A long-term variability study of young stars: Periodicity, hot spots, accretion and early evolution at the time of planet formation

Young stars are variable due to the processes happening on the star and its protoplanetary disk at the time of planet formation. The star-disk connection holds the clues to several open questions including rotational evolution, the origin of our Solar System and the feasibility of habitable planets. Early stellar evolution depends on accretion, angular momentum and mass transport between disk and star (affecting stellar activity), planet formation and migration (linked to accretion), and the cluster environment and structure. The main technical limitation for these studies is the scale: few stellar radii to sub-au are impossible to map even with interferometry.

Time-resolved observations are therefore the only way to access these tiny spatial scales. In this project, we will "use time to map space" in young stars via a variability survey of populous clusters in the Northern Hemisphere in an unprecedented effort to study the properties of thousands of stars aged 1-5 Myr using data from the Javalambre telescope and the North-PHASE international legacy project (led by A. Sicilia-Aguilar). The successful candidate will work on the identification and calibration of the data for different star forming regions, in order to derive a statistical study of the variability properties of the stars in the cluster and their causes (accretion, occultations by the inner disk, hot and cold spots) and how these evolve in few-years timescales. The survey covers 5 years of data, depending on the starting point of the student, 2 or more years of data will be available. The student will derive models to explain the various causes of variability and their dependency on the stellar mass and cluster environment. Possibilities to collaborate with our international partners in the EU and beyond in the North-PHASE project are available through the course of the PhD.

Knowledge of stellar astrophysics and/or star formation as well as Python programming will be appreciated.

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