Search for unusual objects in the WISE Survey

Automatic source detection and classification tools based on machine learning (ML) algorithms are growing in popularity due to their efficiency when dealing with large amounts of data simultaneously and their ability to work in multidimensional parameter spaces. In this work, we present a new, automated method of outlier selection based on support vector machine (SVM) algorithm called one-class SVM (OCSVM), which uses the training data as one class to construct a model of 'normality' in order to recognize novel points. We test the performance of OCSVM algorithm on \textit{Wide-field Infrared Survey Explorer (WISE)} data trained on the Sloan Digital Sky Survey (SDSS) sources. Among others, we find $\sim 40,000$ sources with abnormal patterns which can be associated with obscured and unobscured active galactic nuclei (AGN) source candidates. We present the preliminary estimation of the clustering properties of these objects and find that the unobscured AGN candidates are preferentially found in less massive dark matter haloes ($M_{DMH}\sim10^{12.4}$) than the obscured candidates ($M_{DMH}\sim 10^{13.2}$). This result contradicts the unification theory of AGN sources and indicates that the obscured and unobscured phases of AGN activity take place in different evolutionary paths defined by different environments.Comment: 4 figures, 6 page

Automated novelty detection in the WISE survey with one-class support vector machines

Wide-angle photometric surveys of previously uncharted sky areas or wavelength regimes will always bring in unexpected sources whose existence and properties cannot be easily predicted from earlier observations: novelties or even anomalies. Such objects can be efficiently sought for with novelty detection algorithms. Here we present an application of such a method, called one-class support vector machines (OCSVM), to search for anomalous patterns among sources preselected from the mid-infrared AllWISE catalogue covering the whole sky. To create a model of expected data we train the algorithm on a set of objects with spectroscopic identifications from the SDSS DR13 database, present also in AllWISE. OCSVM detects as anomalous those sources whose patterns - WISE photometric measurements in this case - are inconsistent with the model. Among the detected anomalies we find artefacts, such as objects with spurious photometry due to blending, but most importantly also real sources of genuine astrophysical interest. Among the latter, OCSVM has identified a sample of heavily reddened AGN/quasar candidates distributed uniformly over the sky and in a large part absent from other WISE-based AGN catalogues. It also allowed us to find a specific group of sources of mixed types, mostly stars and compact galaxies. By combining the semi-supervised OCSVM algorithm with standard classification methods it will be possible to improve the latter by accounting for sources which are not present in the training sample but are otherwise well-represented in the target set. Anomaly detection adds flexibility to automated source separation procedures and helps verify the reliability and representativeness of the training samples. It should be thus considered as an essential step in supervised classification schemes to ensure completeness and purity of produced catalogues.Comment: 14 pages, 15 figure

Machine-learning identification of galaxies in the WISExSuperCOSMOS all-sky catalogue

The two currently largest all-sky photometric datasets, WISE and SuperCOSMOS, were cross-matched by Bilicki et al. (2016) (B16) to construct a novel photometric redshift catalogue on 70% of the sky. Galaxies were therein separated from stars and quasars through colour cuts, which may leave imperfections because of mixing different source types which overlap in colour space. The aim of the present work is to identify galaxies in the WISExSuperCOSMOS catalogue through an alternative approach of machine learning. This allows us to define more complex separations in the multi-colour space than possible with simple colour cuts, and should provide more reliable source classification. For the automatised classification we use the support vector machines learning algorithm, employing SDSS spectroscopic sources cross-matched with WISExSuperCOSMOS as the training and verification set. We perform a number of tests to examine the behaviour of the classifier (completeness, purity and accuracy) as a function of source apparent magnitude and Galactic latitude. We then apply the classifier to the full-sky data and analyse the resulting catalogue of candidate galaxies. We also compare thus produced dataset with the one presented in B16. The tests indicate very high accuracy, completeness and purity (>95%) of the classifier at the bright end, deteriorating for the faintest sources, but still retaining acceptable levels of 85%. No significant variation of classification quality with Galactic latitude is observed. Application of the classifier to all-sky WISExSuperCOSMOS data gives 15 million galaxies after masking problematic areas. The resulting sample is purer than the one in B16, at a price of lower completeness over the sky. The automatic classification gives a successful alternative approach to defining a reliable galaxy sample as compared to colour cuts.Comment: 12 pages, 15 figures, accepted for publication in A&A. Obtained catalogue will be included in the public release of the WISExSuperCOSMOS galaxy catalogue available from http://ssa.roe.ac.uk/WISExSCO

Radio-Infrared Correlation for Local Dusty Galaxies and Dusty AGNs from the AKARI All Sky Survey

We use the new release of the AKARI Far-Infrared all sky Survey matched with the NVSS radio database to investigate the local ($z<0.25$) far infrared-radio correlation (FIRC) of different types of extragalactic sources. To obtain the redshift information for the AKARI FIS sources we crossmatch the catalogue with the SDSS DR8. This also allows us to use emission line properties to divide sources into four categories: i) star-forming galaxies (SFGs), ii) composite galaxies (displaying both star-formation and active nucleus components), iii) Seyfert galaxies, and iv) low-ionization nuclear emission-line region (LINER) galaxies. We find that the Seyfert galaxies have the lowest FIR/radio flux ratios and display excess radio emission when compared to the SFGs. We conclude that FIRC can be used to separate SFGs and AGNs only for the most radio-loud objects.Comment: 9 pages, accepted to PAS

