David P. Fleming, Rory Barnes, Rodrigo Luger, and Jacob T. VanderPlas
We model the long-term XUV luminosity of TRAPPIST-1 to constrain the evolving high-energy radiation environment experienced by its planetary system. Using Markov Chain Monte Carlo (MCMC), we derive probabilistic constraints for TRAPPIST-1's stellar and XUV evolution that account for observational uncertainties, degeneracies between model parameters, and empirical data of low-mass stars. We constrain TRAPPIST-1's mass to 0.089 +/- 0.001 Msun and find that its early XUV luminosity likely saturated at log10(L_{XUV}/L_{bol}) = -3.03^{+0.23}_{-0.12}. From the posterior distribution, we infer that there is a ~40% chance that TRAPPIST-1 is still in the saturated phase today, suggesting that TRAPPIST-1 has maintained high activity and L_{XUV}/L_{bol} ~ 10^-3 for several Gyrs. TRAPPIST-1's planetary system therefore likely experienced a persistent and extreme XUV flux environment, potentially driving significant atmospheric erosion and volatile loss. The inner planets likely received XUV fluxes ~10^3 - 10^4 times that of the modern Earth during TRAPPIST-1's ~1 Gyr-long pre-main sequence phase. Deriving these constraints via MCMC is computationally non-trivial, so scaling our methods to constrain the XUV evolution of a larger number of M dwarfs that harbor terrestrial exoplanets would incur significant computational expenses. We demonstrate that approxposterior, an open source Python machine learning package for approximate Bayesian inference using Gaussian processes, accurately and efficiently replicates our analysis using 980 times less computational time and 1330 times fewer simulations than MCMC sampling using emcee. We find that approxposterior derives constraints with mean errors on the best fit values and 1 sigma uncertainties of 0.61% and 5.5%, respectively, relative to emcee.
The published version in The Astrophysical Journal.
The open-access ArXiv version.