论文标题
深度学习用于估计引力波参数
Deep Learning for estimating parameters of Gravitational Waves
论文作者
论文摘要
近年来，对引力波（GW）天文学的深度学习（DL）技术的改进导致了已成功采用的各种分类算法的发展显着上升，这些算法已成功地从噪声的时间序列数据中提取了二进制黑洞合并事件的GW。但是，这些模型的成功受到时间样本的长度和GW来源的类别的限制：在某种程度上，二进制黑洞和中子星二进制。在这项工作中，我们打算使用卷积神经网络提高DL技术的界限，超越二进制分类并预测事件的物理参数。我们旨在提出一种可用于实时检测和参数预测的替代方法。我们提供的DL模型已在12s数据上进行了训练，以预测GW源参数，如果检测到。在训练过程中，获得的最高准确度为90.93％，验证精度为89.97％。
In recent years, improvements in Deep Learning (DL) techniques towards Gravitational Wave (GW) astronomy have led to a significant rise in the development of various classification algorithms that have been successfully employed to extract GWs of binary blackhole merger events from noisy time-series data. However, the success of these models is constrained by the length of time-sample and the class of GW source: binary blackhole and neutron star binaries to some extent. In this work, we intended to advance the boundaries of DL techniques using Convolutional Neural Networks, to go beyond binary classification and predict the physical parameters of the events. We aim to propose an alternative method that can be employed for realtime detection and parameter prediction. The DL model we present has been trained on 12s of data to predict the GW source parameters if detected. During training, the maximum accuracy attained was 90.93%, with a validation accuracy of 89.97%.
