Detection of Bars in Galaxies using a Deep Convolutional Neural Network

TL;DR

This paper introduces a deep learning method for automatically detecting bars in galaxy images, achieving high accuracy comparable to human experts, and providing a scalable solution for large astronomical datasets.

Contribution

The study develops a deep convolutional neural network that accurately identifies barred galaxies, creating a large, publicly available catalogue and demonstrating scalability for big data in astronomy.

Findings

Achieved ~94% precision in bar detection.

Matched human expert accuracy without additional image info.

Created an online catalogue of barred galaxies.

Abstract

We present an automated method for the detection of bar structure in optical images of galaxies using a deep convolutional neural network which is easy to use and provides good accuracy. In our study we use a sample of 9346 galaxies in the redshift range 0.009-0.2 from the Sloan Digital Sky Survey, which has 3864 barred galaxies, the rest being unbarred. We reach a top precision of ~94 per cent in identifying bars in galaxies using the trained network. This accuracy matches the accuracy reached by human experts on the same data without additional information about the images. Since Deep Convolutional Neural Networks can be scaled to handle large volumes of data, the method is expected to have great relevance in an era where astronomy data is rapidly increasing in terms of volume, variety, volatility and velocity along with other V's that characterize big data. With the trained model we have constructed a catalogue of barred galaxies from SDSS and made it available online.