2017A&A...606A..39S


C.D.S. - SIMBAD4 rel 1.7 - 2020.07.10CEST02:40:05

2017A&A...606A..39S - Astronomy and Astrophysics, volume 606A, 39-39 (2017/10-1)

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

SOLARZ A., BILICKI M., GROMADZKI M., POLLO A., DURKALEC A. and WYPYCH M.

Abstract (from CDS):

Wide-angle photometric surveys of previously uncharted sky areas or wavelength regimes will always bring in unexpected sources - novelties or even anomalies - whose existence and properties cannot be easily predicted from earlier observations. Such objects can be efficiently located 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. The OCSVM method 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 more 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.

Abstract Copyright: © ESO, 2017

Journal keyword(s): infrared: galaxies - infrared: stars - galaxies: statistics - stars: statistics - Galaxy: fundamental parameters - Galaxy: fundamental parameters

VizieR on-line data: <Available at CDS (J/A+A/606/A39): ocsvm_an.dat>

Status at CDS:   All or part of tables of objects will not be ingested in SIMBAD.

Simbad objects: 6

goto Full paper

goto View the reference in ADS

Number of rows : 6

N Identifier Otype ICRS (J2000)
RA
ICRS (J2000)
DEC
Mag U Mag B Mag V Mag R Mag I Sp type #ref
1850 - 2020
#notes
1 M 31 G 00 42 44.330 +41 16 07.50 4.86 4.36 3.44     ~ 10904 1
2 M 33 GiG 01 33 50.904 +30 39 35.79 6.17 6.27 5.72     ~ 5135 1
3 NAME Magellanic Clouds GrG 03 00 -71.0           ~ 5669 1
4 NAME Gal Anticenter reg 05 46 +28.9           ~ 720 0
5 NAME Gal Center reg 17 45 40.04 -29 00 28.1           ~ 11540 0
6 NAME Galactic Bulge reg ~ ~           ~ 3391 0

    Equat.    Gal    SGal    Ecl

To bookmark this query, right click on this link: simbad:objects in 2017A&A...606A..39S and select 'bookmark this link' or equivalent in the popup menu


2020.07.10-02:40:05

© Université de Strasbourg/CNRS

    • Contact