For my PhD thesis at Valongo Observatory (UFRJ), I have chosen to study the atmospheres of exoplanets, i.e. planets outside the Solar System. To this day there are more than 5000 confirmed exoplanets, some similar to the Earth, other similar to Jupiter, Saturn, Neptune, and some different from anything we have ever imagined!
Although Earth-like planets seem to be the most interesting kind of planetary objects, they are really hard to observe due to their small size. Therefore, most of the available observed data are from giant gaseous planets. These are the type of planets I currently work with.
My work is based on the study and analysis of atmospheric spectral data from (mostly) giant exoplanets. I specifically work with transmission spectra, which are spectra obtained when there is a certain lineup configuration, and we can see planet passing in front of its star. When this happens, the planet "hides" a small portion of the star, as in the image below, and part of the stellar light is blocked. However, is this planet passing in front of the star has a significant atmosphere, part of the stellar radiation goes through the planet's atmosphere, and interacts with the atmospheric chemical species. Thus, when we observe this transmitted light, we have a transmission spectrum.
© Christine Daniloff/MIT, Julien de Wit
This is why the transmission method is useful to help characterize the atmosphere of an exoplanet: because atoms, molecules, and other species leave individual imprints in the spectra. These imprints are unique for each species, so you can think of it like a bar code, or a signature. And so, as we analyze spectral features, such as the shape and height of lines, we can infer which chemical species exist in that atmosphere. However, the spectral quality and resolution available today is rather low. To manage this problem, we use the observed spectral data to create high-resolution synthetic spectra.
Inferring the structure and composition of an atmosphere using observed and synthetic spectra is a very popular technique called atmospheric retrieval. We use a Python code to establish different atmospheric models, and try to adjust each model to our observed data. In the end, the most well-adjusted model is considered the one that best represents the given atmosphere, and the properties of the best model are an estimate to the properties of that atmosphere. For example, if the best model is a cloud-free model, chances are that this atmosphere is, in fact, free of clouds.
Therefore, the retrieval technique helps determining atmospheric features, such as temperature, pressure, chemical abundances, and even the presence (or absence) of clouds. Furthermore, by understanding exoplanet atmospheres, we can learn about the formation and evolution of exo and Solar Sytem planets, including our own planet, and even get to know potentially habitable worlds!
Mais atualizações sobre o meu trabalho serão postadas aqui e na minha página do Twitter, então certifique-se de me seguir lá ;)