A probabilistic approach to separate mergers and jellyfish galaxies: Application of morphological non-parametric indexes to the DESI Legacy Survey
Abstract
Galaxy morphology traces the physical processes that shape galaxy evolution, but non-parametric diagnostic boundaries depend on image quality and preprocessing. We investigate the classification of regular spirals, regular ellipticals, mergers, and jellyfish (ram-pressure-stripping candidate) galaxies in the DESI Legacy Imaging Surveys. Using galmex measurements of ten non-parametric morphological indices, we evaluate two-dimensional diagnostic planes with a cross-validated Bayesian kernel-density classifier and combine all indices using probability-calibrated LightGBM models. For separating regular spirals, regular ellipticals, and perturbed systems, the most informative planes combine Shannon entropy with M20 or asymmetry indices, reaching balanced accuracies of 0.76-0.77. We provide survey-specific probabilistic boundaries in the entropy-M20 and entropy-shape-asymmetry planes. Separating mergers from jellyfish candidates is more challenging; the best two-dimensional combinations, all involving concentration, reach balanced accuracies of approximately 0.73. The calibrated three-class LightGBM model achieves a balanced accuracy and macro-F1 score of 0.89, while the four-class model achieves 0.75 and 0.76, respectively. We provide class probabilities and preferred classifications for 1,744,454 galaxies. Our results show that non-parametric morphology identifies broad structural disturbance reliably, but distinguishing its physical origin is harder: jellyfish candidates occupy a relatively coherent region of parameter space, whereas mergers span diverse and overlapping morphologies.
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BibTeX
@misc{sampaio2026probabilistic,
title = {{A probabilistic approach to separate mergers and jellyfish galaxies: Application of morphological non-parametric indexes to the DESI Legacy Survey}},
author = {V. M. Sampaio and Y. Jaff'e and A. Moretti and C. Lima-Dias and H. M'endez-Hern'andez and S. V'eliz Astudillo and A. Monachesi and S. Torres-Flores and A. Krabbe and C. Dulcien},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.32960},
eprint = {2610.32960},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.32960},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - A probabilistic approach to separate mergers and jellyfish galaxies: Application of morphological non-parametric indexes to the DESI Legacy Survey
AU - V. M. Sampaio
AU - Y. Jaff'e
AU - A. Moretti
AU - C. Lima-Dias
AU - H. M'endez-Hern'andez
AU - S. V'eliz Astudillo
AU - A. Monachesi
AU - S. Torres-Flores
AU - A. Krabbe
AU - C. Dulcien
PY - 2026
DA - 2026/10/23
PB - arXiv
DO - 10.48550/arxiv.2610.32960
UR - https://arxiv.org/abs/2610.32960
AB - Galaxy morphology traces the physical processes that shape galaxy evolution, but non-parametric diagnostic boundaries depend on image quality and preprocessing. We investigate the classification of regular spirals, regular ellipticals, mergers, and jellyfish (ram-pressure-stripping candidate) galaxies in the DESI Legacy Imaging Surveys. Using galmex measurements of ten non-parametric morphological indices, we evaluate two-dimensional diagnostic planes with a cross-validated Bayesian kernel-density classifier and combine all indices using probability-calibrated LightGBM models. For separating regular spirals, regular ellipticals, and perturbed systems, the most informative planes combine Shannon entropy with M_20 or asymmetry indices, reaching balanced accuracies of 0.76-0.77. We provide survey-specific probabilistic boundaries in the entropy-M_20 and entropy-shape-asymmetry planes. Separating mergers from jellyfish candidates is more challenging; the best two-dimensional combinations, all involving concentration, reach balanced accuracies of approximately 0.73. The calibrated three-class LightGBM model achieves a balanced accuracy and macro-F1 score of 0.89, while the four-class model achieves 0.75 and 0.76, respectively. We provide class probabilities and preferred classifications for 1,744,454 galaxies. Our results show that non-parametric morphology identifies broad structural disturbance reliably, but distinguishing its physical origin is harder: jellyfish candidates occupy a relatively coherent region of parameter space, whereas mergers span diverse and overlapping morphologies.
ER -