ViT-based Local Volume dwarf galaxy Identificationin (VIDA) in the CSST survey

TL;DR

This paper presents a machine learning pipeline using ViT models to detect and classify dwarf galaxies in the Local Volume from simulated CSST imaging data, achieving high accuracy and deeper detection limits than traditional methods.

Contribution

The study introduces a novel ViT-based classification pipeline combined with traditional detection techniques for dwarf galaxy identification in the CSST survey.

Findings

Achieves over 85% true positive rate with 0.1% false positive rate.

Detects dwarf galaxies down to $M_V = -7$ within 10 Mpc.

Can identify dwarf galaxies within 20 Mpc using the proposed framework.

Abstract

Identifying dwarf galaxies within the Local Volume is crucial for constraining the luminosity function of satellite galaxies in the nearby universe. We report the detection capabilities of dwarf galaxies within the Local Volume using the Chinese Space Station Telescope (CSST). Based on the simulated imaging data of CSST, we develop a detection and classification pipeline that combines traditional image-based search techniques with advanced machine learning classification models. The simulated Local Volume dwarf galaxies can be identified using a pre-processing method for "extended source detection", followed by classification with a pretrained ViT-Base model. This pipeline achieves a true positive rate (TPR) exceeding 85% with a false positive rate (FPR) of only 0.1%. We quantify the detection completeness of Local Volume dwarf galaxies across a three-dimensional parameter space defined by absolute magnitude ($M_V$), half-light radius ($R_h$), and heliocentric distance, based on simulated single-exposure CSST wide-field imaging survey data. For unresolved or semi-resolved dwarf galaxies, our method achieves a significantly deeper absolute magnitude detection limit compared to catalog-based approaches, reaching $M_V = -7$ within 10 \Mpc. By combining this image-based approach with traditional stellar catalog-based "matched filter" techniques, our automated framework established in this work can identify dwarf galaxies within 20 \Mpc for the CSST mission.