Automatic detection of peculiar galaxy pairs in Sloan Digital Sky Survey

TL;DR

This paper presents an automated method using machine learning and novelty detection to identify peculiar galaxy pairs in SDSS data, successfully finding unusual mergers among millions of galaxy images.

Contribution

It introduces a novel pipeline combining morphology detection and novelty algorithms to efficiently find peculiar galaxy mergers in large astronomical datasets.

Findings

Detected ~26,000 galaxy images with merger-like morphology.

Produced a shortlist of 500 most peculiar galaxies.

Manual review confirmed many unusual galaxy pairs.

Abstract

We applied computational tools for automatic detection of peculiar galaxy pairs. We first detected in SDSS DR7 ~400,000 galaxy images with i magnitude <18 that had more than one point spread function, and then applied a machine learning algorithm that detected ~26,000 galaxy images that had morphology similar to the morphology of galaxy mergers. That dataset was mined using a novelty detection algorithm, producing a short list of 500 most peculiar galaxies as quantitatively determined by the algorithm. Manual examination of these galaxies showed that while most of the galaxy pairs in the list were not necessarily peculiar, numerous unusual galaxy pairs were detected. In this paper we describe the protocol and computational tools used for the detection of peculiar mergers, and provide examples of peculiar galaxy pairs that were detected.