Single-pulse classifier for the LOFAR Tied-Array All-sky Survey

TL;DR

The paper introduces SpS, a machine-learning classifier tailored for LOFAR's all-sky survey, significantly improving the detection of astrophysical signals amidst RFI and discovering new pulsars.

Contribution

It presents a novel machine-learning classifier optimized for RFI-rich environments, enhancing single-pulse detection in large radio survey data.

Findings

Discovered 7 new pulsars using SpS.

Identified ~80 known sources in blind searches.

Successfully applied to other projects like FRB 121102.

Abstract

Searches for millisecond-duration, dispersed single pulses have become a standard tool used during radio pulsar surveys in the last decade. They have enabled the discovery of two new classes of sources: rotating radio transients and fast radio bursts. However, we are now in a regime where the sensitivity to single pulses in radio surveys is often limited more by the strong background of radio frequency interference (RFI, which can greatly increase the false-positive rate) than by the sensitivity of the telescope itself. To mitigate this problem, we introduce the Single-pulse Searcher (SpS). This is a new machine-learning classifier designed to identify astrophysical signals in a strong RFI environment, and optimized to process the large data volumes produced by the new generation of aperture array telescopes. It has been specifically developed for the LOFAR Tied-Array All-Sky Survey (LOTAAS), an ongoing survey for pulsars and fast radio transients in the northern hemisphere. During its development, SpS discovered 7 new pulsars and blindly identified ~80 known sources. The modular design of the software offers the possibility to easily adapt it to other studies with different instruments and characteristics. Indeed, SpS has already been used in other projects, e.g. to identify pulses from the fast radio burst source FRB 121102. The software development is complete and SpS is now being used to re-process all LOTAAS data collected to date.