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Register a new userMorfometryka -- A New Way of Establishing Morphological Classification of Galaxies
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We present an extended morphometric system to automatically classify galaxies from astronomical images. The new system includes the original and modifie
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Classifying the morphologies of galaxies is an important step in understanding their physical properties and evolutionary histories. The advent of large-scale surveys has hastened the need to develop techniques for automated morphological classification. We train and test several convolutional neural network architectures to classify the morphologies of galaxies in both a 3-class (elliptical, lenticular, spiral) and 4-class (+irregular/miscellaneous) schema with a dataset of 14034 visually-classified SDSS images. We develop a new CNN architecture that outperforms existing models in both 3 and 4-way classification, with overall classification accuracies of 83% and 81% respectively. We also compare the accuracies of 2-way / binary classifications between all four classes, showing that ellipticals and spirals are most easily distinguished (>98% accuracy), while spirals and irregulars are hardest to differentiate (78% accuracy). Through an analysis of all classified samples, we find tentative evidence that misclassifications are physically meaningful, with lenticulars misclassified as ellipticals tending to be more massive, among other trends. We further combine our binary CNN classifiers to perform a hierarchical classification of samples, obtaining comparable accuracies (81%) to the direct 3-class CNN, but considerably worse accuracies in the 4-way case (65%). As an additional verification, we apply our networks to a small sample of Galaxy Zoo images, obtaining accuracies of 92%, 82% and 77% for the binary, 3-way and 4-way classifications respectively.
There are several supervised machine learning methods used for the application of automated morphological classification of galaxies; however, there has not yet been a clear comparison of these different methods using imaging data, or a investigation for maximising their effectiveness. We carry out a comparison between several common machine learning methods for galaxy classification (Convolutional Neural Network (CNN), K-nearest neighbour, Logistic Regression, Support Vector Machine, Random Forest, and Neural Networks) by using Dark Energy Survey (DES) data combined with visual classifications from the Galaxy Zoo 1 project (GZ1). Our goal is to determine the optimal machine learning methods when using imaging data for galaxy classification. We show that CNN is the most successful method of these ten methods in our study. Using a sample of $sim$2,800 galaxies with visual classification from GZ1, we reach an accuracy of $sim$0.99 for the morphological classification of Ellipticals and Spirals. The further investigation of the galaxies that have a different ML and visual classification but with high predicted probabilities in our CNN usually reveals an the incorrect classification provided by GZ1. We further find the galaxies having a low probability of being either spirals or ellipticals are visually Lenticulars (S0), demonstrating that supervised learning is able to rediscover that this class of galaxy is distinct from both Es and Spirals. We confirm that $sim$2.5% galaxies are misclassified by GZ1 in our study. After correcting these galaxies labels, we improve our CNN performance to an average accuracy of over 0.99 (accuracy of 0.994 is our best result).
Encoded within the morphological structure of galaxies are clues related to their formation and evolutionary history. Recent advances pertaining to the statistics of galaxy morphology include sophisticated measures of concentration (C), asymmetry (A), and clumpiness (S). In this study, these three parameters (CAS) have been applied to a suite of simulated galaxies and compared with observational results inferred from a sample of nearby galaxies. The simulations span a range of late-type systems, with masses between ~1e10 Msun and ~1e12 Msun, and employ star formation density thresholds between 0.1 cm^-3 and 100 cm^-3. We have found that the simulated galaxies possess comparable concentrations to their real counterparts. However, the results of the CAS analysis revealed that the simulated galaxies are generally more asymmetric, and that the range of clumpiness values extends beyond the range of those observed. Strong correlations were obtained between the three CAS parameters and colour (B-V), consistent with observed galaxies. Furthermore, the simulated galaxies possess strong links between their CAS parameters and Hubble type, mostly in-line with their real counterparts.
Galaxy morphology is a fundamental quantity, that is essential not only for the full spectrum of galaxy-evolution studies, but also for a plethora of science in observational cosmology. While a rich literature exists on morphological-classification techniques, the unprecedented data volumes, coupled, in some cases, with the short cadences of forthcoming Big-Data surveys (e.g. from the LSST), present novel challenges for this field. Large data volumes make such datasets intractable for visual inspection (even via massively-distributed platforms like Galaxy Zoo), while short cadences make it difficult to employ techniques like supervised machine-learning, since it may be impractical to repeatedly produce training sets on short timescales. Unsupervised machine learning, which does not require training sets, is ideally suited to the morphological analysis of new and forthcoming surveys. Here, we employ an algorithm that performs clustering of graph representations, in order to group image patches with similar visual properties and objects constructed from those patches, like galaxies. We implement the algorithm on the Hyper-Suprime-Cam Subaru-Strategic-Program Ultra-Deep survey, to autonomously reduce the galaxy population to a small number (160) of morphological clusters, populated by galaxies with similar morphologies, which are then benchmarked using visual inspection. The morphological classifications (which we release publicly) exhibit a high level of purity, and reproduce known trends in key galaxy properties as a function of morphological type at z<1 (e.g. stellar-mass functions, rest-frame colours and the position of galaxies on the star-formation main sequence). Our study demonstrates the power of unsupervised machine learning in performing accurate morphological analysis, which will become indispensable in this new era of deep-wide surveys.
We study the two main constituent galaxies of a constrained simulation of the Local Group as candidates for the Milky Way (MW) and Andromeda (M31). We focus on the formation of the stellar discs and its relation to the formation of the group as a rich system with two massive galaxies, and investigate the effects of mergers and accretion as drivers of morphological transformations. We use a state-of-the-art hydrodynamical code which includes star formation, feedback and chemical enrichment to carry out our study. We run two simulations, where we include or neglect the effects of radiation pressure from stars, to investigate the impact of this process on the morphologies and star formation rates of the simulated galaxies. We find that the simulated M31 and MW have different formation histories, even though both inhabit, at z=0, the same environment. These differences directly translate into and explain variations in their star formation rates, in-situ fractions and final morphologies. The M31 candidate has an active merger history, as a result of which its stellar disc is unable to survive unaffected until the present time. In contrast, the MW candidate has a smoother history with no major mergers at late times, and forms a disc that grows steadily; at z=0 the simulated MW has an extended, rotationally-supported disc which is dominant over the bulge. Our two feedback implementations predict similar evolution of the galaxies and their discs, although some variations are detected, the most important of which is the formation time of the discs: in the model with weaker/stronger feedback the discs form earlier/later. In summary, by comparing the formation histories of the two galaxies, we conclude that the particular merger/accretion history of a galaxy rather than its environment at the LG-scales is the main driver of the formation and subsequent growth or destruction of galaxy discs.
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