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arXiv 2011.14388astro-ph.HE
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一种基于机器学习的脉冲星信号SNR改进方法

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

Amitesh Singh, Kamlesh N Pathak

摘要:

目前,许多脉冲星折叠算法被用来产生强信噪比的总强度剖面。但是它们需要大量的观测时间来有效地提高信噪比。近年来,在机器学习和深度学习算法的支持下,脉冲星总强度数据去噪的新方法如雨后春笋般涌现。在目前的工作中,努力实现目前提出的监督机器学习模型,例如集成技术,如决策树回归器,随机森林回归器,Adaboost回归器,梯度提升回归器(GBR),K-最近邻(KNN)和支持向量回归器(SVR),以找出最好的可能算法,该算法可以在EPN数据库中的各种机器上工作。本工作中使用的所有数据均来自欧洲脉冲星网络(EPN)的脉冲星剖面数据库。训练数据集是通过对来自EPN数据库的脉冲星轮廓数据进行后处理而获得的,手动测试是在原始数据的预选部分上进行的。通过对10个不同的地震波(包括一些历史上重要的地震波)测试上述算法获得了结果,并绘制了预测的剖面图。我们发现梯度提升回归器在脉冲星数据去噪方面效果最好,其次是KNN回归器。这项工作还强调,当采用机器学习模型与现有脉冲星折叠技术(如快速折叠算法(FFA))相结合时,折叠周期的数量减少了35- 40%,这反过来又可以进一步减少脉冲星观测时间,用于今天的望远镜寻找脉冲星。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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