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

An Updated Tomographic Analysis of the Integrated Sachs-Wolfe Effect and implications for Dark Energy

We derive updated constraints on the Integrated Sachs-Wolfe (ISW) effect through cross-correlation of the cosmic microwave background with galaxy surveys. We improve with respect to similar previous analyses in several ways. First, we use the most recent versions of extragalactic object catalogs: SDSS DR12 photometric redshift (photo-$z$) and 2MASS Photo-$z$ datasets, as well as employed earlier for ISW, SDSS QSO photo-$z$ and NVSS samples. Second, we use for the first time the WISE~$\times$~SuperCOSMOS catalog, which allows us to perform an all-sky analysis of the ISW up to $z\sim0.4$. Third, thanks to the use of photo-$z$s, we separate each dataset into different redshift bins, deriving the cross-correlation in each bin. This last step leads to a significant improvement in sensitivity. We remove cross-correlation between catalogs using masks which mutually exclude common regions of the sky. We use two methods to quantify the significance of the ISW effect. In the first one, we fix the cosmological model, derive linear galaxy biases of the catalogs, and then evaluate the significance of the ISW using a single parameter. In the second approach we perform a global fit of the ISW and of the galaxy biases varying the cosmological model. We find significances of the ISW in the range 4.7-5.0 $\sigma$ thus reaching, for the first time in such an analysis, the threshold of 5 $\sigma$. Without the redshift tomography we find a significance of $\sim$ 4.0 $\sigma$, which shows the importance of the binning method. Finally we use the ISW data to infer constraints on the Dark Energy redshift evolution and equation of state. We find that the redshift range covered by the catalogs is still not optimal to derive strong constraints, although this goal will be likely reached using future datasets such as from Euclid, LSST, and SKA.Comment: 16 pages, 6 figures, 8 tables, 2 appendices; v2: minor changes, matches version published in PRD; ISW likelihood code is available within the new release of MontePython (see arXiv:1804.07261

Is the Two Micron all Sky Survey Clustering Dipole Convergent?

There is a long-standing controversy about the convergence of the dipole moment of the galaxy angular distribution (the so-called clustering dipole). Is the dipole convergent at all, and if so, what is the scale of the convergence? We study the growth of the clustering dipole of galaxies as a function of the limiting flux of the sample from the Two Micron All Sky Survey (2MASS). Contrary to some earlier claims, we find that the dipole does not converge before the completeness limit of the 2MASS Extended Source Catalog, i.e., up to 13.5 mag in the near-infrared K_s band (equivalent to an effective distance of 300 Mpc h ^(−1)). We compare the observed growth of the dipole with the theoretically expected, conditional one (i.e., given the velocity of the Local Group relative to the cosmic microwave background), for the ΛCDM power spectrum and cosmological parameters constrained by the Wilkinson Microwave Anisotropy Probe. The observed growth turns out to be within 1σ confidence level of its theoretical counterpart once the proper observational window of the 2MASS flux-limited catalog is included. For a contrast, if the adopted window is a top hat, then the predicted dipole grows significantly faster and converges (within the errors) to its final value for a distance of about 300 Mpc h ^(−1). By comparing the observational windows, we show that for a given flux limit and a corresponding distance limit, the 2MASS flux-weighted window passes less large-scale signal than the top-hat one. We conclude that the growth of the 2MASS dipole for effective distances greater than 200 Mpc h^(−1) is only apparent. On the other hand, for a distance of 80 Mpc h^(−1) (mean depth of the 2MASS Redshift Survey) and the ΛCDM power spectrum, the true dipole is expected to reach only ~80% of its final value. Eventually, since for the window function of 2MASS the predicted growth is consistent with the observed one, we can compare the two to evaluate β ≡ Ω^(0.55)_m /b. The result is β = 0.38 ± 0.04, which leads to an estimate of the density parameter Ω_m = 0.20 ± 0.08

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