BRAHMa: Bar Recognition And Hatching using Machine learning -- a framework for oriented bounding box detection of galactic bars

astro-ph.GA

Submitted: 2026-10-05

Updated: 2026-10-07

Comments: This version is being withdrawn because errors were identified in the analysis that require substantial correction and revision. The authors will address these issues in a revised version of the manuscript

Code: https://github.com/ultralytics/ultralytics

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Sources

• The DESI Experiment Part I: Science,Targeting, and Survey Design
• YOLOv11: An Overview of the Key Architectural Enhancements
• Identifying bars in galaxies using machine learning
• SmoothGrad: removing noise by adding noise
• Deep Learning Segmentation of Spiral Arms and Bars
• mixup: Beyond Empirical Risk Minimization

Related papers

• Apparent Stability in Self-Gravitating Turbulence and the Evolution of Molecular Clouds
• Two sets of potential-density basis pairs for the study of radial perturbations in collisionless spherical stellar systems
• Constraining reionization-era Ly alpha escape with JELS-MUSE: a highly complete H alpha-selected sample at z about6.1
• Deriving volume density profiles of filaments from observed surface densities
• Little Red Dots and Supermassive Black Hole Seed Formation in Ultralight Dark Matter Halos
• MEGATRON: how the first stars can create an iron metallicity plateau in the smallest dwarf galaxies