Bruno Merín - Personal Profiles

Main Research Fields

I study the formation of stars and their planetary systems using data from Gaia, HST, Herschel, Spitzer and CHEOPS and by applying Machine Learning algorithms trained with labels from Citizen Science projects on large archival datasets to generalise the data-driven identification of phenomena and provide less biased statistical results.

We are specifically applying machine learning to the study of exoplanets around mature stars in two ways: a first project deals with massive light-curve analysis using deep neural networks, based on data from the ground-based telescope ASTEP in Antarctica, and from CHEOPS and TESS. The goal is to define an AI-supported data pipeline that will minimise dependencies with the model selections for characterising transiting exoplanets around nearby bright stars. This will provide essentially useful information in preparation for the ESA PLATO planet-hunting mission, scheduled for launch in 2026. We have been awarded CHEOPS time to observe a set of small and temperate exoplanets which were discovered by TESS and confirmed by ASTEP and we are testing our new algorithms on these data before applying them systematically at other larger datasets. The second project deals with using Graphical Neural Networks to significantly accelerate the exoplanet atmospheric composition retrievals coming from transit spectroscopy of nearby bright planets observed with HST and JWST. This atmospheric retrievals will be essential for the upcoming ESA mission ARIEL, scheduled for launch in 2029.

Recently, we have also started a new line of research on data science on ESA's Space Science Archives, applying Machine Learning algorithms (most typically clustering algorithms, dimensionality reduction algorithms, automatic image classifiers with Convolutional Neural Networks and recommendation engines) to the large datasets in ESA Space Science Archives, hosted at ESAC. The goals of these data science projects are two-fold: on one hand we seek revealing new information in full archival datasets not previously identified with traditional data exploitation methods and on the second hand, we seek identifying operational improvements for the provision of data to the scientists of the future, e.g. by providing advanced embedded data curation and navigation in ESA Science Archives.