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This chapter presents recent advancements in the application of Sparse Dictionary Learning (SDL) to gravitational wave (GW) signal processing, specifically in denoising, glitch removal and signal classification. We outline the mathematical framework of SDL and its role in improving GW data analysis by efficiently representing signals with sparse dictionaries. The method’s effectiveness is demonstrated in several use cases, including reducing noise in signals from core-collapse supernovae and binary black hole mergers, as well as mitigating transient noise (e.g., blip glitches) in LIGO data. Additionally, we explore the use of Low-Rank Shared Dictionary Learning for classifying GW signals with high morphological similarity, particularly those from different supernova explosion mechanisms. The results underscore the potential of SDL for refining signal recovery and classification, offering new possibilities for enhancing the precision and reliability of GW detections.

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Sparse Dictionary Learning for Gravitational-Wave Signal Denoising, Reconstruction and Classification

  • 章节

  • 发表日期：

    2025年

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  • Miquel Llorens-Monteagudo,
  • Alejandro Torres-Forné,
  • José A. Font,
  • Antonio Marquina

摘要

This chapter presents recent advancements in the application of Sparse Dictionary Learning (SDL) to gravitational wave (GW) signal processing, specifically in denoising, glitch removal and signal classification. We outline the mathematical framework of SDL and its role in improving GW data analysis by efficiently representing signals with sparse dictionaries. The method’s effectiveness is demonstrated in several use cases, including reducing noise in signals from core-collapse supernovae and binary black hole mergers, as well as mitigating transient noise (e.g., blip glitches) in LIGO data. Additionally, we explore the use of Low-Rank Shared Dictionary Learning for classifying GW signals with high morphological similarity, particularly those from different supernova explosion mechanisms. The results underscore the potential of SDL for refining signal recovery and classification, offering new possibilities for enhancing the precision and reliability of GW detections.