Galaxies OBserved as Low-luminosity Identified Nebulae (GOBLIN): a catalog of 43,000 high-probability dwarf galaxy candidates in the UNIONS survey

TL;DR

This paper introduces GOBLIN, a comprehensive catalog of 43,000 high-probability dwarf galaxy candidates detected in the UNIONS survey, utilizing a novel detection pipeline and deep learning classification to advance understanding of low surface brightness galaxies.

Contribution

It presents a systematic detection framework and a large catalog of dwarf galaxy candidates, combining image processing, low surface brightness detection, and deep learning classification, which is a significant expansion over previous surveys.

Findings

Identified 42,965 dwarf candidates with probability > 0.8.

Catalog includes structural parameters and classification probabilities.

High-probability candidates are spatially correlated with massive galaxies.

Abstract

The detection of low surface brightness galaxies beyond the Local Group poses significant observational challenges, yet these faint systems are fundamental to our understanding of dark matter, hierarchical galaxy formation, and cosmic structure. Their abundance and distribution provide crucial tests for cosmological models, particularly regarding the small-scale predictions of $\Lambda$CDM. We present a systematic detection framework for dwarf galaxy candidates in Ultraviolet Near Infrared Optical Northern Survey (UNIONS) data covering 4,861 deg$^{2}$. Our pipeline preprocesses UNIONS gri-band data through binning, artifact removal, and stellar masking, then employs MTObjects (MTO) for low surface brightness detection. After parameter cuts and cross-matching, we obtain $\sim$360 candidates per deg$^{2}$, totaling $\sim$1.5 million candidates forming our GOBLIN (Galaxies OBserved as Low-luminosity Identified Nebulae) catalog. We fine-tuned the deep learning model Zoobot, pre-trained on Galaxy Zoo labels, for classification. Training data came from visual inspection of literature candidates with probability labels from expert assessments, capturing consensus and uncertainty. Applied to all MTO objects, our method identifies 42,965 dwarf candidates with probability $>$ 0.8, including 23,072 with probability $>$ 0.9. High-probability candidates correlate spatially with massive galaxies (log$(M_{*}/M_{\odot}) \geq$ 10) within 120 Mpc. While some of these objects may have been previously identified in other surveys, we present this extensive catalog of candidates, including their positions, structural parameter estimates, and classification probabilities, as a resource for the community to enable studies of galaxy formation, evolution, and the distribution of dwarf galaxies in different environments.