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Register a new userThe impact from survey depth and resolution on the morphological classification of galaxies
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We consistently analyse for the first time the impact of survey depth and spatial resolution on the most used morphological parameters for classifying galaxies through non-parametric methods: Abraham and Conselice-Bershady concentration indices, Gini, M20 moment of light, asymmetry, and smoothness. Three different non-local datasets are used, ALHAMBRA and SXDS (examples of deep ground-based surveys), and COSMOS (deep space-based survey). We used a sample of 3000 local, visually classified galaxies, measuring their morphological parameters at their real redshifts (z ~ 0). Then we simulated them to match the redshift and magnitude distributions of galaxies in the non-local surveys. The comparisons of the two sets allow to put constraints on the use of each parameter for morphological classification and evaluate the effectiveness of the commonly used morphological diagnostic diagrams. All analysed parameters suffer from biases related to spatial resolution and depth, the impact of the former being much stronger. When including asymmetry and smoothness in classification diagrams, the noise effects must be taken into account carefully, especially for ground-based surveys. M20 is significantly affected, changing both the shape and range of its distribution at all brightness levels.We suggest that diagnostic diagrams based on 2 - 3 parameters should be avoided when classifying galaxies in ground-based surveys, independently of their brightness; for COSMOS they should be avoided for galaxies fainter than F814 = 23.0. These results can be applied directly to surveys similar to ALHAMBRA, SXDS and COSMOS, and also can serve as an upper/lower limit for shallower/deeper ones.
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Methods. We used different galaxy classification techniques: human labeling, multi-photometry diagrams, Naive Bayes, Logistic Regression, Support Vector Machine, Random Forest, k-Nearest Neighbors, and k-fold validation. Results. We present results of a binary automated morphological classification of galaxies conducted by human labeling, multiphotometry, and supervised Machine Learning methods. We applied its to the sample of galaxies from the SDSS DR9 with 0.02 < z < 0.1 and 24m < Mr < 19.4m. To study the classifier, we used absolute magnitudes: Mu, Mg, Mr , Mi, Mz, Mu-Mr , Mg-Mi, Mu-Mg, Mr-Mz, and inverse concentration index to the center R50/R90. Using the Support vector machine classifier and the data on color indices, absolute magnitudes, inverse concentration index of galaxies with visual morphological types, we were able to classify 316 031 galaxies from the SDSS DR9 with unknown morphological types. Conclusions. The methods of Support Vector Machine and Random Forest with Scikit-learn machine learning in Python provide the highest accuracy for the binary galaxy morphological classification: 96.4% correctly classified (96.1% early E and 96.9% late L types) and 95.5% correctly classified (96.7% early E and 92.8% late L types), respectively. Applying the Support Vector Machine for the sample of 316 031 galaxies from the SDSS DR9 at z < 0.1, we found 141 211 E and 174 820 L types among them.
We attempt to visually classify the morphologies of 18190 molecular clouds, which are identified in the $^{12}$CO(1-0) spectral line data over $sim$ 450 deg$^{2}$ of the second Galactic quadrant from the Milky Way Imaging Scroll Painting project (MWISP). Using the velocity-integrated intensity maps of the $^{12}$CO(1-0) emission, molecular clouds are first divided into unresolved and resolved ones. The resolved clouds are further classified as non-filaments or filaments. Among the 18190 molecular clouds, $sim$ 25 $%$ are unresolved, $sim$ 64$%$ are non-filaments, and $sim$ 11$%$ are filaments. In the terms of the integrated flux of $^{12}$CO(1-0) spectra of the whole 18190 molecular clouds, $sim$ 90$%$ are from filaments, $sim$ 9$%$ are from non-filaments, and the rest $sim$ 1$%$ are from unresolved sources. Although non-filaments are dominant in the number of the discrete molecular clouds, filaments are the main contributor of $^{12}$CO emission flux. We also present the number distributions of physical parameters of the molecular clouds in our catalog, including their angular sizes, velocity spans, peak intensities of $^{12}$CO(1-0) emission, and $^{12}$CO(1-0) total fluxes. We find that there is a systematic difference between the angular sizes of the non-filaments and filaments, with the filaments tending to have larger angular scales. The H$_{2}$ column densities of them are not significantly different. We also discuss the observational effects, such as those induced by the finite spatial resolution, beam dilution and line-of-sight projection, on the morphological classification of molecular clouds in our sample.
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$2.5% galaxies are misclassified by GZ1 in our study. After correcting these galaxies labels, we improve our CNN performance to an average accuracy of over 0.99 (accuracy of 0.994 is our best result).
We study the morphological transformation from late types to early types and the quenching of galaxies with the seventh Data Release (DR7) of the Sloan Digital Sky Survey (SDSS). Both early type galaxies and late type galaxies are found to have bimodal distributions on the star formation rate versus stellar mass diagram ($lg SFR - lg M_*$). We therefore classify them into four types: the star-forming early types (sEs), the quenched early types (qEs), the star-forming late types (sLs) and the quenched late types (qLs). We checked many parameters on various environmental scales for their potential effects on the quenching rates of late types and early types, as well as the early type fractions among star-forming galaxies and those among quenched galaxies. These parameters include: the stellar mass $M_*$, and the halo mass $M_{halo}$; the small-scale environmental parameters, such as the halo centric radius $R_p/r_{180}$ and the third nearest neighbor distances ($d_{3nn}$); the large-scale environmental parameters, specifically whether they are located in clusters, filaments, sheets, or voids. We found that the morphological transformation is mainly regulated by the stellar mass. Quenching is mainly driven by the stellar mass for more massive galaxies and by the halo mass for galaxies with smaller stellar masses. In addition, we see an overall stronger halo quenching effect in early type galaxies, which might be attributed to their lacking of cold gas or earlier accretion into the massive host halos.
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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