{
  "abstract": "Aims. Non-parametric morphological statistics can be used for efficient classification of galaxy mergers. This work aims to compare the performance of morphological merger classifiers to state-of-the-art machine learning (ML) models. A secondary aim is to produce updated criteria for mergers based on non-parametric morphological statistics. Methods. The Gini coefficient (G), $M_{20}$ statistic, and concentration ($C$) were calculated for mock Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) images based on the IllustrisTNG and Horizon-AGN simulations, and observations from HSC-SSP. The IllustrisTNG images were used to find the line which best separates mergers and non-mergers in 2D morphological space with a Markov Chain Monte-Carlo (MCMC) method. Results. Based on the MCMC results, we classified galaxies with $G>(-0.267\\pm0.081)M_{20}+(0.143\\pm0.012)$ or $G>(0.162\\pm0.048)C-(0.149\\pm0.12)$ as mergers, these criteria had precisions of 69.5\\% and 72.3\\% respectively when applied to previously unseen IllustrisTNG mock HSC-SSP images. The precisions of the morphological classifications are consistent with state-of-the-art ML methods. The morphological classifiers were found to be effective at selecting only pre-mergers; post-merger galaxies are indistinguishable from non-mergers in terms of their $G$, $M_{20}$, and $C$ values. Morphological classifiers displayed a similar robustness to new data to ML methods up to a redshift of $\\sim0.52$ and maintained robustness better than ML methods based on convolutional neural networks in the redshift range $0.52<z<1$. Conclusions. This work presents updated morphological classifiers which achieve similar precisions to ML based merger classifiers with a high robustness to new data. New morphological statistics are needed to identify the features of post-merger galaxies.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2607.09209",
  "author_total": 6,
  "authors": [
    {
      "name": "Aidan P. Cotter"
    },
    {
      "name": "William J. Pearson"
    },
    {
      "name": "Subhrata Dey"
    },
    {
      "name": "Berta Margalef-Bentabol"
    },
    {
      "name": "Alejandro Guzmán-Ortega"
    },
    {
      "name": "Vicente Rodriguez-Gomez"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2f3fr007an6qp.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2607.09209",
  "fulltext_url": null,
  "id": "2f3fr007an6qp",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2607.09209",
  "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.GA"
    ],
    [
      "openalex",
      "3103|Galaxies: Formation, Evolution, Phenomena"
    ]
  ],
  "openalex": "W7168209439",
  "pdf_url": "https://arxiv.org/pdf/2607.09209",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-18",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/2f3fr007an6qp.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:23:03Z",
      "native_id": "2607.09209",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2607.09209"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.GA",
      "subfield": "galaxies"
    }
  ],
  "title": "Performance of morphological classifiers for galaxy mergers compared to current machine learning methods",
  "title_text": "Performance of morphological classifiers for galaxy mergers compared to current machine learning methods",
  "type": "preprint",
  "updated_at": "2026-10-30T12:23:03Z",
  "url": "https://research.draconitic.com/paper/2f3fr007an6qp",
  "venue": "arXiv",
  "versions": []
}