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Register a new userDeep Learning Assessment of galaxy morphology in S-PLUS DataRelease 1
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The morphological classification of galaxies is a relevant probe for galaxy evolution and unveils its connection with cosmological structure formation. To this scope, it is fundamental to recover galaxy morphologies over large areas of the sky. In this paper, we present a morphological catalogue for galaxies in the Stripe-82 area, observed with S-PLUS, till a magnitude limit of $rle17$, using the state-of-the-art of Convolutional Neural Networks (CNNs) for computer vision. This analysis will then be extended to the whole S-PLUS survey data, covering $simeq 9300$ $deg^{2}$ of the celestial sphere in twelve optical bands. We find that the networks performance increases with 5 broad bands and additional 3 narrow bands compared to our baseline with 3 bands. However, it does lose performance when using the full $12$ band image information. Nevertheless, the best result is achieved with 3 bands, when using pre-trained network weights in an ImageNet dataset. These results enhance the importance of previous knowledge in the neural network weights based on training in non related extensive datasets. Thus, we release a model pre-trained in several bands that could be adapted to other surveys. We develop a catalogue of 3274 galaxies in Stripe-82 that are not presented in Galaxy Zoo 1 (GZ1). We also add classification to 4686 galaxies considered ambiguous in GZ1 dataset. Finally, we present a prospect of a novel way to take advantage of $12$ bands information for morphological classification using multiband morphometric features. The morphological catalogues are publicly available.
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Establishing accurate morphological measurements of galaxies in a reasonable amount of time for future big-data surveys such as EUCLID, the Large Synoptic Survey Telescope or the Wide Field Infrared Survey Telescope is a challenge. Because of its high level of abstraction with little human intervention, deep learning appears to be a promising approach. Deep learning is a rapidly growing discipline that models high-level patterns in data as complex multilayered networks. In this work we test the ability of deep convolutional networks to provide parametric properties of Hubble Space Telescope like galaxies (half-light radii, Sersic indices, total flux etc..). We simulate a set of galaxies including point spread function and realistic noise from the CANDELS survey and try to recover the main galaxy parameters using deep-learning. We com- pare the results with the ones obtained with the commonly used profile fitting based software GALFIT. This way showing that with our method we obtain results at least equally good as the ones obtained with GALFIT but, once trained, with a factor 5 hundred time faster.
We present a star/galaxy classification for the Southern Photometric Local Universe Survey (S-PLUS), based on a Machine Learning approach: the Random Forest algorithm. We train the algorithm using the S-PLUS optical photometry up to $r$=21, matched to SDSS/DR13, and morphological parameters. The metric of importance is defined as the relative decrease of the initial accuracy when all correlations related to a certain feature is vanished. In general, the broad photometric bands presented higher importance when compared to narrow ones. The influence of the morphological parameters has been evaluated training the RF with and without the inclusion of morphological parameters, presenting accuracy values of 95.0% and 88.1%, respectively. Particularly, the morphological parameter {rm FWHM/PSF} performed the highest importance over all features to distinguish between stars and galaxies, indicating that it is crucial to classify objects into stars and galaxies. We investigate the misclassification of stars and galaxies in the broad-band colour-colour diagram $(g-r)$ versus $(r-i)$. The morphology can notably improve the classification of objects at regions in the diagram where the misclassification was relatively high. Consequently, it provides cleaner samples for statistical studies. The expected contamination rate of red galaxies as a function of the redshift is estimated, providing corrections for red galaxy samples. The classification of QSOs as extragalactic objects is slightly better using photometric-only case. An extragalactic point-source catalogue is provided using the classification without any morphology feature (only the SED information) with additional constraints on photometric redshifts and {rm FWHM/PSF} values.
This work is a Brazilian-Indian collaboration. It aims at investigating the structuralproperties of Lenticular galaxies in the Stripe 82 using a combination of S-PLUS (Southern Photometric Local Universe Survey) and SDSS data. S-PLUS is a noveloptical multi-wavelength survey which will cover nearly 8000 square degrees of the Southern hemisphere in the next years and the first data release covers the Stripe 82 area. The morphological classification and study of the galaxies stellar population will be performed combining the Bayesian Spectral type (from BPZ) and Morfometryka (MFMTK) parameters. BPZ and MFMTK are two complementary techniques, since the first one determines the most likely stellar population of a galaxy, in order to obtain its photometric redshift (phot-z), and the second one recovers non-parametric morphological quantities, such as asymmetries and concentration. The combination ofthe two methods allows us to explore the correlation between galaxies shapes (smooth, with spiral arms, etc.) and their stellar contents (old or young population). The preliminary results, presented in this work, show how this new data set opens a new window on our understanding of the nearby universe.
We present Galaxy Zoo DECaLS: detailed visual morphological classifications for Dark Energy Camera Legacy Survey images of galaxies within the SDSS DR8 footprint. Deeper DECaLS images (r=23.6 vs. r=22.2 from SDSS) reveal spiral arms, weak bars, and tidal features not previously visible in SDSS imaging. To best exploit the greater depth of DECaLS images, volunteers select from a new set of answers designed to improve our sensitivity to mergers and bars. Galaxy Zoo volunteers provide 7.5 million individual classifications over 314,000 galaxies. 140,000 galaxies receive at least 30 classifications, sufficient to accurately measure detailed morphology like bars, and the remainder receive approximately 5. All classifications are used to train an ensemble of Bayesian convolutional neural networks (a state-of-the-art deep learning method) to predict posteriors for the detailed morphology of all 314,000 galaxies. When measured against confident volunteer classifications, the networks are approximately 99% accurate on every question. Morphology is a fundamental feature of every galaxy; our human and machine classifications are an accurate and detailed resource for understanding how galaxies evolve.
The Southern Photometric Local Universe Survey (S-PLUS) aims to map $approx$ 9300 deg$^2$ of the Southern sky using the Javalambre filter system of 12 optical bands, 5 Sloan-like filters and 7 narrow-band filters centered on several prominent stellar features ([OII], Ca H+K, D4000, H$_{delta}$, Mgb, H$_{alpha}$ and CaT). S-PLUS is carried out with the T80-South, a new robotic 0.826-m telescope located on CTIO, equipped with a wide FoV camera (2 deg$^2$). In this poster we introduce project #59 of the S-PLUS collaboration aimed at studying the Fornax galaxy cluster covering an sky area of $approx$ 11 $times$ 7 deg$^2$, and with homogeneous photometry in the 12 optical bands of S-PLUS (Coordinator: A. Smith Castelli).
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