD).

Given an observed cluster the Masses and binary fraction notebook shows how to apply the following methods: