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Over the past decade, hundreds of nights have been spent on the worldslargest telescopes to search for and directly detect new exoplanets usinghigh-contrast imaging (HCI). Thereby, two scientific goals are of centralinterest: First, to study the characteristics of the underlying planetpopulation and distinguish between different planet formation and evolutiontheories. Second, to find and characterize planets in our immediate Solarneighborhood. Both goals heavily rely on the metric used to quantify planetdetections and non-detections.  Current standards often rely on several explicit or implicit assumptionsabout the noise. For example, it is often assumed that the residual noise afterdata post-processing is Gaussian. While being an inseparable part of themetric, these assumptions are rarely verified. This is problematic as anyviolation of these assumptions can lead to systematic biases. This makes ithard, if not impossible, to compare results across datasets or instruments withdifferent noise characteristics.  We revisit the fundamental question of how to quantify detection limits inHCI. We focus our analysis on the error budget resulting from violatedassumptions. To this end, we propose a new metric based on bootstrapping thatgeneralizes current standards to non-Gaussian noise. We apply our method toarchival HCI data from the NACO-VLT instrument and derive detection limits fordifferent types of noise. Our analysis shows that current standards tend togive detection limit that are about one magnitude too optimistic in thespeckle-dominated regime. That is, HCI surveys may have excluded planets thatcan still exist.

Comparing Apples with Apples: Robust Detection Limits for Exoplanet High-Contrast Imaging in the Presence of non-Gaussian Noise