exoscene 1.1

Library for simulating direct images of exoplanetary systems.

exoscene

Installation: pip install exoscene

exoscene is a library of classes and utility functions for simulating direct images of exoplanetary systems. The package was developed by Neil Zimmerman (NASA/GSFC), with source code contributions from Maxime Rizzo and Christopher Stark. This work was funded in part by a WFIRST CGI Science Investigation Team contract (PI: Margaret Turnbull).

exoscene makes significant use of the Astropy, NumPy, SciPy, and Scikit-image packages.

A jupyter notebook providing usage examples for much of the functionality is included under the docs subdirectory: exoscene/docs/notebooks/Roman-CGI_scene_demo.ipynb

The functions are organized in 3 modules: exoscene/planet.py, exoscene/star.py, and exoscene/image.py.

1. exoscene/planet.py

• a Planet() class with a data structure for containing the basic physical parameters of a planet, its orbit, its host star, and associated methods for computing its relative astrometry ephemeris, its phase function, and flux ratio.

• A function for modeling the orbital position and the Lambert sphere phase function, based on the Keplerian orbital elements and date of observation.

• A function for mapping the time-dependent sky-projected position and Lambert phase factor.

2. exoscene/star.py

• Functions for computing the band-integrated irradiance of a star based on its apparent magnitude and spectral type, and instrument bandpass, using the built-in Bruzual-Persson-Gunn-Stryker (BPGS) Spectral Atlas (under exoscene/data/bpgs/)

• A function for computing the approximate parallax and proper motion offset for a star, based on the celestial coordinates and observing dates.

3. exoscene/image.py

• A function for accurately resampling an image model array to a detector array.

• Functions for translating a coronagraph PSF model to an arbitrary field point, taking into account position-dependent properties included in the model.

• Functions for applying a noise model to a detector intensity map, to simulate an image with photon counting noise, read noise, and dark current, for a given integration time.