Gravitational-wave Inference

The Detection, Extraction and Parameter Estimation of Extreme-Mass-Ratio Inspirals with Deep Learning

One of the primary goals of space-borne gravitational wave detectors is to detect and analyze extreme-mass-ratio inspirals (EMRIs). This endeavor presents a significant challenge due to the complex and lengthy EMRI signals, further compounded by …

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))