exoring

Exoring transit simulation using numerical integration.

installation

Simply clone the repository, navigate to it, then run pip install.

git clone [email protected]:leigh2/exoring.git
cd exoring
pip install .

usage examples

generate the opacity image of a ringed exoplanet

from exoring import build_exoring_image
import matplotlib.pyplot as plt

image, x_grid, y_grid, area = build_exoring_image(
    200, 1.5, 1.9, 0.2, 0.35, full_output=True
)

plt.imshow(image)
plt.show()

This will produce and show the opacity image of a planet with a ring of opacity 0.2, inner and outer radii of 1.5 and 1.9 planetary radii, and with a tilt of 0.35 radians relative to the line of sight to the observer. The code will generate the image at a resolution of 200 pixels per planet radius, and will return the full 2d grid of opacity values and x,y coordinates.

generate a transit light curve of the ringed exoplanet

Extending the above example, we can generate the transit light curve with:

from exoring import occult_star
import numpy as np

x_offsets = np.linspace(-2, 2, 1000)
light_curve = occult_star(
    image, x_grid, y_grid, area,
    0.03, x_offsets, 0.3, 0.2,
    (0.395, 0.295)
)

plt.plot(x_offsets, light_curve)
plt.show()

This will simulate and show the transit of the above ringed exoplanet in front of a star. The image is scaled such that the planet has 3% of the stellar radius. The planet transits the star with a minimum separation of 0.3 stellar radii, in the other dimension it passes with values between -2 and 2 stellar radii. The tilt of the planet with respect to it's orbital axis (direction of motion) is 0.2 radians. Quadratic limb darkening parameters are (0.395, 0.295), which are roughly appropriate for the Sun in the Kepler K band (according to https://exoctk.stsci.edu/limb_darkening).

notes

• Unless you want a pretty silhouette picture of a ringed exoplanet there is no need to run build_exoring_image() with full_output=True, passing the full 2d image and grid to occult_star results in unnecessary computational expense.
• The code is compiled jit by numba, meaning the first run of each of the above methods is relatively slow but subsequent executions are significantly faster.

Acknowledgements

LCS acknowledges support from PLATO grant UKSA ST/R004838/1