Learning Robust Models for Multi-Source Gravitational-Wave Analysis
A workshop talk at NUS on learning the aggregate Galactic foreground for noise calibration and multi-source inference.
inference
迈向智能化引力波天文学：机器学习、生成模型与大语言模型
A seminar at Beijing Normal University connecting machine learning, generative modeling, and LLMs with gravitational-wave astronomy.
基于人工智能的引力波数据分析
A spring-school lecture on machine learning and simulation-based inference for gravitational-wave parameter estimation.
Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis
Reviews simulation-based inference for gravitational-wave astronomy, including neural posterior, likelihood, and ratio estimation, flow matching, and consistency models. Applications range from individual sources to population studies, with attention …
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 …
Rapid Parameter Estimation for Extreme Mass Ratio Inspirals Using Machine Learning
Explores flow-matching posterior estimation for extreme-mass-ratio inspirals with a high-dimensional source parameter space. The study tests continuous normalizing flows as a faster route toward candidate posterior distributions for these long and …
Advancing space-based gravitational wave astronomy: Rapid parameter estimation via normalizing flows
Uses normalizing flows to estimate the parameters of massive black hole binaries in the Taiji detector configuration. The work studies rapid posterior generation and its potential to complement conventional Bayesian analysis of space-based …
Gravitational Wave Detection and AI Technology: New Methods for Unveiling the Mysteries of the Universe
International Workshop on Intelligent Computing in Astronomy 'Computing Senses Cosmos' (2023) @Zhejiang Lab, Hangzhou, China https://en.zhejianglab.com/newsevents/AI4Astro/
空间引力波科学数据处理的挑战与人工智能技术应用
2023年“第三届空间科学大会”空间引力波探测和精密测量与引力宇宙 | 浙江·德清 https://cssr.kejie.org.cn/meeting/cssa2023/
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/