Automated Algorithmic Discovery for Gravitational-Wave Detection Guided by LLM-Informed Evolutionary Monte Carlo Tree Search

• URL: http://arxiv.org/abs/2508.03661v1
• Date: Tue, 05 Aug 2025 17:18:20 GMT
• Title: Automated Algorithmic Discovery for Gravitational-Wave Detection Guided by LLM-Informed Evolutionary Monte Carlo Tree Search
• Authors: He Wang, Liang Zeng,
• Abstract summary: Evo-MCTS is a framework that combines tree-structured search with evolutionary optimization and large language models to create interpretable algorithmic solutions.<n>Our framework achieves a 20.2% improvement over state-of-the-art gravitational wave detection algorithms on the MLG-1WSC benchmark dataset.
• Score: 10.617016967920863
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Computational scientific discovery increasingly relies on algorithms to process complex data and identify meaningful patterns - yet faces persistent challenges in gravitational-wave signal identification. While existing algorithmic approaches like matched filtering (MF) and deep neural networks (DNNs) have achieved partial success, their limitations directly stem from fundamental limitations: MF's excessive computational demands arise from its reliance on predefined theoretical waveform templates, while DNNs' black-box architectures obscure decision logic and introduce hidden biases. We propose Evolutionary Monte Carlo Tree Search (Evo-MCTS), a framework that addresses these limitations through systematic algorithm space exploration guided by domain-aware physical constraints. Our approach combines tree-structured search with evolutionary optimization and large language model heuristics to create interpretable algorithmic solutions. Our Evo-MCTS framework demonstrates substantial improvements, achieving a 20.2\% improvement over state-of-the-art gravitational wave detection algorithms on the MLGWSC-1 benchmark dataset. High-performing algorithm variants consistently exceed thresholds. The framework generates human-interpretable algorithmic pathways that reveal distinct performance patterns. Beyond performance improvements, our framework discovers novel algorithmic combinations, thereby establishing a transferable methodology for automated algorithmic discovery across computational science domains.

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