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Register a new userMorphological Star-Galaxy Separation
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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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Context: It is crucial to develop a method for classifying objects detected in deep surveys at infrared wavelengths. We specifically need a method to separate galaxies from stars using only the infrared information to study the properties of galaxies, e.g., to estimate the angular correlation function, without introducing any additional bias. Aims. We aim to separate stars and galaxies in the data from the AKARI North Ecliptic Pole (NEP) Deep survey collected in nine AKARI / IRC bands from 2 to 24 {mu}m that cover the near- and mid-infrared wavelengths (hereafter NIR and MIR). We plan to estimate the correlation function for NIR and MIR galaxies from a sample selected according to our criteria in future research. Methods: We used support vector machines (SVM) to study the distribution of stars and galaxies in the AKARIs multicolor space. We defined the training samples of these objects by calculating their infrared stellarity parameter (sgc). We created the most efficient classifier and then tested it on the whole sample. We confirmed the developed separation with auxiliary optical data obtained by the Subaru telescope and by creating Euclidean normalized number count plots. Results: We obtain a 90% accuracy in pinpointing galaxies and 98% accuracy for stars in infrared multicolor space with the infrared SVM classifier. The source counts and comparison with the optical data (with a consistency of 65% for selecting stars and 96% for galaxies) confirm that our star/galaxy separation methods are reliable. Conclusions: The infrared classifier derived with the SVM method based on infrared sgc- selected training samples proves to be very efficient and accurate in selecting stars and galaxies in deep surveys at infrared wavelengths carried out without any previous target object selection.
Modern cosmological surveys such as the Hyper Suprime-Cam (HSC) survey produce a huge volume of low-resolution images of both distant galaxies and dim stars in our own galaxy. Being able to automatically classify these images is a long-standing problem in astronomy and critical to a number of different scientific analyses. Recently, the challenge of star-galaxy classification has been approached with Deep Neural Networks (DNNs), which are good at learning complex nonlinear embeddings. However, DNNs are known to overconfidently extrapolate on unseen data and require a large volume of training images that accurately capture the data distribution to be considered reliable. Gaussian Processes (GPs), which infer posterior distributions over functions and naturally quantify uncertainty, havent been a tool of choice for this task mainly because popular kernels exhibit limited expressivity on complex and high-dimensional data. In this paper, we present a novel approach to the star-galaxy separation problem that uses GPs and reap their benefits while solving many of the issues traditionally affecting them for classification of high-dimensional celestial image data. After an initial filtering of the raw data of star and galaxy image cutouts, we first reduce the dimensionality of the input images by using a Principal Components Analysis (PCA) before applying GPs using a simple Radial Basis Function (RBF) kernel on the reduced data. Using this method, we greatly improve the accuracy of the classification over a basic application of GPs while improving the computational efficiency and scalability of the method.
We provide a brief overview of the Galaxy Zoo and Zooniverse projects, including a short discussion of the history of, and motivation for, these projects as well as reviewing the science these innovative internet-based citizen science projects have produced so far. We briefly describe the method of applying en-masse human pattern recognition capabilities to complex data in data-intensive research. We also provide a discussion of the lessons learned from developing and running these community--based projects including thoughts on future applications of this methodology. This review is intended to give the reader a quick and simple introduction to the Zooniverse.
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.
The search for fast optical transients, such as the expected electromagnetic counterparts to binary neutron star mergers, is riddled with false positives ranging from asteroids to stellar flares. While moving objects are readily rejected via image pairs separated by $sim$1 hr, stellar flares represent a challenging foreground that significantly outnumber rapidly-evolving explosions. Identifying stellar sources close to and fainter than the transient detection limit can eliminate these false positives. Here, we present a method to reliably identify stars in deep co-adds of Palomar Transient Factory (PTF) imaging. Our machine-learning methodology utilizes the random forest (RF) algorithm, which is trained using $> 3times{10}^6$ sources with Sloan Digital Sky Survey (SDSS) spectra. When evaluated on an independent test set, the PTF RF model outperforms the SExtractor star classifier by $sim$4%. For faint sources ($rge{21}$ mag), which dominate the field population, the PTF RF model produces a $sim$19% improvement over SExtractor. To avoid false negatives in the PTF transient-candidate stream, we adopt a conservative stellar classification threshold, corresponding to a galaxy misclassification rate = 0.005. Ultimately, $sim$$1.70times{10}^8$ objects are included in our PTF point-source catalog, of which only $sim$$10^6$ are expected to be galaxies. We demonstrate that the PTF RF catalog reveals transients that otherwise would have been missed. To leverage its superior image quality, we additionally create an SDSS point-source catalog, which is also tuned to have a galaxy misclassification rate = 0.005. These catalogs have been incorporated into the PTF real-time pipelines to automatically reject stellar sources as non-extragalactic transients.
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