Gravitational Waves

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

Space-based gravitational wave (GW) detectors will be able to observe signals from sources that are otherwise nearly impossible from current ground-based detection. Consequently, the well established signal detection method, matched filtering, will …

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.

Rapid search for massive black hole binary coalescences using deep learning

The coalescences of massive black hole binaries (MBHBs) are one of the main targets of space-based gravitational wave observatories. Such gravitational wave sources are expected to be accompanied by electromagnetic emission. Low latency time of …

AI For Science 创客松——人工智能驱动的科学研究

AI For Scientist.