{
  "abstract": "Deep learning has proven to be a vital advancement in many fields that present difficult classification and prediction tasks. This includes astrophysics, particularly space weather prediction, where accurate modeling has become essential for protecting satellite and ground infrastructure from the impacts of solar flares and solar energetic particle events. Despite their strong predictive ability, one issue with deep learning as a method for space weather prediction is the poor interpretability of these “black box” methods. A possible solution to this comes in the form of a structured deep learning framework named Amortized Causal Discovery (ACD), which aims to enforce an easily interpretable layer in the neural network that represents the causal summary graph. We apply this method to the Space Weather Analytics for Solar Flares dataset, a rich source of multivariate time series (MVTS) data of solar active regions, to demonstrate that high-intensity solar flares can be detected ahead of time by comparing their causal summary graphs with those of other flare events. In this work, we use graph metric statistics as well as objective performance tests against state-of-the-art classifiers to demonstrate the depth of insight that ACD can produce into differences between MVTS samples in the context of solar flare prediction.",
  "abstract_shown": true,
  "affiliations": [
    "Utah State University"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Nathan Nelson",
      "orcid": "0009-0000-7712-8959"
    },
    {
      "affs": [
        0
      ],
      "name": "Onur Vural",
      "orcid": "0009-0004-4950-7520"
    },
    {
      "affs": [
        0
      ],
      "name": "MohammadReza EskandariNasab",
      "orcid": "0009-0004-0697-3716"
    },
    {
      "affs": [
        0
      ],
      "name": "Shah Muhammad Hamdi",
      "orcid": "0000-0002-9303-7835"
    },
    {
      "affs": [
        0
      ],
      "name": "Soukaina Filali Boubrahimi",
      "orcid": "0000-0001-5693-6383"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/08j80z8ea8249.bib",
  "cited_by": 0,
  "doi": "10.3847/1538-4365/aea84c",
  "fulltext_url": null,
  "id": "08j80z8ea8249",
  "issns": [
    "0067-0049",
    "1538-4365"
  ],
  "landing_url": "https://iopscience.iop.org/article/10.3847/1538-4365/aea84c",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "3103|Solar and Space Plasma Dynamics"
    ]
  ],
  "openalex": "W7203674892",
  "pdf_url": "https://iopscience.iop.org/article/10.3847/1538-4365/aea84c/pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-22",
  "publisher": "American Astronomical Society",
  "ris": "https://research.draconitic.com/paper/08j80z8ea8249.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:15Z",
      "native_id": "10.3847/1538-4365/aea84c",
      "source": "crossref",
      "url": "https://iopscience.iop.org/article/10.3847/1538-4365/aea84c"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "openalex:3103|Solar and Space Plasma Dynamics",
      "subfield": "stellar"
    }
  ],
  "title": "Causal Discovery in Photospheric Magnetic Field Time Series for Interpretable Solar Flare Prediction",
  "title_text": "Causal Discovery in Photospheric Magnetic Field Time Series for Interpretable Solar Flare Prediction",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:15Z",
  "url": "https://research.draconitic.com/paper/08j80z8ea8249",
  "venue": "The Astrophysical Journal Supplement Series",
  "versions": []
}