Unveiling the Cosmos: a Comprehensive Exploration of Key Factors Shaping Pulsar Candidates Using HTRU2 Dataset Analysis and Predictions

Following the completion of the 500-metre aperture spherical radio telescope (FAST) and the launch of the 19-beam receiver survey project, the number of candidates obtained from pulsar searches has exhibited a notable increase, resulting in a significant expansion in the number of observable celestial objects and astronomical phenomena. It has furnished a substantial corpus of data for astronomical research. However, the vast quantity of pulsar candidate data presents a considerable challenge in identifying genuine candidates, due to the presence of numerous interfering signals. (1) The search for pulsar candidates is a complex and time-consuming process, necessitating the acquisition and analysis of a substantial amount of observational data; (2) The discovery of each new pulsar contributes to our understanding of extreme physical processes in the Universe. It is therefore necessary to search for new pulsar candidates on an ongoing basis. This paper examines the key factors influencing the identification of pulsar candidates, with a view to developing an efficient screening process. The High Time Resolution Universe Survey 2 (HTRU2) dataset from the University of California, Irvine (UCI or UC Irvine) platform was employed for data analysis and the construction of predictive analysis models. These were evaluated using classification metrics, including precision, recall, F1-score, and ROC-AUC curves. The latter were constructed using decision trees (DT), random forest (RF) and alpha-investing algorithms. The experimental results demonstrate that the three most critical factors affecting the pulsar candidates are the mean value of the integrated profile (Mean_IP), the excess kurtosis of the integrated profile (Excess_kurtosis_IP), and the skewness of the integrated profile (Skewness_IP).