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Register a new userGalaxy And Mass Assembly: Automatic Morphological Classification of Galaxies Using Statistical Learning
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Added by
Sreevarsha Sreejith
Publication date
2017
fields
Physics
and research's language is
English
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We apply four statistical learning methods to a sample of $7941$ galaxies ($z<0.06$) from the Galaxy and Mass Assembly (GAMA) survey to test the feasibility of using automated algorithms to classify galaxies. Using $10$ features measured for each galaxy (sizes, colours, shape parameters & stellar mass) we apply the techniques of Support Vector Machines (SVM), Classification Trees (CT), Classification Trees with Random Forest (CTRF) and Neural Networks (NN), returning True Prediction Ratios (TPRs) of $75.8%$, $69.0%$, $76.2%$ and $76.0%$ respectively. Those occasions whereby all four algorithms agree with each other yet disagree with the visual classification (`unanimous disagreement) serves as a potential indicator of human error in classification, occurring in $sim9%$ of ellipticals, $sim9%$ of Little Blue Spheroids, $sim14%$ of early-type spirals, $sim21%$ of intermediate-type spirals and $sim4%$ of late-type spirals & irregulars. We observe that the choice of parameters rather than that of algorithms is more crucial in determining classification accuracy. Due to its simplicity in formulation and implementation, we recommend the CTRF algorithm for classifying future galaxy datasets. Adopting the CTRF algorithm, the TPRs of the 5 galaxy types are : E, $70.1%$; LBS, $75.6%$; S0-Sa, $63.6%$; Sab-Scd, $56.4%$ and Sd-Irr, $88.9%$. Further, we train a binary classifier using this CTRF algorithm that divides galaxies into spheroid-dominated (E, LBS & S0-Sa) and disk-dominated (Sab-Scd & Sd-Irr), achieving an overall accuracy of $89.8%$. This translates into an accuracy of $84.9%$ for spheroid-dominated systems and $92.5%$ for disk-dominated systems.
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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 explore constraints on the joint photometric and morphological evolution of typical low redshift galaxies as they move from the blue cloud through the green valley and onto the red sequence. We select GAMA survey galaxies with $10.25<{rm log}(M_*/M_odot)<10.75$ and $z<0.2$ classified according to their intrinsic $u^*-r^*$ colour. From single component Sersic fits, we find that the stellar mass-sensitive $K-$band profiles of red and green galaxy populations are very similar, while $g-$band profiles indicate more disk-like morphologies for the green galaxies: apparent (optical) morphological differences arise primarily from radial mass-to-light ratio variations. Two-component fits show that most green galaxies have significant bulge and disk components and that the blue to red evolution is driven by colour change in the disk. Together, these strongly suggest that galaxies evolve from blue to red through secular disk fading and that a strong bulge is present prior to any decline in star formation. The relative abundance of the green population implies a typical timescale for traversing the green valley $sim 1-2$~Gyr and is independent of environment, unlike that of the red and blue populations. While environment likely plays a r^ole in triggering the passage across the green valley, it appears to have little effect on time taken. These results are consistent with a green valley population dominated by (early type) disk galaxies that are insufficiently supplied with gas to maintain previous levels of disk star formation, eventually attaining passive colours. No single event is needed quench their star formation.
We derive the close pair fractions and volume merger rates as a function of luminosity and morphology for galaxies in the GAMA survey with -23 < M(r) < -17 at 0.01 < z < 0.22. The merger fraction is about 0.015 at all luminosities (assuming 1/2 of pairs merge) and the volume merger rate is about 0.00035 per cubic Mpc per Gyr. Dry mergers (between red or spheroidal galaxies) are uncommon and decrease with decreasing luminosity. Fainter mergers are wet, between blue or disky galaxies. Damp mergers (one of each type) follow the average of dry and wet mergers. In the brighter luminosity bin (-23 < M(r) < -20) the merger rate evolution is flat, irrespective of colour or morphology. The makeup of the merging population does not change since z = 0.2. Major mergers and dry mergers appear comparatively unimportant in the buildup of the red sequence over the past 2 Gyr. We compare the colour, morphology, environmental density and degree of activity of galaxies in pairs to those of more isolated objects in the same volume. Galaxies in close pairs tend to be both redder and slightly more spheroid-dominated. This may be due to harassment in multiple previous passes prior to the current interaction. Galaxy pairs do not appear to prefer significantly denser environments. There is no evidence of an enhancement in the AGN fraction in pairs, compared to other galaxies in the same volume.
We use multi-wavelength data from the Galaxy and Mass Assembly (GAMA) survey to explore the cause of red optical colours in nearby (0.002<z<0.06) spiral galaxies. We show that the colours of red spiral galaxies are a direct consequence of some environment-related mechanism(s) which has removed dust and gas, leading to a lower star formation rate. We conclude that this process acts on long timescales (several Gyr) due to a lack of morphological transformation associated with the transition in optical colour. The sSFR and dust-to-stellar mass ratio of red spiral galaxies is found to be statistically lower than blue spiral galaxies. On the other hand, red spirals are on average $0.9$ dex more massive, and reside in environments 2.6 times denser than their blue counterparts. We find no evidence of excessive nuclear activity, or higher inclination angles to support these as the major causes for the red optical colours seen in >= 47% of all spirals in our sample. Furthermore, for a small subsample of our spiral galaxies which are detected in HI, we find that the SFR of gas-rich red spiral galaxies is lower by ~1 dex than their blue 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.
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