normalized flow

Rapid Parameter Estimation for Extreme Mass Ratio Inspirals Using Machine Learning

Extreme-mass-ratio inspiral (EMRI) signals pose significant challenges in gravitational wave (GW) astronomy owing to their low-frequency nature and highly complex waveforms, which occupy a high-dimensional parameter space with numerous variables. …

Challenges in space-based gravitational wave data analysis and applications of artificial intelligence

As space-based gravitational wave detection projects such as LISA, Taiji, and Tianqin continue to advance, we are on the cusp of gaining a new viewpoint on observing the universe.However, the scientific data processing for these projects faces …

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 for exploring massive black hole …

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))

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 …