About the School
Massive stars are fundamental to understanding the chemical and dynamical evolution of the universe. As they evolve, they release chemically enriched material through powerful winds and eruptions, shaping their surroundings. Studying their physical properties and evolution is crucial—especially considering the impact of binary and multiple systems on their final stages. These stars also play a key role in generating gravitational waves, making them central to current research in multi-messenger astronomy.
Today, researchers rely on both synthetic and observational data to study massive stars. In this context, machine learning has become a powerful tool for identifying patterns, analyzing large datasets, and predicting stellar properties.
Complementing this, astrostatistics provides a rigorous framework to handle uncertainties and validate models— ensuring robust and trustworthy scientific conclusions.
In the frame of the Marie Curie Staff Exchange action, titled OCEANS (Overcoming Challenges in the Evolution And Nature of Massive Stars), we are organizing the Machine Learning and Astrostatistics School: Applications to Massive Stars, hosted in Valparaíso, Chile.
The school will provide a solid introduction to modern data analysis techniques in stellar astrophysics. Through lectures and hands-on sessions, participants will explore how machine learning and statistical methods can be applied to massive stars research—from generating synthetic data to analyzing large-scale observational surveys.
