madys 0.3.2

Manifold Age Determination for Young Stars

Manifold Age Determination for Young Stars (MADYS)

Description

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. Particular attention has been dedicated to the categorization of these models, labeled through a four-level taxonomical classification.

Note that, since this is an alpha version, it only includes a sub-set of isochrones. The full set, with 17 models and more than 100 isochrone grids, will be included the first full release, expected for July 2022.

Despite our efforts, the model list is far from being complete. If you wish a new model to be included in a new version of MADYS, feel free to get in contact with us.

A thorough description of photometric filters featured in MADYS is provided; finally, several dedicated plotting functions are included to allow a visual perception of the numerical output.

Latest news:

Jun 20, 2022 - BEX models (Linder et al. 2019) added to the list of available models.

Jun 17, 2022 - Gaia DR3 now available! The new catalog replaces, for all intents and purposes, Gaia EDR3.

Installation:

Catalog queries are mediated by the TAP Gaia Query package (tap). If you import madys from the command line, the module is automatically installed if not found. However, this does not work from Jupyter Notebook. We suggest to manually install the package from pip, through:

pip install git+https://github.com/mfouesneau/tap

Please make sure you use the command above, as just using pip install tap will download a different, although eponymous, package.

Note that TAP Gaia Query might require the installation of lxml (v4.6.3).

Once TAP Gaia Query is installed, the current MADYS repository can be installed using pip:

pip install madys

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

Requirements

This package relies on usual packages for data science and astronomy: numpy (v1.18.1), scipy (v1.6.1), pandas (v1.1.4), matplotlib (v3.3.4), astropy (v4.3.1) and h5py (v3.2.1).

In addition, it also requires astroquery (v0.4.2.dev0) and tabulate (v0.8.9)

It also requires TAP Gaia Query (v0.1). The last package might require the installation of lxml (v4.6.3).

Examples

The package is fully documented and a detailed description of its features, together with several examples of the kind of scientific results that can be obtained with it, is provided in Squicciarini & Bonavita 2022 arXiv:2206.02446.

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:

• A scaled-up planetary system around a supernova progenitor, Squicciarini et al. 2022, arXiv220502279S
• Results from The COPAINS Pilot Survey: four new brown dwarfs and a high companion detection rate for accelerating stars, Bonavita et al. 2022, MNRAS.513.5588B
• A wide-orbit giant planet in the high-mass b Centauri binary system, Janson et al. 2021, Natur.600..231J
• Unveiling the star formation history of the Upper Scorpius association through its kinematics, Squicciarini et al. 2021, MNRAS.507.1381S
• New binaries from the SHINE survey, Bonavita et al. 2021, arXiv210313706B
• BEAST begins: sample characteristics and survey performance of the B-star Exoplanet Abundance Study,Janson, Squicciarini et al. 2021, A&A...646A.164J

Authors

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 authors.

Attribution

Please cite Squicciarini & Bonavita 2022 arXiv:2206.02446 whenever you publish results obtained with MADYS.