3

G-LNS: Generative Large Neighborhood Search for LLM-Based Automatic Heuristic Design

Revolutionary framework using LLMs to automatically design Large Neighborhood Search operators through synergy-aware co-evolution. Achieves near-optimal solutions on TSP and CVRP benchmarks with strong generalization.

Automated Algorithmic Discovery for Scientific Computing through LLM-Guided Evolutionary Search: A Case Study in Gravitational-Wave Detection

A framework for automated discovery of gravitational-wave detection algorithms, combining LLM guidance with Evolutionary Monte Carlo Tree Search, enabling efficient and creative pipeline discovery.

Search for exotic gravitational wave signals beyond general relativity using deep learning

The direct detection of gravitational waves by LIGO has confirmed general relativity (GR) and sparked rapid growth in gravitational wave (GW) astronomy. However, subtle post-Newtonian (PN) deviations observed during the analysis of high …

Gravitational Wave Signal Denoising and Merger Time Prediction By Deep Neural Network

The mergers of massive black hole binaries could generate rich electromagnetic emissions, which allow us to probe the environments surrounding these massive black holes and gain deeper insights into the high energy astrophysics. However, due to the …

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. …

Probing the gravitational wave background from cosmic strings with Alternative LISA-TAIJI network

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.

Space-based gravitational wave signal detection and extraction with deep neural network

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.

Rapid search for massive black hole binary coalescences using deep learning

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 machine learning gravitational-wave search mock data challenge

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.