No CrossRef data available.
Published online by Cambridge University Press: 04 June 2018
For fifty years astronomers have been searching for pulsar signals in observational data. Throughout this time the process of choosing detections worthy of investigation, so called ‘candidate selection’, has been effective, yielding thousands of pulsar discoveries. Yet in recent years technological advances have permitted the proliferation of pulsar-like candidates, straining our candidate selection capabilities, and ultimately reducing selection accuracy. To overcome such problems, we now apply ‘intelligent’ machine learning tools. Whilst these have achieved success, candidate volumes continue to increase, and our methods have to evolve to keep pace with the change. This talk considers how to meet this challenge as a community.