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Modern radio pulsar surveys produce a large volume of prospective candidates,the majority of which are polluted by human-created radio frequencyinterference or other forms of noise. Typically, large numbers of candidatesneed to be visually inspected in order to determine if they are real pulsars.This process can be labor intensive. In this paper, we introduce an algorithmcalled PEACE (Pulsar Evaluation Algorithm for Candidate Extraction) whichimproves the efficiency of identifying pulsar signals. The algorithm ranks thecandidates based on a score function. Unlike popular machine-learning basedalgorithms, no prior training data sets are required. This algorithm has beenapplied to data from several large-scale radio pulsar surveys. Using thehuman-based ranking results generated by students in the Arecibo Remote Commandenter programme, the statistical performance of PEACE was evaluated. It wasfound that PEACE ranked 68% of the student-identified pulsars within the top0.17% of sorted candidates, 95% within the top 0.34%, and 100% within the top3.7%. This clearly demonstrates that PEACE significantly increases the pulsaridentification rate by a factor of about 50 to 1000. To date, PEACE has beendirectly responsible for the discovery of 47 new pulsars, 5 of which aremillisecond pulsars that may be useful for pulsar timing basedgravitational-wave detection projects.

PEACE: Pulsar Evaluation Algorithm for Candidate Extraction -- A software package for post-analysis processing of pulsar survey candidates