论文标题
基于机器学习的方法来改善脉冲星信号的SNR
A machine learning-based approach towards the improvement of SNR of pulsar signals
论文作者
论文摘要
目前，许多PULSAR折叠算法都被部署以为总强度曲线生成强大的SNR。但是他们需要大量的观察时间才能有效地改善SNR。多年来，由机器学习和深度学习算法提供支持的新方法来消除PULSAR总强度数据多年。在当前的工作中，要努力实施当前提出的监督机器学习模型，例如决策树回归器，随机森林回归器，Adaboost回归剂，梯度升级回归器（GBR），K-Nearest邻居（KNN）和支持媒介回归者（SVR）可以从最佳的Algorith corvers of Algorith corvers of Algorith corvers of Algorith corvers of Algorith verter的范围，从而找到了一项综述，这是一件能力的范围。脉冲星。这项工作中使用的所有数据都是从欧洲PULSAR网络（EPN）数据库中提取的。通过在原始数据的预选部分上执行EPN数据库手动测试的PULSAR配置文件数据来获得训练数据集。结果是通过测试上述10种不同脉冲星的上述算法获得的，其中包括一些具有历史意义的脉冲星，并绘制了预测的曲线。我们发现，梯度提升回归器在降解脉冲星数据方面是最好的，其次是KNN回归器。这项工作还强调，当使用机器学习模型与现有的Pulsar折叠技术（例如快速折叠算法（FFA））的组合相结合时，折叠周期的数量减少了35-40 \％，这反过来又可以进一步减少望远镜狩猎Pulsars的Pulsar观察时间。
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.
