PyStarshade

Overview

Developed by Jamila Taaki (MIDAS fellow).

PyStarshade is a Python library for Starshade (or any external occulter) simulations from star-planet system to CCD with Fresnel diffraction methods. This library efficiently calculates output fields using Bluestein FFTs.

What is a starshade? A starshade is a particular apodization (mask), which flown at Fresnel distances from a telescope achieves star-light suppression for imaging exoplanets in orbit around the star.

When a starshade is aligned with a star, starlight is diffracted by the starshade $s(\mathbf{x})$ onto a telescope aperture. For a starshade mask $s(u, v)$ which is zero inside the mask and unity outside, at a wavelength $\lambda$ and starshade-telescope distance $z$, the field at the telescope aperture is $f_{\lambda}(x, y)$ is related to the Fourier transform of the starshade mask: $$f_{\lambda}[u, v] \propto \mathcal{F} \left( s(u, v) e^{\frac{j \pi}{\lambda z} (u^2 + v^2)} \right) \left[ \frac{x}{\lambda z} ,\frac{y}{\lambda z} \right]$$ Numerical diffraction calculations must use a very small numerical resolution $d u$ of the starshade $s(u, v)$ in order to accurately calculate starlight suppression. Using a standard FFT to perform these calculations is inefficient as very large zero-padding factors are needed to sample the field at the telescope aperture. The Bluestein FFT is a technique to calculate arbitrary spectral samples of a propagated field, indirectly using FFTs and therefore benefiting from their efficiency. For an $N \cdot N$ starshade mask, and an $M \cdot M$ telescope aperture, the Bluestein FFT approach achieves a complexity of $O((N+M)^2 \log (M+N))$. This technique is utilized in multiple aspects of the optical train to efficiently propagate fields.

Example

Simulated imaging of a synthetic exoscene (ExoVista) with three visible exoplanets at a wavelength of 500 nm. A 60 m starshade configuration and a 6m segmented pupil was used for this example.

Installation

You can install PyStarshade using pip:

pip install pystarshade

To use pre-computed data, use git lfs:

$ git clone https://github.com/xiaziyna/PyStarshade.git PyStarshade
$ cd PyStarshade
$ git lfs pull

Dependencies

Scipy, Numpy,

Optional: HCIPy, astropy

Quickstart

Detailed documentation for all PyStarshade utilities.

Input data

PyStarshade can take as input any pixelized source-field such as Haystacks model, or analytic descriptions of sources (so far a point source and Gaussian source). If you wish to perform propagation using analytic descriptions, please use 'pystarshade.simulate_field.point_source_to_ccd'.

The easiest way to interface with PyStarshade is via the StarshadeProp class. Generate fields/psf models for a chosen design reference mission (drm). Simulate imaging for a 'source_field' with a default 2 mas sampling.

    from pystarshade.propagator import StarshadeProp

    drm = 'hwo'
    hwo_starshade = StarshadeProp(drm = drm)
    hwo_starshade.gen_pupil_field()

    pupil_type = 'hex'
    hwo_starshade.gen_psf_basis(pupil_type = pupil_type)
    focal_intensity = hwo_starshade.gen_scene(pupil_type, source_field.astype(np.float32), 500e-9)

Worked examples

See examples folder for different simulation examples.

License

[PyStarshade] is released under the GNU General Public License v3.0.