FLOSSY

Fit Linear periOd-Spacing patternS interactivelY

(Acronym created before the advent of LLMs)

Physical context

Period-spacing patterns are an asteroseismic tool that probes the near-core regions of a star. Tassoul 1980 predicts the period of gravity modes to be equidistant (i.e., constant ΔP) for a spherical chemically-homogeneous non-rotating star. Keppler and TESS observations have revealed a variety of non-constant patterns in plots of ΔP vs P whose signatures have been associated with the physic inside the star. For instance, Miglio et al. 2008 linked the dips in such patterns to gradients in the star's chemical composition and Van Reeth et al. 2015abc established the relation between the pattern's slope and the interior rotation. The community is currently working on mathematical descriptions (e.g. Townsend 2003, Cunha et al. 2019) and, at the same time, using these patterns as constraints on forward asteroseismic modelling to probe the physics near the stellar core (e.g. Pedersen et al. 2021).

Period-spacing patterns are ultimately identified manually, especially when they are used for forward asteroseismic modelling (Aerts et al. 2018). FLOSSY aims to facilitate this task by offering an interactive tool that lets the user search the ΔP pattern in a previously computed periodogram while offering diagnostic plots as the ΔP vs P and the period Echelle diagram, both often used to spot and confirm the pattern. FLOSSY assumes that the ΔP changes at a linear rate with respect to P and uses the same parametrization as Li et al. 2019.

Requirements

You can cover all requirements by installing the following virtual environment using Conda and the YML file in the repo.

conda env create -f flossy_environment.yml

You can now activate the just created Conda environment using

conda activate flossy-env

If you wish so, you can later remove it with:

conda env remove --name flossy-env

⮕ Alternatively, you can use pip to directly install the package along with all its dependences. For it, go to the directory containing the setup.py file and run:

pip install .

If you wish so, you can later uninstall it with:

pip uninstall flossy

Importing FLOSSY

If you used pip to install the package, then flossy should be ready to import like any other Python module.

If you just downloaded or cloned the repository, then you need to add its location to the environment variable used by Python to search for modules, so that you can have access to the flossy module regardless your location on your machine.

Run example

The minimum sets of data to run FLOSSY are:

1. prewhitened periods (e.g., pw_periods)
2. prewhitened period errors (e.g., pw_e_periods)
3. prewhitened amplitudes (e.g., pw_amplitudes)
4. periodogram periods (e.g., pg_periods)
5. periodogram amplitudes (e.g., pg_amplitudes)

They all must be of type astropy.units.quantity.Quantity. They are then fed to the GUI:

from astropy import units as u
from flossy import flossyGUI

GUI = flossyGUI(
    pw_periods=pw_periods,
    pw_e_periods=pw_e_periods,
    pw_amplitudes=pw_amplitudes,
    pg_periods=pg_periods,
    pg_amplitudes=pg_amplitudes,
    ID='TIC 374944608',
    freq_resolution=1/u.yr
)

# Run the interface using a context manager
with GUI as flossy:
    app = QApplication([]) # Main windows to show dialogs, etc
    plt.show() # Display the GUI
    sys.exit(app.exec_()) # Close the app

To try an example, go the repo's root folder and run:

python flossy/example.py

You should be transported to a live example like the one shown in the GIF below. Have fun!

Fit example

Found an issue in the code?

Create an issue on GitHub and include a minimal reproducible example of it.

Reference paper

https://doi.org/10.1051/0004-6361/202141926