Automated Classification of Galaxy Images INSTITUTO NACIONAL DE ASTROFÍSICA, ÓPTICA Y ELECTRÓNICA Automated Classification of Galaxy Images Jorge de la Calleja and Olac Fuentes Instituto Nacional de Astrofísica Óptica y Electrónica Tonantzintla, Puebla, Mexico

Introduction A galaxy contains millions of stars. There are several billion galaxies in the universe. Galaxy classification can provide important clues about the origin and evolution of the universe. Traditionally, galaxy classification is perfomed by visual inspection of images by expert astronomers Current Sky Surveys make manual classification unfeasible.

Introduction

Hubble Classification Scheme Introduction Hubble Classification Scheme

Hubble Classification Scheme: Three Main Classes Introduction Hubble Classification Scheme: Three Main Classes Spiral Galaxies Eliptical Galaxies Irregular Galaxies Classes can in turn be divided into subclasses.

Introduction Previous Work: Semi-automated Schemes: Problems: Combine manually-extracted features with features obtained using image-analysis techniques Use these features to train a neural network. Problems: Need for human intervention Feature extraction is not robust Accuracy much lower than that obtained by humans.

Completely Automated Classification Proposed Method Completely Automated Classification

Proposed Method Image Standardization: Rotation, Centering, Cropping and Resizing Data Compression: Principal Component Analysis (PCA) Machine Learning: Naive Bayes, C4.5 and Random Forest

Proposed Method Image Standardization Rotation Extract binary image from original image using a threshold Find main axis of image using PCA Rotate image so that the main axis is horizontal Centering Translate galaxy to center of image Cropping Eliminate rows and/or columns that don’t contain pixels that belong to the galaxy image Resizing Convert image to standard (128X128) size

Proposed Method Image Standardization

Proposed Method Image Standardization Examples:

Proposed Method Data Compression From set of images perform PCA to obtain “eigengalaxies” (analogous to eigenfaces [Turk and Pentland, 91]). Project images onto the first few eigengalaxies. Use projection as attributes for the Machine Learning algorithm.

Proposed Method Data Compression Original Images Reconstructed images using first 25 PCs

Proposed Method Machine Learning Perform experiments using the WEKA implementation of: Naive Bayes Classifier C4.5 Random Forest

Data and Experiments We used 292 images of galaxies downloaded from the SEDS database. 8, 13 y 25 principal components were used. We report averages of 5 runs using 10-fold cross-validation.

Results Accuracies

Conclussions The proposed method yields better results that any other automated method reported in the literature (still not as good as those obtained by experts). Random Forest obtained the highest accuracy among learning algorithms. A small number of PCs is enough to obtain good accuracy. Image standardization results in an improvement in accuracy.

Future Work Extend method to a larger variety of astronomical objects (nebulae, star clusters, peculiar objects). Work on wide-field images and large sky surveys, such as the Sloan Digital Sky Survey (SDSS). Experiment with more sophisticated image analysis techniques and learning algorithms to improve accuracy.

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