
 Pulsar detection has become an active research topic in radio astronomy recently. One of the essential procedures for pulsar detection is pulsar candidate sifting (PCS), a procedure of find out the potential pulsar signals in a survey. However, pulsar candidates are always class-imbalanced, as most candidates are non-pulsars such as RFI and only a tiny part of them are from real pulsars. Class imbalance has greatly damaged the performance of machine learning (ML) models, resulting in a heavy cost as some real pulsars are misjudged. To deal with the problem, techniques of choosing relevant features to discriminate pulsars from non-pulsars are focused on, which is known as {\itshape feature selection}. Feature selection is a process of selecting a subset of the most relevant features from a feature pool. The distinguishing features between pulsars and non-pulsars can significantly improve the performance of the classifier even if the data are highly imbalanced. In this work, an algorithm of feature selection called K-fold Relief-Greedy algorithm (KFRG) is designed. KFRG is a two-stage algorithm. In the first stage, it filters out some irrelevant features according to their K-fold Relief scores, while in the second stage, it removes the redundant features and selects the most relevant features by a forward greedy search strategy. Experiments on the dataset of the High Time Resolution Universe survey verified that ML models based on KFRG are capable for PCS, correctly separating pulsars from non-pulsars even if the candidates are highly class-imbalanced.

Dealing with the data imbalance problem on pulsar candidates sifting based on feature selection

Pulsar candidate identification using advanced transformer-based models

In this paper we present the science potential of the enhanced X-ray Timing and Polarimetry (eXTP) mission, in its new configuration, for studies of strongly magnetized compact objects. We discuss the scientific potential of eXTP for quantum electrodynamic (QED) studies, especially leveraging the recent observations made with the NASA IXPE mission. Given eXTP’s unique combination of timing, spectroscopy, and polarimetry, we focus on the perspectives for physics and astrophysics studies of strongly magnetized compact objects, such as magnetars and accreting X-ray pulsars. Developed by an international Consortium led by the Institute of High Energy Physics of the Chinese Academy of Sciences, the eXTP mission is expected to launch in early 2030.

Physics of strong magnetism with eXTP


 Artificial intelligence methods are indispensable to identifying pulsars from large amounts of candidates. We develop a new pulsar identification system that utilizes the CoAtNet to score two-dimensional features of candidates, uses a multilayer perceptron to score one-dimensional features, and uses logistic regression to judge the scores above. In the data preprocessing stage, we performed two feature fusions separately, one for one-dimensional features and the other for two-dimensional features, which are used as inputs for the multilayer perceptron and the CoAtNet respectively. The newly developed system achieves 98.77\% recall, 1.07\% false positive rate and 98.85\% accuracy in our GPPS test set.

Pulsar Candidate Classification Using A Computer Vision Method Combining with Convolution and Attention

Fast radio bursts (FRBs), typically highly polarized, usually have a nearly constant polarization position angle (PA) during each burst. Some bursts show significant PA variations, and one of them was claimed to have a PA variation pattern consistent with the prediction of the rotating vector model (RVM) commonly adopted to fit the PA variations in radio pulsars. We systematically study the PA evolution pattern of 1727 bursts from three active repeating FRB sources monitored by the Five-hundred-meter Aperture Spherical Radio Telescope. We identify 46 bursts whose PA variations are fully consistent with the RVM. However, the inferred geometrical parameters and rotation periods derived from these RVM fittings are inconsistent from each other. This suggests that the magnetosphere of the FRB central engine is constantly distorted by the FRB emitter and the magnetic configuration is dynamically evolving.

Polarization Position Angle Swing and the Rotating Vector Model of Repeating Fast Radio Bursts

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Pulsar Candidate Classification Using A Computer Vision Method Combining with Convolution and Attention

Polarization Position Angle Swing and the Rotating Vector Model of Repeating Fast Radio Bursts