{
  "abstract": "The detection of gravitational waves (GWs) from compact binary coalescences (CBCs) using matched filtering is computationally demanding because detector data must be correlated with many template waveforms spanning a high-dimensional intrinsic parameter space. In our previous work, we showed that a convolutional neural network (CNN) can classify noisy signals against pure noise and constrain the intrinsic parameter space of a true non-spinning binary black hole (BBH) signal, enabling a narrower matched-filter search region and reducing computational cost. Here, we extend this framework to aligned-spin BBH systems and investigate how different parameter-space representations affect CNN-based patch identification. Using IMRPhenomD waveforms over the aligned-spin BBH parameter space and Advanced LIGO design sensitivity, we show that a CNN trained solely on the aligned-spin template bank achieves more than 99.8% signal-noise classification accuracy on independently generated uniformly sampled BBH signals, indicating that additional uniformly sampled training data are unnecessary. We partition the template bank into four approximately balanced patches using a Principal Component Analysis (PCA)-based quantile scheme and evaluate five parameter-space representations for patch identification. The chirp mass-duration representation achieves the highest average accuracy (93.2%), followed by chirp mass (91.6%) and component masses (90.5%). The post-Newtonian coordinates tau_0-tau_3 and theta_0-theta_3-theta_3s yield substantially lower accuracies. These results show that the existing template bank is sufficient for training and high-accuracy signal detection, while the choice of parameter-space representation is critical for constraining the parameters of the true signal.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.28031",
  "author_total": 5,
  "authors": [
    {
      "name": "Chetan Verma"
    },
    {
      "name": "Amit Reza"
    },
    {
      "name": "Gurudatt Gaur"
    },
    {
      "name": "Dilip Krishnaswamy"
    },
    {
      "name": "Sarah Caudill"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3k6pnp6pfdp24.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.28031",
  "fulltext_url": null,
  "id": "3k6pnp6pfdp24",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.28031",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "astro-ph.IM"
    ],
    [
      "arxiv",
      "gr-qc"
    ],
    [
      "openalex",
      "3103|Pulsars and Gravitational Waves Research"
    ]
  ],
  "openalex": "W7214210952",
  "pdf_url": "https://arxiv.org/pdf/2609.28031",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-23",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/3k6pnp6pfdp24.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:23:56Z",
      "native_id": "2609.28031",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.28031"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.IM",
      "subfield": "instrumentation-methods"
    },
    {
      "field": "physics",
      "origin": "arxiv:gr-qc",
      "subfield": "gravitation"
    }
  ],
  "title": "Neural Network Guided Parameter Space Constraints for Gravitational Wave Searches from Binary Black Holes",
  "title_text": "Neural Network Guided Parameter Space Constraints for Gravitational Wave Searches from Binary Black Holes",
  "type": "preprint",
  "updated_at": "2026-10-30T12:23:56Z",
  "url": "https://research.draconitic.com/paper/3k6pnp6pfdp24",
  "venue": "arXiv",
  "versions": []
}