Star-galaxy Classification Using Deep Convolutional Neural Networks

TL;DR

This paper demonstrates that deep convolutional neural networks can automatically classify stars and galaxies directly from pixel data, achieving accuracy comparable to traditional methods and reducing manual feature engineering.

Contribution

The authors introduce a ConvNet-based framework for star-galaxy classification using raw pixel data, showing its effectiveness on SDSS and CFHTLenS datasets.

Findings

ConvNets achieve accurate probabilistic classifications.

The method reduces the need for manual feature extraction.

Results are competitive with conventional machine learning techniques.

Abstract

Most existing star-galaxy classifiers use the reduced summary information from catalogs, requiring careful feature extraction and selection. The latest advances in machine learning that use deep convolutional neural networks allow a machine to automatically learn the features directly from data, minimizing the need for input from human experts. We present a star-galaxy classification framework that uses deep convolutional neural networks (ConvNets) directly on the reduced, calibrated pixel values. Using data from the Sloan Digital Sky Survey (SDSS) and the Canada-France-Hawaii Telescope Lensing Survey (CFHTLenS), we demonstrate that ConvNets are able to produce accurate and well-calibrated probabilistic classifications that are competitive with conventional machine learning techniques. Future advances in deep learning may bring more success with current and forthcoming photometric surveys, such as the Dark Energy Survey (DES) and the Large Synoptic Survey Telescope (LSST), because deep neural networks require very little, manual feature engineering.