Caught in the Act: Core-powered Mass-loss Predictions for Observing Atmospheric Escape

Past studies have demonstrated that atmospheric escape by the core-powered mass-loss mechanism can explain a multitude of observations associated with the radius valley that separates the super-Earth and sub-Neptune planet populations. Complementing such studies, in this work, we present a shortlist of planets that could be losing their atmospheres today if their evolution is indeed primarily dictated by core-powered mass-loss. We use Bayesian inference analysis on our planet evolution and mass-loss model to estimate the posteriors of the parameters that encapsulate the current state of a given planet, given their published masses, radii and host star properties. Our models predict that the following planets could be losing their atmospheres today at a rate ≳ 107 g/s at 50% confidence level: pi Men c, Kepler-60 d, Kepler-60 b, HD 86226 c, EPIC 249893012 b, Kepler-107 c, HD 219134 b, Kepler-80 e, Kepler-138 d and GJ 9827 d. As a by-product of our Bayesian inference analysis, we were also able to identify planets that most-likely harbor either secondary atmospheres abundant with high mean-molecular weight species, low-density interiors abundant with ices, or both. The planets belonging to this second category are WASP-47 e, Kepler-78 b, Kepler-10 b, CoRoT-7 b, HD 80653 b, 55 Cnc e and Kepler-36 b. While the aforementioned lists are by no means exhaustive, we believe that candidates presented here can serve as useful input for target selection for future surveys and for testing the importance of core-powered mass-loss in individual planetary systems.

Mining the data

This chapter introduces the ideas used in gravitational-wave data analysis, starting from matched filtering and building up to Bayesian inference analysis. Key concepts like the signal-to-noise ratio are defined and their use in actual searches is demonstrated by examples.

Bayesian inference analysis of the uncertainty linked to the evaluation of potential flood damage in urban areas

Flood damage in urbanized watersheds may be assessed by combining the flood depth–damage curves and the outputs of urban flood models. The complexity of the physical processes that must be simulated and the limited amount of data available for model calibration may lead to high uncertainty in the model results and consequently in damage estimation. Moreover depth–damage functions are usually affected by significant uncertainty related to the collected data and to the simplified structure of the regression law that is used. The present paper carries out the analysis of the uncertainty connected to the flood damage estimate obtained combining the use of hydraulic models and depth–damage curves. A Bayesian inference analysis was proposed along with a probabilistic approach for the parameters estimating. The analysis demonstrated that the Bayesian approach is very effective considering that the available databases are usually short.