摘要：Thanks to missions like Kepler and TESS, we now have access to tens of thousands of high-precision, fast-cadence, and long-baseline stellar photometric observations. In principle, these light curves encode a vast amount of information about stellar variability and, in particular, the distribution of starspots and other features on their surfaces. Unfortunately, the problem of inferring stellar surface properties from a rotational light curve is famously ill-posed, as it often does not admit a unique solution. Inferences about the number, size, contrast, and location of spots can therefore depend very strongly on the assumptions of the model, the regularization scheme, or the prior. The goal of this paper is twofold: (1) to explore the various degeneracies affecting the stellar light-curve "inversion" problem and their effect on what can and cannot be learned from a stellar surface, given unresolved photometric measurements, and (2) to motivate ensemble analyses of the light curves of many stars at once as a powerful data-driven alternative to common priors adopted in the literature. We further derive novel results on the dependence of the null space on stellar inclination and limb darkening and show that single-band photometric measurements cannot uniquely constrain quantities like the total spot coverage without the use of strong priors. This is the first in a series of papers devoted to the development of novel algorithms and tools for the analysis of stellar light curves and spectral time series, with the explicit goal of enabling statistically robust inferences about their surface properties.
