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.
A review of modern simulation-based inference techniques for gravitational wave data analysis, highlighting methodological advances, practical applications, and future outlook.
🌊 AI-powered gravitational wave detection system using Model Context Protocol (MCP) for efficient parameter space exploration. Features optimization client, analysis server, and automated parameter exploration for LIGO GW150914 data with LLM-agent validation.
Comprehensive review of data analysis challenges in space-based gravitational wave detection (LISA, Taiji, TianQin) and transformative applications of artificial intelligence in addressing these challenges.
Gravitational Wave Data Exploration: A Practical Training in Programming and Analysis
Demo script (Python) of particle swarm optimization (PSO) partly translated from [SDMBIGDAT19](https://github.com/mohanty-sd/SDMBIGDAT19) (MATLAB).
模型选择 (model selection) 是统计推断专题里一个很重要的话题。此文是于 2021.1.19 在 ITP-CAS 为 Journal Club 准备的一个整理与调研。算是自己对 model selection 在个人在当前理解程度上的一个记录。
All I need to know on "*data analysis in gravitational-wave science*".
Pioneer exploration of deep learning applications in gravitational wave data analysis, addressing challenges in signal detection, computational efficiency, and discovery of unexpected signals beyond theoretical templates.