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Register a new userJ-PLUS: Morphological star/galaxy classification by PDF analysis
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Carlos L\\'opez-Sanjuan
Publication date
2018
fields
Physics
and research's language is
English
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Our goal is to morphologically classify the sources identified in the images of the J-PLUS early data release (EDR) into compact (stars) or extended (galaxies) using a suited Bayesian classifier. J-PLUS sources exhibit two distinct populations in the r-band magnitude vs. concentration plane, corresponding to compact and extended sources. We modelled the two-population distribution with a skewed Gaussian for compact objects and a log-normal function for the extended ones. The derived model and the number density prior based on J-PLUS EDR data were used to estimate the Bayesian probability of a source to be star or galaxy. This procedure was applied pointing-by-pointing to account for varying observing conditions and sky position. Finally, we combined the morphological information from g, r, and i broad bands in order to improve the classification of low signal-to-noise sources. The derived probabilities are used to compute the pointing-by-pointing number counts of stars and galaxies. The former increases as we approach to the Milky Way disk, and the latter are similar across the probed area. The comparison with SDSS in the common regions is satisfactory up to r ~ 21, with consistent numbers of stars and galaxies, and consistent distributions in concentration and (g - i) colour spaces. We implement a morphological star/galaxy classifier based on PDF analysis, providing meaningful probabilities for J-PLUS sources to one magnitude deeper (r ~ 21) than a classical boolean classification. These probabilities are suited for the statistical study of 150k stars and 101k galaxies with 15 < r < 21 present in the 31.7 deg2 of the J-PLUS EDR. In a future version of the classifier, we will include J-PLUS colour information from twelve photometric bands.
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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.
The universe is composed of galaxies that have diverse shapes. Once the structure of a galaxy is determined, it is possible to obtain important information about its formation and evolution. Morphologically classifying galaxies means cataloging them according to their visual appearance and the classification is linked to the physical properties of the galaxy. A morphological classification made through visual inspection is subject to biases introduced by subjective observations made by human volunteers. For this reason, systematic, objective and easily reproducible classification of galaxies has been gaining importance since the astronomer Edwin Hubble created his famous classification method. In this work, we combine accurate visual classifications of the Galaxy Zoo project with emph {Deep Learning} methods. The goal is to find an efficient technique at human performance level classification, but in a systematic and automatic way, for classification of elliptical and spiral galaxies. For this, a neural network model was created through an Ensemble of four other convolutional models, allowing a greater accuracy in the classification than what would be obtained with any one individual. Details of the individual models and improvements made are also described. The present work is entirely based on the analysis of images (not parameter tables) from DR1 (www.datalab.noao.edu) of the Southern Photometric Local Universe Survey (S-PLUS). In terms of classification, we achieved, with the Ensemble, an accuracy of $approx 99 %$ in the test sample (using pre-trained networks).
We discuss the statistical foundations of morphological star-galaxy separation. We show that many of the star-galaxy separation metrics in common use today (e.g. by SDSS or SExtractor) are closely related both to each other, and to the model odds ratio derived in a Bayesian framework by Sebok (1979). While the scaling of these algorithms with the noise properties of the sources varies, these differences do not strongly differentiate their performance. We construct a model of the performance of a star-galaxy separator in a realistic survey to understand the impact of observational signal-to-noise ratio (or equivalently, 5-sigma limiting depth) and seeing on classification performance. The model quantitatively demonstrates that, assuming realistic densities and angular sizes of stars and galaxies, 10% worse seeing can be compensated for by approximately 0.4 magnitudes deeper data to achieve the same star-galaxy classification performance. We discuss how to probabilistically combine multiple measurements, either of the same type (e.g., subsequent exposures), or differing types (e.g., multiple bandpasses), or differing methodologies (e.g., morphological and color-based classification). These methods are increasingly important for observations at faint magnitudes, where the rapidly rising number density of small galaxies makes star-galaxy classification a challenging problem. However, because of the significant role that the signal-to-noise ratio plays in resolving small galaxies, surveys with large-aperture telescopes, such as LSST, will continue to see improving star-galaxy separation as they push to these fainter magnitudes.
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The intracluster light (ICL) is a luminous component of galaxy clusters composed of stars that are gravitationally bound to the cluster potential but do not belong to the individual galaxies. Previous studies of the ICL have shown that its formation and evolution are intimately linked to the evolutionary stage of the cluster. Thus, the analysis of the ICL in the Coma cluster will give insights into the main processes driving the dynamics in this highly complex system. Using a recently developed technique, we measure the ICL fraction in Coma at several wavelengths, using the J-PLUS unique filter system. The combination of narrow- and broadband filters provides valuable information on the dynamical state of the cluster, the ICL stellar types, and the morphology of the diffuse light. We use the Chebyshev-Fourier Intracluster Light Estimator (CICLE) to disentangle the ICL from the light of the galaxies, and to robustly measure the ICL fraction in seven J-PLUS filters. We obtain the ICL fraction distribution of the Coma cluster at different optical wavelengths, which varies from $sim 7%-21%$, showing the highest values in the narrowband filters J0395, J0410, and J0430. This ICL fraction excess is distinctive pattern recently observed in dynamically active clusters (mergers), indicating a higher amount of bluer stars in the ICL compared to the cluster galaxies. Both the high ICL fractions and the excess in the bluer filters are indicative of a merging state. The presence of younger/lower-metallicity stars the ICL suggests that the main mechanism of ICL formation for the Coma cluster is the stripping of the stars in the outskirts of infalling galaxies and, possibly, the disruption of dwarf galaxies during past/ongoing mergers.
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