Special Session SS34  3 July 2020

NAC: Machine learning

Aims and scope

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).


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 RŲttgering, Jan Lub, Simon Portegies Zwart, Martijn Oei, Martijn Wilhelm, Frits Sweijen, Lydia Stofanova, Dirk van Dam, RafaŽl Mostert, Marieke Baan


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

Updated on Thu Jun 25 12:40:25 CEST 2020