Catalog of quasars from the Kilo-Degree Survey Data Release 3

We present a catalog of quasars selected from broad-band photometric ugri data of the Kilo-Degree Survey Data Release 3 (KiDS DR3). The QSOs are identified by the random forest (RF) supervised machine learning model, trained on SDSS DR14 spectroscopic data. We first cleaned the input KiDS data from entries with excessively noisy, missing or otherwise problematic measurements. Applying a feature importance analysis, we then tune the algorithm and identify in the KiDS multiband catalog the 17 most useful features for the classification, namely magnitudes, colors, magnitude ratios, and the stellarity index. We used the t-SNE algorithm to map the multi-dimensional photometric data onto 2D planes and compare the coverage of the training and inference sets. We limited the inference set to r<22 to avoid extrapolation beyond the feature space covered by training, as the SDSS spectroscopic sample is considerably shallower than KiDS. This gives 3.4 million objects in the final inference sample, from which the random forest identified 190,000 quasar candidates. Accuracy of 97%, purity of 91%, and completeness of 87%, as derived from a test set extracted from SDSS and not used in the training, are confirmed by comparison with external spectroscopic and photometric QSO catalogs overlapping with the KiDS footprint. The robustness of our results is strengthened by number counts of the quasar candidates in the r band, as well as by their mid-infrared colors available from WISE. An analysis of parallaxes and proper motions of our QSO candidates found also in Gaia DR2 suggests that a probability cut of p(QSO)>0.8 is optimal for purity, whereas p(QSO)>0.7 is preferable for better completeness. Our study presents the first comprehensive quasar selection from deep high-quality KiDS data and will serve as the basis for versatile studies of the QSO population detected by this survey.Comment: Data available from the KiDS website at http://kids.strw.leidenuniv.nl/DR3/quasarcatalog.php and the source code from https://github.com/snakoneczny/kids-quasar

A Semi-automatic Search for Giant Radio Galaxy Candidates and their Radio-Optical Follow-up

We present results of a search for giant radio galaxies (GRGs) with a projected largest linear size in excess of 1 Mpc. We designed a computational algorithm to identify contiguous emission regions, large and elongated enough to serve as GRG candidates, and applied it to the entire 1.4-GHz NRAO VLA Sky survey (NVSS). In a subsequent visual inspection of 1000 such regions we discovered 15 new GRGs, as well as many other candidate GRGs, some of them previously reported, for which no redshift was known. Our follow-up spectroscopy of 25 of the brighter hosts using two 2.1-m telescopes in Mexico, and four fainter hosts with the 10.4-m Gran Telescopio Canarias (GTC), yielded another 24 GRGs. We also obtained higher-resolution radio images with the Karl G. Jansky Very Large Array for GRG candidates with inconclusive radio structures in NVSS.Comment: 4 pages, 1 figure, to appear in the proceedings of The Universe of Digital Sky Surveys, Naples, Italy, Nov 25-28, 2014; Astrophysics and Space Science, eds. N.R. Napolitano et a

The dipole anisotropy of WISE x SuperCOSMOS number counts

We probe the isotropy of the Universe with the largest all-sky photometric redshift dataset currently available, namely WISE~$\times$~SuperCOSMOS. We search for dipole anisotropy of galaxy number counts in multiple redshift shells within the $0.10 < z < 0.35$ range, for two subsamples drawn from the same parent catalogue. Our results show that the dipole directions are in good agreement with most of the previous analyses in the literature, and in most redshift bins the dipole amplitudes are well consistent with $\Lambda$CDM-based mocks in the cleanest sample of this catalogue. In the $z<0.15$ range, however, we obtain a persistently large anisotropy in both subsamples of our dataset. Overall, we report no significant evidence against the isotropy assumption in this catalogue except for the lowest redshift ranges. The origin of the latter discrepancy is unclear, and improved data may be needed to explain it.Comment: 5 pages, 4 figures, 2 tables. Published in MNRA

Tracing dark energy with quasars

The nature of dark energy, driving the accelerated expansion of the Universe, is one of the most important issues in modern astrophysics. In order to understand this phenomenon, we need precise astrophysical probes of the universal expansion spanning wide redshift ranges. Quasars have recently emerged as such a probe, thanks to their high intrinsic luminosities and, most importantly, our ability to measure their luminosity distances independently of redshifts. Here we report our ongoing work on observational reverberation mapping using the time delay of the Mg II line, performed with the South African Large Telescope (SALT).Comment: 3 pages, 2 figures, submitted as PTA proceeding

Searching for galaxy clusters in the Kilo-Degree Survey

In this paper, we present the tools used to search for galaxy clusters in the Kilo Degree Survey (KiDS), and our first results. The cluster detection is based on an implementation of the optimal filtering technique that enables us to identify clusters as over-densities in the distribution of galaxies using their positions on the sky, magnitudes, and photometric redshifts. The contamination and completeness of the cluster catalog are derived using mock catalogs based on the data themselves. The optimal signal to noise threshold for the cluster detection is obtained by randomizing the galaxy positions and selecting the value that produces a contamination of less than 20%. Starting from a subset of clusters detected with high significance at low redshifts, we shift them to higher redshifts to estimate the completeness as a function of redshift: the average completeness is ~ 85%. An estimate of the mass of the clusters is derived using the richness as a proxy. We obtained 1858 candidate clusters with redshift 0 < z_c < 0.7 and mass 13.5 < log(M500/Msun) < 15 in an area of 114 sq. degrees (KiDS ESO-DR2). A comparison with publicly available Sloan Digital Sky Survey (SDSS)-based cluster catalogs shows that we match more than 50% of the clusters (77% in the case of the redMaPPer catalog). We also cross-matched our cluster catalog with the Abell clusters, and clusters found by XMM and in the Planck-SZ survey; however, only a small number of them lie inside the KiDS area currently available.Comment: 13 pages, 15 figures. Accepted for publication on Astronomy & Astrophysic