SPINN: A straightforward machine learning solution to the pulsar candidate selection problem

Abstract

We describe SPINN (Straightforward Pulsar Identification using Neural Networks), a highperformance machine learning solution developed to process increasingly large data outputs from pulsar surveys. SPINN has been cross-validated on candidates from the southern High Time Resolution Universe (HTRU) survey and shown to identify every known pulsar found in the survey data while maintaining a false positive rate of 0.64 per cent. Furthermore, it ranks 99 per cent of pulsars among the top 0.11 per cent of candidates, and 95 per cent among the top 0.01 per cent. In conjunction with the PEASOUP pipeline, it has already discovered four new pulsars in a re-processing of the intermediate Galactic latitude area of HTRU, three of which have spin periods shorter than 5 ms. SPINN's ability to reduce the amount of candidates to visually inspect by up to four orders of magnitude makes it a very promising tool for future large-scale pulsar surveys. In an effort to provide a common testing ground for pulsar candidate selection tools and stimulate interest in their development, we also make publicly available the set of candidates on which SPINN was cross-validated. © 2014 The Authors. Published by Oxford University Press on behalf of the Royal Astronomical Society.