Unveiling habitable planets: Toy coronagraph tackles the exozodiacal dust challenge

TL;DR

This paper presents Toy Coronagraph, a Python tool that helps researchers assess how exozodiacal dust affects the detection of Earth-like exoplanets, aiding in the development of better observation strategies.

Contribution

It introduces a novel Python package that simulates the impact of exozodiacal dust on exoplanet imaging, including contrast metrics and visualization tools, to improve future telescope designs.

Findings

Toy Coronagraph accurately models dust impact on contrast curves.

The tool enables visualization of exoplanet motion against dust backgrounds.

It provides a foundation for optimizing exoplanet detection strategies.

Abstract

Directly imaging Earth-like exoplanets within habitable zones is challenging because faint signals can be obscured by exozodiacal dust, analogous to our solar system's zodiacal dust. This dust scatters starlight, creating a bright background noise. This paper introduces Toy Coronagraph, a Python package designed to quantify the impact of this dust on exoplanet detection. It takes circularly symmetric disk images point spread functions (PSFs), and exoplanet orbital parameters as input, generating key metrics like contrast curves, signal-to-noise ratios, and dynamic visualizations of exoplanet motion under the dust background. The package also provides tools for generating vortex coronagraph PSFs and includes example disk images. Toy Coronagraph empowers researchers to understand exozodiacal dust, develop mitigation strategies, and optimize future telescope designs and mission time, ultimately advancing the search for potentially habitable worlds. Future work will focus on handling non-circularly symmetric inputs, incorporating realistic noise models, and estimating exoplanet yield rates for future space telescope missions.