We are developing new modeling algorithms that use Markov-chain Monte Carlo (MCMC) methods, coupled to Bayesian statistics, with the goal of discovering new low-mass planets and better characterizing their host stars. A key part of this work is the development of new and improved noise models that consider the effects of both instrumental and stellar noise sources.
Type of research: Research – actively recruiting new young researchers
Status: Ongoing
Investigadores James Jenkins & Pablo Peña
Funding source: FONDECYT Regular, Basal CATA2, CASSACA