Sapce-borne gravitational wave antennas, such as LISA and LISA-like mission (Taiji and Tianqin), will offer novel perspectives for exploring our Universe while introduce new challenges, especially in data analysis. Aside from the known challenges …
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 …
Analysis of LISA-TAIJI network configurations for detecting stochastic gravitational wave background from cosmic strings, finding LISA-TAIJIc offers optimal sensitivity for constraining string tension at G𝜇~10^-17.
Deep learning approach for EMRI detection achieving 94.2% TPR at 1% FPR, demonstrating the potential for efficient signal detection in space-based gravitational wave detectors.
(This article belongs to the [Special Issue Newest Results in Gravitational Waves and Machine Learning](https://www.mdpi.com/journal/universe/special_issues/48U1E55JLC))
Science-driven multi-stage deep neural network for space-based gravitational wave detection and extraction achieving >99% accuracy and ≥95% signal similarity, with strong generalization and interpretability demonstrated on synthetic LISA data.
Deep learning method for rapid detection of massive black hole binary coalescences in LISA data, achieving high sensitivity with no false alarms while processing 1-year data in seconds.
First community-wide machine learning gravitational wave search challenge with 6 algorithms tested on progressively realistic data including real O3a noise. Top ML methods achieve 95% of matched filtering sensitivity for Gaussian noise and 70% for real noise.
Ensemble deep learning model combining multiple CNNs successfully detects all O1/O2 BBH events (except GW170818) with zero false alarms on one month of O2 data, demonstrating real-time GW analysis capability.
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 …