normalized flow

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))

Advancing Space-Based Gravitational Wave Astronomy: Rapid Detection and Parameter Estimation Using Normalizing Flows

Gravitational wave (GW) astronomy is witnessing a transformative shift from terrestrial to space-based detection, with missions like Taiji at the forefront. While the transition brings unprecedented opportunities to explore massive black hole …

机器学习在引力波数据处理中的应用

东北大学引力波宇宙学与射电天文学研究中心青年学者研讨会 - 特邀报告

Machine Learning in Gravitational Wave Data Analysis

Poster: https://ictp-ap.org/event/60

Deep neural networks & Gravitational-wave signal recognization

Exploring Gravitational-Wave Detection & Parameter Inference using Deep Learning

Exploring Gravitational-Wave Detection and Parameter Inference using Deep Learning

Sampling with prior knowledge for high-dimensional gravitational wave data analysis

Extracting knowledge from high-dimensional data has been notoriously difficult, primarily due to the so-called "curse of dimensionality" and the complex joint distributions of these dimensions. This is a particularly profound issue for …

Exploring Gravitational-Wave Detection and Parameter Inference using Deep Learning