This hands-on session of the NAC* aims to introduce participants to the usefulness and best practices of machine learning for astronomical research.

*The NAC is the Dutch Astronomers Conference (Nederlandse Astronomenconferentie).

Programme

Machine learning has gained prominence in astronomical data analysis in recent years. This is due to the growth of data volume and complexity, the latency with which data must now be processed, and the explosion of available tools from the artificial intelligence community. In this session I will deliver a short talk on the recent advances in machine learning for astrophysics, where I will focus on real-time transient surveys like Apertif, LSST, and CHIME. My talk will be followed by an interactive session in which we will attempt to train a convolutional deep neural network in order to discover fast radio bursts.

Invited speakers

The session is led by Dr. Liam Connor from the University of Amsterdam

Scientific organisers

Huub Röttgering, Jan Lub, Simon Portegies Zwart, Martijn Oei, Martijn Wilhelm, Frits Sweijen, Lydia Stofanova, Dirk van Dam, Rafaël Mostert, Marieke Baan

Contact

Rafaël Mostert: mostert? @ ?strw.leidenuniv.nl