starry_process: Interpretable Gaussian processes for stellar light curves

TL;DR

starry_process introduces an interpretable Gaussian process model for stellar light curves that explicitly incorporates physical star properties, enabling better understanding and marginalization of stellar variability effects.

Contribution

It provides a novel GP framework that models stellar flux variability with explicit physical parameters, improving interpretability over traditional black-box models.

Findings

Allows modeling of flux variability due to starspots with physical parameters

Enables understanding of starspot properties from light curves

Facilitates marginalization of stellar variability as a nuisance signal

Abstract

In this note we present the starry_process code, which implements an interpretable Gaussian process (GP) for modeling variability in stellar light curves. As dark starspots rotate in and out of view, the total flux received from a distant star will change over time. Unresolved flux time series therefore encode information about the spatial structure of features on the stellar surface. The starry_process software package allows one to easily model the flux variability due to starspots, whether one is interested in understanding the properties of these spots or marginalizing over the stellar variability when it is treated as a nuisance signal. The main difference between the GP implemented here and typical GPs used to model stellar variability is the explicit dependence of our GP on physical properties of the star, such as its period, inclination, and limb darkening coefficients, and on properties of the spots, such as their radius and latitude distributions. This code is the Python implementation of the interpretable GP algorithm developed in Luger, Foreman-Mackey, and Hedges (2021).