Related papers: Dealing with the data imbalance problem on pulsar …
It is an active topic to investigate the schemes based on machine learning (ML) methods for detecting pulsars as the data volume growing exponentially in modern surveys. To improve the detection performance, input features into an ML model…
Discovering pulsars is a significant and meaningful research topic in the field of radio astronomy. With the advent of astronomical instruments such as he Five-hundred-meter Aperture Spherical Telescope (FAST) in China, data volumes and…
Feature selection is beneficial for improving the performance of general machine learning tasks by extracting an informative subset from the high-dimensional features. Conventional feature selection methods usually ignore the class…
Feature selection (FS) is a process which attempts to select more informative features. In some cases, too many redundant or irrelevant features may overpower main features for classification. Feature selection can remedy this problem and…
Pulsar candidate sifting is an essential process for discovering new pulsars. It aims to search for the most promising pulsar candidates from an all-sky survey, such as High Time Resolution Universe (HTRU), Green Bank Northern Celestial Cap…
This work brings together some of the most common machine learning (ML) algorithms, and the objective is to make a comparison at the level of obtained results from a set of unbalanced data. This dataset is composed of almost 17 thousand…
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…
Improving survey specifications are causing an exponential rise in pulsar candidate numbers and data volumes. We study the candidate filters used to mitigate these problems during the past fifty years. We find that some existing methods…
Searching for extraterrestrial, transient signals in astronomical data sets is an active area of current research. However, machine learning techniques are lacking in the literature concerning single-pulse detection. This paper presents a…
Pulsar searching with next-generation radio telescopes requires efficiently sifting through millions of candidates generated by search pipelines to identify the most promising ones. This challenge has motivated the utilization of Artificial…
We evaluate the performance of four different machine learning (ML) algorithms: an Artificial Neural Network Multi-Layer Perceptron (ANN MLP ), Adaboost, Gradient Boosting Classifier (GBC), XGBoost, for the separation of pulsars from radio…
In this work, we explore the possibility of using probabilistic learning to identify pulsar candidates. We make use of Deep Gaussian Process (DGP) and Deep Kernel Learning (DKL). Trained on a balanced training set in order to avoid the…
Pulsar surveys generate millions of candidates per run, overwhelming manual inspection. This thesis builds a deep learning pipeline for radio pulsar candidate selection that fuses array-derived features with image diagnostics. From…
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…
In this paper, we present a novel artificial intelligence (AI) program that identifies pulsars from recent surveys using image pattern recognition with deep neural nets---the PICS (Pulsar Image-based Classification System) AI. The AI mimics…
Pulsars have been primarily detected by their narrow pulses or periodicity in time domain data. Interferometric surveys for pulsars are challenging due to the trade-off between beam sensitivity and beam size and the corresponding tradeoff…
Software fault prediction (SFP) is a critical task in software engineering, enabling early identification of faults in modules to improve software quality and reduce maintenance costs. This research investigates the combined effects of…
As performance of dedicated facilities continually improved, massive pulsar candidates are being received, which makes selecting valuable pulsar signals from candidates challenging. In this paper, we designed a deep convolutional neural…
Many pulsar folding algorithms are currently deployed to generate strong SNRs for the total intensity profiles. But they require large observation times to improve the SNR effectively. New approaches to de-noise the pulsar total intensity…
Feature selection is a crucial step in machine learning, especially for high-dimensional datasets, where irrelevant and redundant features can degrade model performance and increase computational costs. This paper proposes a novel…