1,480 research outputs found
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 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 () than the
obscured candidates (). 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-) and 2MASS Photo- datasets, as well as
employed earlier for ISW, SDSS QSO photo- and NVSS samples. Second, we use
for the first time the WISE~~SuperCOSMOS catalog, which allows us to
perform an all-sky analysis of the ISW up to . Third, thanks to the
use of photo-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 thus reaching, for the first time in such an analysis, the
threshold of 5 . Without the redshift tomography we find a significance
of 4.0 , 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
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