A machine learning-based approach towards the improvement of SNR of pulsar signals

Abstract

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 data have sprung up over the years, powered by Machine learning and Deep learning algorithms. In the current work, efforts are made to implement the currently proposed supervised machine learning models, such as ensembling techniques like Decision Tree Regressor, Random Forest Regressor, Adaboost Regressor, Gradient Boosting Regressor (GBR), K-Nearest Neighbours(KNN), and Support Vector Regressor (SVR) to find out the best possible algorithm which can work over a variety of pulsars from the EPN database of pulsars. All the data used in this work is extracted from the European Pulsar Network (EPN) database of pulsar profiles. The training dataset is obtained by post-processing the pulsar profile data from the EPN database hand testing is performed on a preselected portion of the original data. The results are obtained by testing the above algorithms for 10 different pulsars, including some historically significant ones, and the predicted profiles are plotted. We find that Gradient boosting regressor works the best in denoising pulsar data, followed closely by KNN regressor. This work also emphasizes that there is a reduction in the number of periods of folding by 35-40\% when a combination of machine learning models with the existing pulsar folding techniques like Fast Folding Algorithm(FFA) is employed, which in turn can further reduce the pulsar observation times for the telescopes hunting for pulsars today.

arXiv commentThe type of analysis does not work for new pulsar data anymore

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