We describe SPINN (Straightforward Pulsar Identification using Neural Networks), a high-performance 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%. Furthermore, it ranks 99% of pulsars among the top 0.11% of candidates, and 95% among the top 0.01%. In conjunction with the PEASOUP pipeline (Barr et al., in prep.), 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 milliseconds. 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.
@article{arxiv.1406.3627,
title = {SPINN: a straightforward machine learning solution to the pulsar candidate selection problem},
author = {V. Morello and E. D. Barr and M. Bailes and C. M. Flynn and E. F. Keane and W. van Straten},
journal= {arXiv preprint arXiv:1406.3627},
year = {2014}
}
Comments
Accepted for publication in MNRAS, 13 pages, 9 figures. To obtain the set of pulsar candidates we used to train SPINN see http://astronomy.swin.edu.au/~vmorello/