基于人工智能的引力波数据分析 · Part 2
Part 2 of the 2026 summer-school lectures: convolutional networks and deep-learning methods for gravitational-wave analysis.
CNN
The detection, extraction and parameter estimation of extreme-mass-ratio inspirals with deep learning
Extends neural-network analysis of extreme-mass-ratio inspirals from detection to signal extraction and estimation of intrinsic source properties. The study explores whether learned estimates can narrow the parameter space for subsequent Bayesian …
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
Combines Q-transform representations with a compact convolutional network to detect extreme-mass-ratio inspirals. Tests on simulated space-detector data include time-delay interferometry and examine classification performance over different …
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
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.
Clearing the Path to Discovery: Detecting and Denoising Gravitational Waves with Deep Learning
中国物理学会引力与相对论天体物理分会“2023年学术年会”. Website: http://cqutp.org/conferences/gr23/index.php
基于人工智能技术的引力波数据分析前沿
国家天文数据中心·天文信息学与虚拟天文台 2022 年学术年会. Website: https://nadc.china-vo.org/events/cvo2022/