Christopher Messenger

April 6, 2022, at 4 PM 

Online (Zoom)

 

Abstract

 

As gravitational wave astrophysicists we find ourselves on the rising wave of machine learning sweeping through the physical sciences. In the past ~5 years many of us have embraced the world of machine learning and tried to apply it to our most challenging problems including the detection of gravitational wave signals buried deep in our detector noise. However, we are domain-experts in astrophysics and statistical analysis, not necessarily experts in machine learning. In this talk I will walk through the steps we took to outsource our problem to a collection of the world’s best machine learning experts – for free.

There is a large and growing community of data analysis experts across all sectors – academic, industrial and commercial. Specifically, one such group of experts that focus on the application of machine learning can be found at Kaggle, a Google-owned data-science company. In addition to providing a host of online resources and support for anyone interested in data-science, Kaggle hosts competitions whereby individuals or teams are invited to solve data analysis problems where winners can earn “medals” and in some cases large cash prizes. We turned to Kaggle who helped us set up our first data challenge – the task of finding gravitational wave signals from binary black hole mergers.

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Christopher Messenger
(University of Glasgow, UK)


Chris Messenger is a senior lecturer at the University of Glasgow, UK. He has been a long time member of the global gravitational wave collaboration (currently) known as the LIGO-Virgo-Kagra Collaboration (LVK). He obtained his undergraduate degree from the University of Birmingham, UK where he also completed his PhD on the topic of gravitational wave detection for continuously emitting sources. He then worked as a postdoctoral researcher at the University of Glasgow, UK, at the Albert Einstein Institute in Hannover, Germany, and Cardiff University, UK before returning to Glasgow University as a Lord Kelvin Adam Smith Fellow. His current research interests lie in the field of gravitational wave cosmology and the introduction of machine learning and quantum computation to the problems of detection and Bayesian parameter estimation for any and all types of gravitational wave signal.

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