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Manifold Age Determination for Young Stars

Project description

Manifold Age Determination for Young Stars (MADYS)

Information

This repository includes the first version of MADYS: the Manifold Age Determination for Young Stars, a flexible Python tool for age and mass determination of young stellar and substellar objects.

MADYS automatically retrieves and cross-matches photometry from several catalogs, estimates interstellar extinction, and derives age and mass estimates for individual objects through isochronal fitting.

Harmonising the heterogeneity of publicly-available isochrone grids, the tool allows to choose amongst several models, many of which with customisable astrophysical parameters

Note that, since this is an alpha version, it only includes a sub-set of isochrones. The full set, with 16 models and more than 100 isochrone grids, will be included the first full release, expected for July 2022.

Several dedicated plotting function are provided to allow a visual perception of the numerical output.

Requirements

This package relies on usual packages for data science and astronomy: numpy, scipy, pandas, matplotlib, astropy and h5py.

In addition, it also requires astroquery, tabulate and TAP Gaia Query. The last package might require the installation of lxml.

Installation:

The current repository can be installed using pip:

pip install madys

Note that, when using for the first time an extinction map, MADYS will download the relative file (0.7 GB or 2.2 GB, depending on the map).

Examples

The package is fully documented and a detailed description is provided in Squicciarini & Bonavita 2022.

However, we recommend you check out the examples provided for a better understanding of its usage.

If you find a bug or want to suggest improvements, please create a ticket

Recent papers using MADYS:

MADYS has already been used, in its various preliminary forms, for several publications, including:

Author and contributors

Vito Squicciarini, University of Padova, IT

Mariangela Bonavita, The Open University, UK

We are grateful for your effort, and hope that these tools will contribute to your scientific work and discoveries. Please feel free to report any bug or possible improvement to the author(s).

Attribution

Please cite Squicciarini & Bonavita 2022 whenever you publish results obtained with MADYS.

Project details


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