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J/A+A/606/A39           OCSVM anomalies                          (Solarz+, 2017)

Automated novelty detection in the WISE survey with one-class support vector machines. Solarz A., Bilicki M., Gromadzki M., Pollo A., Durkalec A., Wypych M. <Astron. Astrophys. 606, A39 (2017)> =2017A&A...606A..39S (SIMBAD/NED BibCode)
ADC_Keywords: Galaxies, IR ; Active gal. nuclei ; Photometry, infrared Keywords: infrared: galaxies - infrared: stars - galaxies: statistics - stars: statistics - Galaxy: fundamental parameters Abstract: 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. Description: One table containing 642,353 sources selected as anomalous with one-class support vector machine algorithm in AllWISE data release. Data have AllWISE photometry in W1, W2 and W3 passband and include W3 flux correction described in Krakowski et al. (2016A&A...596A..39K). File Summary:
FileName Lrecl Records Explanations
ReadMe 80 . This file ocsvm_an.dat 103 642353 OCSVM anomalies
See also: II/328 : AllWISE Data Release (Cutri+ 2013) Byte-by-byte Description of file: ocsvm_an.dat
Bytes Format Units Label Explanations
1- 19 A19 --- Name AllWISE identifier (JHHMMSS.ss+DDMMSS.s) 21- 31 F11.7 deg RAdeg Right ascension (J2000.0) 33- 43 F11.7 deg DEdeg Declination (J2000.0) 45- 50 F6.3 mag W1mag Instrumental profile-fit photometry magnitude, band 1 52- 56 F5.3 mag e_W1mag Instrumental profile-fit photometry flux uncertainty in mag units, band 1 58- 63 F6.3 mag W2mag Instrumental profile-fit photometry magnitude, band 2 65- 69 F5.3 mag e_W2mag Instrumental profile-fit photometry flux uncertainty in mag units, band 2 71- 76 F6.3 mag W3mag ?=- Instrumental profile-fit photometry magnitude, band 3 78- 82 F5.3 mag e_W3mag ?=- Instrumental profile-fit photometry flux uncertainty in mag units, band 3 84- 89 F6.3 mag W1mag1 W1 5.5" radius aperture magnitude 91- 96 F6.3 mag W1mag3 W1 11.0" radius aperture magnitude 98-103 F6.3 mag W3corr ?=- Correction to W3 mag for objects with W3 upper limits only (W3mag+0.75)
Acknowledgements: Aleksandra Solarz, aleksandra.solarz(at)ncbj.gov.pl
(End) Aleksandra Solarz [NCBJ, Poland], Patricia Vannier [CDS] 04-Jul-2017
The document above follows the rules of the Standard Description for Astronomical Catalogues.From this documentation it is possible to generate f77 program to load files into arrays or line by line

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