Ensemble candidate classification for the LOTAAS pulsar survey

One of the biggest challenges arising from modern large-scale pulsar surveys is the number of candidates generated. Here, we implemented several improvements to the machine learning (ML) classifier previously used by the LOFAR Tied-Array All-Sky Survey (LOTAAS) to look for new pulsars via filtering the candidates obtained during periodicity searches. To assist the ML algorithm, we have introduced new features which capture the frequency and time evolution of the signal and improved the signal-to-noise calculation accounting for broad profiles. We enhanced the ML classifier by including a third class characterizing RFI instances, allowing candidates arising from RFI to be isolated, reducing the false positive return rate. We also introduced a new training data set used by the ML algorithm that includes a large sample of pulsars misclassified by the previous classifier. Lastly, we developed an ensemble classifier comprised of five different Decision Trees. Taken together these updates improve the pulsar recall rate by 2.5 per cent, while also improving the ability to identify pulsars with wide pulse profiles, often misclassified by the previous classifier. The new ensemble classifier is also able to reduce the percentage of false positive candidates identified from each LOTAAS pointing from 2.5 per cent (∼500 candidates) to 1.1 per cent (∼220 candidates)

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

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 seven 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