{
  "abstract": "We present a deep learning detection method capable of effectively identifying planetary nebulae (PNe) on IPHAS and VPHAS+ surveys. Our detection pipeline is based on the YOLO architecture and is adaptable to other datasets. Using the HiPS system, we preprocess and augment the $r$, $i$, and $Hα$ bands to produce two red, green, and blue (RGB) image datasets: (1) $640 \\times 640$ px images at full resolution, and (2) $1 280 \\times 1 280$ px images designed to capture extended objects up to $0.2$ deg. Images of $\\sim 2600$ PNe are labelled using the HASH database. The loss function and evaluation metrics are adapted to the diffuse and low-contrast nature of PNe; a very low intersection over union (IoU) threshold ($1\\%$) is used. A $k$-fold cross validation shows that the YOLOv12m model achieves an average precision of $0.979$ on the first dataset. At a confidence threshold of $0.15$, it achieves a precision rate of $98.7\\%$ and a recall rate of $97.4\\%$. We deployed our system (on $1280 \\times 1280$ images) on the full surveys' coverage ($\\sim 3755$ deg$^2$), and present a preliminary analysis of the detection results. The system is robust against artifacts such as CCD corner defects and HiPS tile joints. Our convolutional-neural-network-based model achieved excellent results on the test set, comparable to-or exceeding-that of a recent Swin Transformer approach. However, a challenge persists when applying deep learning models to extensive and densely populated Galactic fields: the development of an automated system that can effectively discriminate PNe from mimics that human experts are able to distinguish (e.g., H II regions). We outline possible improvements for future work.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "Journal reference: Titouan Sérandour et al 2026 AJ 172 238",
  "arxiv": "2610.19760",
  "author_total": 8,
  "authors": [
    {
      "name": "Titouan Sérandour"
    },
    {
      "name": "Anne-Laure Méalier"
    },
    {
      "name": "Bruno Colicchio"
    },
    {
      "name": "Dominique Daniel"
    },
    {
      "name": "Philippe Gilberton"
    },
    {
      "name": "Philippe Oros"
    },
    {
      "name": "Thomas Boch"
    },
    {
      "name": "Djaffar Ould Abdeslam"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1m8096mpss42c.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.19760",
  "fulltext_url": null,
  "id": "1m8096mpss42c",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.19760",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "astro-ph.GA"
    ],
    [
      "arxiv",
      "astro-ph.IM"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2610.19760",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-14",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/1m8096mpss42c.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:31Z",
      "native_id": "2610.19760",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.19760"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.GA",
      "subfield": "galaxies"
    },
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.IM",
      "subfield": "instrumentation-methods"
    }
  ],
  "title": "Identifying Planetary Nebulae Candidates from IPHAS and VPHAS+ Surveys via YOLO Deep Learning Models",
  "title_text": "Identifying Planetary Nebulae Candidates from IPHAS and VPHAS+ Surveys via YOLO Deep Learning Models",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:31Z",
  "url": "https://research.draconitic.com/paper/1m8096mpss42c",
  "venue": "arXiv",
  "versions": [
    {
      "doi": "10.3847/1538-3881/aea417",
      "pid": "2j30hx48mwy54",
      "relation": "published-as",
      "title": "Identifying Planetary Nebulae Candidates from IPHAS and VPHAS+ Surveys via YOLO Deep Learning Models",
      "venue": "The Astronomical Journal"
    }
  ]
}