1,208 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
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 () 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~~SuperCOSMOS. We
search for dipole anisotropy of galaxy number counts in multiple redshift
shells within the 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 CDM-based
mocks in the cleanest sample of this catalogue. In the 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
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