For best experience please turn on javascript and use a modern browser!
You are using a browser that is no longer supported by Microsoft. Please upgrade your browser. The site may not present itself correctly if you continue browsing.

The National Science Agenda has awarded a 5 million euro grant to CORTEX – the Center for Optimal, Real-Time Machine Studies of the Explosive Universe. The CORTEX consortium of 12 partners from academia, industry and society will make self-learning machines faster, to figure out how massive cosmic explosions work, and to innovative systems that benefit our society.

Image: ASTRON / D. Futselaar

Machine learning has rapidly become an integral part of our lives. It is now commonly used for speech recognition and information retrieval. This is also true in science, for detecting patterns in nature and the Universe. But the need is growing rapidly for such machines to respond quickly, for example in self-driving cars and for responsive manufacturing. On a more fundamental level, self-learning machines help us unveil a dynamical Universe we did not know existed up until recently. Bright explosions appear all over the radio and gravitational-wave sky. Many citizens and scientists are curious to understand where these come from.

‘In CORTEX we aim to solve these open problems by bridging fundamental research to society,' says Dr Joeri van Leeuwen (ASTRON and Univerity of Amsterdam), the project lead. 'We can only reach these ambitious goals if academic, applied, public and industry partners work together.’

The 5 million Euro grant from the Nationale Wetenschapsagenda will fund research at partners ASTRON, Nikhef, SURF, Netherlands eScience Center, Universiteit van Amsterdam, Radboud Universiteit Nijmegen, Centrum Wiskunde & Informatica, IBM Nederland B.V., BrainCreators B.V., ABN AMRO N.V., NVIDIA, NOVA, and Stichting ILT; in cooperation with Rijksmuseum, Thermo Fisher Scientific, and Leiden University.

‘Gravitational waves whipped up by merging black holes and neutron stars peak for less than a few seconds, maybe several times per month,’ says Dr. Sarah Caudill (Nikhef), ‘and fast self-learning machines can help us recognise the events as they occur.’ Responding quickly to these using radio telescopes can help us understand better how the universe works, Dr. Antonia Rowlinson (University of Amsterdam and ASTRON) explains: ‘Using machine learning, we will pick out their radio afterglow from thousands of sources and, by watching how they change, we can determine the vast energies that must be involved.‘

'CORTEX is unique in that we subsequently translate the latest mathematical and computer-science discoveries in faster and better computing for industry and society,’ says Dr Raymond Oonk from SURF. Maarten Stol from BrainCreators concurs: ‘Both for startups and for large companies, machine learning on large data streams is essential. Only in a collaboration such as CORTEX can cutting edge science make its way to innovation in business.’

UvA participation

The University of Amsterdam GRAPPA, Anton Pannekoek Institute for Astronomy and Institute of Physics led efforts focus on the numerical modelling, joint characterisation and interpretation of neutron star binary mergers using gravitational wave and radio observations. The effort comprises the participation of the groups led by Samaya Nissanke (UvA work package contact and lead), Antonia Rowlinson (co-project lead), Philipp Moesta, Oliver Porth, and Sera Markoff.