Einstein@Home Discovery of 24 Pulsars in the Parkes Multi-beam Pulsar Survey

TL;DR

This paper reports the discovery of 24 new pulsars, including binary and isolated types, using advanced data analysis and distributed computing, significantly enhancing pulsar detection capabilities.

Contribution

It introduces novel methods for removing Doppler modulation in pulsar searches and demonstrates the effectiveness of volunteer computing in large-scale astrophysical data analysis.

Findings

Discovered 24 new pulsars, including binary and isolated types.

Identified pulsars with extremely high dispersion measure to spin period ratios.

Found a binary pulsar with the longest orbital period of 937 days.

Abstract

We have conducted a new search for radio pulsars in compact binary systems in the Parkes multi-beam pulsar survey (PMPS) data, employing novel methods to remove the Doppler modulation from binary motion. This has yielded unparalleled sensitivity to pulsars in compact binaries. The required computation time of approximately 17000 CPU core years was provided by the distributed volunteer computing project Einstein@Home, which has a sustained computing power of about 1 PFlop/s. We discovered 24 new pulsars in our search, of which 18 were isolated pulsars, and six were members of binary systems. Despite the wide filterbank channels and relatively slow sampling time of the PMPS data, we found pulsars with very large ratios of dispersion measure (DM) to spin period. Among those is PSR J1748-3009, the millisecond pulsar with the highest known DM (approximately 420 pc/cc). We also discovered PSR J1840-0643, which is in a binary system with an orbital period of 937 days, the fourth largest known. The new pulsar J1750-2536 likely belongs to the rare class of intermediate-mass binary pulsars. Three of the isolated pulsars show long-term nulling or intermittency in their emission, further increasing this growing family. Our discoveries demonstrate the value of distributed volunteer computing for data-driven astronomy and the importance of applying new analysis methods to extensively searched data.