AI

引力波探测与AI技术:揭示宇宙奥秘的新手段

2023年“第二届机器学习在天文学中的应用”研讨会. https://machinelearning2023.casconf.cn/

Detecting Extreme-Mass-Ratio Inspirals for Space-Borne Detectors with Deep Learning

Deep learning approach for EMRI detection achieving 94.2% TPR at 1% FPR, demonstrating the potential for efficient signal detection in space-based gravitational wave detectors.

Parameter Inference for Coalescing Massive Black Hole Binaries Using Deep Learning

(This article belongs to the [Special Issue Newest Results in Gravitational Waves and Machine Learning](https://www.mdpi.com/journal/universe/special_issues/48U1E55JLC))

GW Data Analysis & Deep Learning III

引力波暑期学校 Summer School on Gravitational Waves. Website

GW Data Analysis & Deep Learning II

引力波暑期学校 Summer School on Gravitational Waves. Website

GW Data Analysis & Machine Learning I

引力波暑期学校 Summer School on Gravitational Waves. Website

Intelligent Noise Suppression for GW Observational Data

2023 中国物理学会秋季学术会议 | 中国·银川. Website: http://meeting.cps-net.org.cn/nxu2023/

Space-based gravitational wave signal detection and extraction with deep neural network

Science-driven multi-stage deep neural network for space-based gravitational wave detection and extraction achieving >99% accuracy and ≥95% signal similarity, with strong generalization and interpretability demonstrated on synthetic LISA data.

Gravitational Wave Detection and AI Technology: New Methods for Unveiling the Mysteries of the Universe

量子宇宙理论物理研究中心研讨会.

机器学习在引力波数据分析中的应用——参数估计及数据降噪

引力波数据分析系列报告.