Identification of galaxy shreds in large photometric catalogs using Convolutional Neural Networks

TL;DR

This paper demonstrates that convolutional neural networks can effectively identify and remove galaxy fragments from large photometric catalogs, significantly reducing contamination and improving catalog accuracy.

Contribution

The study introduces a CNN-based method to accurately identify galaxy shreds in large surveys, addressing a major contamination issue in photometric catalogs.

Findings

CNN achieves ~98% accuracy in identifying galaxy shreds.

Approximately 5% contamination remains even with strict criteria.

The method is successfully applied to SDSS, DESI, and Pan-STARRS surveys.

Abstract

Contamination from galaxy fragments, identified as sources, is a major issue in large photometric galaxy catalogs. In this paper, we prove that this problem can be easily addressed with computer vision techniques. We use image cutouts to train a convolutional neural network (CNN) to identify catalogued sources that are in reality just star formation regions and/or shreds of larger galaxies. The CNN reaches an accuracy ~98% on our testing datasets. We apply this CNN to galaxy catalogs from three amongst the largest surveys available today: the Sloan Digital Sky Survey (SDSS), the DESI Legacy Imaging Surveys and the Panoramic Survey Telescope and Rapid Response System Survey (Pan-STARSS). We find that, even when strict selection criteria are used, all catalogs still show a ~5% level of contamination from galaxy shreds. Our CNN gives a simple yet effective solution to clean galaxy catalogs from these contaminants.