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

TL;DR

SPINN is a machine learning tool that efficiently identifies pulsar candidates with high accuracy, significantly reducing manual inspection efforts and enabling large-scale pulsar surveys.

Contribution

The paper introduces SPINN, a neural network-based method that achieves high detection accuracy and drastically reduces candidate numbers for pulsar surveys.

Findings

Identifies all known pulsars in survey data.

Maintains a false positive rate of 0.64%.

Ranks 99% of pulsars among top 0.11% candidates.

Abstract

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.