Decoding Lights from Exotic Worlds
If I had to select a single word to define the field of exoplanets, that word would be revolutionary. During the past 20 years, about 3500 planets have been found around every type of star. The current statistical estimates indicate that, on average, every star in our Galaxy hosts at least one planetary companion, i.e. our Milky Way is crowded with a thousand billion planets!
The most revolutionary aspect of this young field is the discovery that the Solar System does not appear to be the paradigm in our Galaxy, but rather one of the many possible configurations we are seeing out there.
A key observable for planets is the chemical composition and state of their atmosphere. Knowing what atmospheres are made of is essential to clarify, for instance, whether a planet was born in the orbit it is observed in or whether it has migrated a long way; it is also critical to understand the role of stellar radiation on escape processes, chemical evolution and climate. The atmospheric composition is the only indicator able to discriminate an habitable/inhabited planet from a sterile one.
The ExoLights team at UCL (PI G. Tinetti) has been funded by the European Research Council to develop novel techniques for data analysis and interpretation of exoplanet atmospheres as well as new space mission concepts for exoplanet spectroscopy.
ARIEL - Atmospheric Remote-sensing Infrared Exoplanet Large-survey, (European Space Agency M4 mission).
Information theory and machine learning
I have worked on dynamical systems and information theory during my PhD and, more recently, I have discovered that many of these techniques are also very useful in the field of exoplanets, for data analysis and interpretation. Together with my team and collaborators UCL, we have pioneered the use of machine and deep learning techniques to the analysis of exoplanet atmospheres.