Machine and Deep Learning applied to galaxy morphology - A comparative study

Abstract

Morphological classification is a key piece of information to define samples of galaxies aiming to study the large-scale structure of the universe. In essence, the challenge is to build up a robust methodology to perform a reliable morphological estimate from galaxy images. Here, we investigate how to substantially improve the galaxy classification within large datasets by mimicking human classification. We combine accurate visual classifications from the Galaxy Zoo project with machine and deep learning methodologies. We propose two distinct approaches for galaxy morphology: one based on non-parametric morphology and traditional machine learning algorithms; and another based on Deep Learning. To measure the input features for the traditional machine learning methodology, we have developed a system called CyMorph, with a novel non-parametric approach to study galaxy morphology. The main datasets employed comes from the Sloan Digital Sky Survey Data Release 7 (SDSS-DR7). We also discuss the class imbalance problem considering three classes. Performance of each model is mainly measured by Overall Accuracy (OA). A spectroscopic validation with astrophysical parameters is also provided for Decision Tree models to assess the quality of our morphological classification. In all of our samples, both Deep and Traditional Machine Learning approaches have over 94.5% OA to classify galaxies in two classes (elliptical and spiral). We compare our classification with state-of-the-art morphological classification from literature. Considering only two classes separation, we achieve 99% of overall accuracy in average when using our deep learning models, and 82% when using three classes. We provide a catalog with 670,560 galaxies containing our best results, including morphological metrics and classification.ETGs have T-Type ≤ 0 and LTGs have T-Type > 0 (de Vaucouleurs, 1963). T-Type considers ellipticity and spiral arms strength but does not reflect the presence or absence of the bar feature in spirals.Morphology reveals structural, intrinsic and environmental properties of galaxies. In the local universe, ETGs are mostly situated in the center of galaxy clusters, have a larger mass, less gas, higher velocity dispersion, and older stellar populations than LTGs, which are rich star-forming systems (Roberts and Haynes, 1994;Blanton and Moustakas, 2009;Pozzetti et al., 2010). By mapping where the ETGs are, it is possible to map the large-scale structure of the universe. Therefore, galaxy morphology is of paramount importance for extragalactic research as it relates to stellar properties and key aspects of the evolution and structure of the universe.Astronomy has become an extremely data-rich field of knowledge with the advance of new technologies in recent decades. Nowadays it is impossible to rely on human classification given the huge flow of data attained by current research

Classification and evolution of galaxies according to the dynamical state of host clusters and galaxy luminosities

“…First, we use the Domínguez Sánchez et al (2018) catalog, which provides T morphological types for ∼ 670, 000 galaxies from SDSS, by training Convolutional Neural Networks (CNNs) with information from available sources such as Galaxy Zoo 2 (GZ2, Lintott et al 2008;Willett et al 2013) and the catalog of visual classifications provided by Nair & Abraham (2010). Secondly, we include the morphometric parameter G2, from the catalog of Barchi et al (2020). This parameter is obtained applying the CyMorph algorithm on GZ2 galaxies, that uses the second gradient moments of the galaxy image to compute G2 (Rosa et al 2018).…”

Machine learning technique for morphological classification of galaxies from the SDSS

“…These authors obtained that CNN is the most successful method for the binary morphological classification dealing with galaxy images; using a sample of ∼2 800 galaxies at z < 0.25, they attained an accuracy of ∼99 %. Barchi et al (2020) produced a catalog with morphological data for 670 560 galaxies at 0.03 < z < 0.1, where the input data were taken from SDSS-DR7 (Petrosian magnitude in r-band brighter than 17.78, and |b| > 30 o ). They used traditional machine learning (TML) and deep learning (DL) approaches to distinguish elliptical (E) from spiral (S ) galaxies.…”

Classification of Planetary Nebulae through Deep Transfer Learning

“…The results can be generalized to other datasets, such as the deep-field images where the most distant galaxies also present extended objects in highly confused fields [12]. [11]; Elliptical PNe in Optical images, Hα "Quotient" images and infrared ("WISE432") images; and high-resolution Optical Pan-STARRS images.…”

“…Recently, Akras et al [9] used a Machine Learning (ML) technique alongside the infrared photometric data to distinguish compact PNe from their mimics. Deep learning has been used for galaxy morphology classification [10], mostly utilizing the Galaxy Zoo dataset [11]. Galaxy morphologies are easier to determine, and the objects are little affected by foreground stars, as depicted in Figure 1.…”