A Framework for Assessing the Performance of Pulsar Search Pipelines

TL;DR

This paper introduces a framework to evaluate how non-stationary noise and RFI impact pulsar search pipelines, emphasizing the need for improved algorithms to enhance detection sensitivity and reduce false positives.

Contribution

It provides a novel framework for assessing pulsar search pipeline performance under realistic noise conditions, highlighting the effects of spectrum whitening and the importance of noise mitigation.

Findings

Non-stationary noise and RFI significantly affect detection metrics.

Current spectrum whitening algorithms reduce sensitivity to long-period pulsars.

Effective noise removal improves false positive rates and overall pipeline performance.

Abstract

In this paper, we present a framework for assessing the effect of non-stationary Gaussian noise and radio frequency interference (RFI) on the signal to noise ratio, the number of false positives detected per true positive and the sensitivity of standard pulsar search pipelines. The results highlight the necessity to develop algorithms that are able to identify and remove non-stationary variations from the data before RFI excision and searching is performed in order to limit false positive detections. The results also show that the spectrum whitening algorithms currently employed, severely affect the efficiency of pulsar search pipelines by reducing their sensitivity to long period pulsars.