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The advent of space-based observatories such as CoRoT and Kepler has enabled the testing of our understanding of stellar evolution on thousands of stars. Evolutionary models typically require five input parameters, the mass, initial Helium abundance, initial metallicity, mixing- length (assumed to be constant over time), and the age to which the star must be evolved. Some of these parameters are also very useful in characterizing the associated planets and in studying galactic archaeology. How to obtain these parameters from observations rapidly and accurately, specifically in the context of surveys of thousands of stars, is an outstanding ques- tion, one that has eluded straightforward resolution. For a given star, we typically measure the effective temperature and surface metallicity spectroscopically and low-degree oscillation frequencies through space observatories. Here we demonstrate that statistical learning, using artificial neural networks, is successful in determining the evolutionary parameters based on spectroscopic and seismic measurements. Our trained networks show robustness over a broad range of parameter space, and critically, are entirely computationally inexpensive and fully automated. We analyze the observations of a few stars using this method and the results com- pare well to inferences obtained using other techniques. This method is both computationally cheap and inferentially accurate, paving the way for analyzing the vast quantities of stellar observations from past, current, and future missions.
Seismology of stars that exhibit solar-like oscillations develops a growing interest with the wealth of observational results obtained with the CoRoT and Kepler space-borne missions. In this framework, relations between asteroseismic quantities and s
The differentially rotating outer layers of stars are thought to play a role in driving their magnetic activity, but the underlying mechanisms that generate and sustain differential rotation are poorly understood. We report the measurement of latitud
In previous work we identified six Sun-like stars observed by Kepler with exceptionally clear asteroseismic signatures of rotation. Here, we show that five of these stars exhibit surface variability suitable for measuring rotation. In order to furthe
The aim of this paper is to determinate the fundamental parameters of six exoplanet host (EH) stars and their planets. While techniques for detecting exoplanets yield properties of the planet only as a function of the properties of the host star, hen
Magnetic fields play an important role at all stages of stellar evolution. In Sun-like stars, they are generated in the outer convective layers. Studying the large-scale magnetic fields of these stars enlightens our understanding of the field propert