The goal of this project is to develop applications of machine learning methods to the measurement and interpretation of the 21-cm signal from the Epoch of Reionization. Possible avenues of research are (i) the application of Machine Learning to forward or backward modeling of the signal to provide alternative parameter constraints methods, (ii) the enhancement of the modeling itself (numerical simulations) with Machine Learning, (iii) exploring the use of Machine Learning to speed up the calibration of observations, (iv) the use of unsupervised learning for signal characterization
The MINERVA team is the winner of the second SKA data challenge
In 2021 July 31st, the MINERVA team won the SKA data challenge 2 with a score of 23 254. Link to the leaderboard with the frozen scores.
Previous and upcoming seminars
On tuesdays (2pm Paris time)
Workshops and Seminars
Seminars on Youtube