Predicting Pulsars from Imbalanced Dataset with Hybrid Resampling Approach

Pulsar stars, usually neutron stars, are spherical and compact objects containing a large quantity of mass. Each pulsar star possesses a magnetic field and emits a slightly different pattern of electromagnetic radiation which is used to identify the potential candidates for a real pulsar star. Pulsar stars are considered an important cosmic phenomenon, and scientists use them to study nuclear physics, gravitational waves, and collisions between black holes. Defining the process of automatic detection of pulsar stars can accelerate the study of pulsar stars by scientists. This study contrives an accurate and efficient approach for true pulsar detection using supervised machine learning. For experiments, the high time-resolution (HTRU2) dataset is used in this study. To resolve the data imbalance problem and overcome model overfitting, a hybrid resampling approach is presented in this study. Experiments are performed with imbalanced and balanced datasets using well-known machine learning algorithms. Results demonstrate that the proposed hybrid resampling approach proves highly influential to avoid model overfitting and increase the prediction accuracy. With the proposed hybrid resampling approach, the extra tree classifier achieves a 0.993 accuracy score for true pulsar star prediction

Applying hybrid clustering in pulsar candidate sifting with multi-modality for FAST survey

Pulsar search is always the basis of pulsar navigation, gravitational wave detection and other research topics. Currently, the volume of pulsar candidates collected by Five-hundred-meter Aperture Spherical radio Telescope (FAST) shows an explosive growth rate that has brought challenges for its pulsar candidate filtering System. Particularly, the multi-view heterogeneous data and class imbalance between true pulsars and non-pulsar candidates have negative effects on traditional single-modal supervised classification methods. In this study, a multi-modal and semi-supervised learning based pulsar candidate sifting algorithm is presented, which adopts a hybrid ensemble clustering scheme of density-based and partition-based methods combined with a feature-level fusion strategy for input data and a data partition strategy for parallelization. Experiments on both HTRU (The High Time Resolution Universe Survey) 2 and FAST actual observation data demonstrate that the proposed algorithm could excellently identify the pulsars: On HTRU2, the precision and recall rates of its parallel mode reach 0.981 and 0.988. On FAST data, those of its parallel mode reach 0.891 and 0.961, meanwhile, the running time also significantly decrease with the increment of parallel nodes within limits. So, we can get the conclusion that our algorithm could be a feasible idea for large scale pulsar candidate sifting of FAST drift scan observation