Spectroscopic observations of the machine-learning selected anomaly catalogue from the AllWISE Sky Survey

Autores
Solarz, Aleksandra; Thomas, Romain; Montenegro Montes, Francisco; Gromadzki, Mariusz; Donoso, Emilio; Koprowski, Maciej; Wyrzykowski, Lukasz; Diaz, Carlos Gonzalo; Sani, Eleonora; Bilicki, Maciej Andrzej
Año de publicación
2020
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
We present the results of a programme to search and identify the nature of unusual sources within the All-sky Wide-field Infrared Survey Explorer (WISE) that is based on a machine-learning algorithm for anomaly detection, namely one-class support vector machines (OCSVM). Designed to detect sources deviating from a training set composed of known classes, this algorithm was used to create a model for the expected data based on WISE objects with spectroscopic identifications in the Sloan Digital Sky Survey. Subsequently, it marked as anomalous those sources whose WISE photometry was shown to be inconsistent with this model. We report the results from optical and near-infrared spectroscopy follow-up observations of a subset of 36 bright (gAB < 19.5) objects marked as "anomalous"by the OCSVM code to verify its performance. Among the observed objects, we identified three main types of sources: (i) low redshift (z ∼ 0.03 - 0.15) galaxies containing large amounts of hot dust (53%), including three Wolf-Rayet galaxies; (ii) broad-line quasi-stellar objects (QSOs) (33%) including low-ionisation broad absorption line (LoBAL) quasars and a rare QSO with strong and narrow ultraviolet iron emission; (iii) Galactic objects in dusty phases of their evolution (3%). The nature of four of these objects (11%) remains undetermined due to low signal-to-noise or featureless spectra. The current data show that the algorithm works well at detecting rare but not necessarily unknown objects among the brightest candidates. They mostly represent peculiar sub-types of otherwise well-known sources. To search for even more unusual sources, a more complete and balanced training set should be created after including these rare sub-species of otherwise abundant source classes, such as LoBALs. Such an iterative approach will ideally bring us closer to improving the strategy design for the detection of rarer sources contained within the vast data store of the AllWISE survey.
Fil: Solarz, Aleksandra. European Southern Observatory; Chile
Fil: Thomas, Romain. European Southern Observatory; Chile
Fil: Montenegro Montes, Francisco. European Southern Observatory; Chile
Fil: Gromadzki, Mariusz. University of Warsaw. Astronomical Observatory; Polonia
Fil: Donoso, Emilio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Ciencias Astronómicas, de la Tierra y del Espacio. Universidad Nacional de San Juan. Instituto de Ciencias Astronómicas, de la Tierra y del Espacio; Argentina
Fil: Koprowski, Maciej. Nicolaus Copernicus. Faculty of Physics, Astronomy and Informatics. Institute of Astronomy; Polonia
Fil: Wyrzykowski, Lukasz. University of Warsaw. Astronomical Observatory; Polonia
Fil: Diaz, Carlos Gonzalo. Gemini Observatory. Southern Operations Center; Chile. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Ciencias Astronómicas, de la Tierra y del Espacio. Universidad Nacional de San Juan. Instituto de Ciencias Astronómicas, de la Tierra y del Espacio; Argentina
Fil: Sani, Eleonora. European Southern Observatory ; Chile
Fil: Bilicki, Maciej Andrzej. Polish Academy of Sciences; Argentina
Materia
GALAXIES: ACTIVE
INFRARED: GALAXIES
INFRARED: STARS
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/140895
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Spectroscopic observations of the machine-learning selected anomaly catalogue from the AllWISE Sky SurveySolarz, AleksandraThomas, RomainMontenegro Montes, FranciscoGromadzki, MariuszDonoso, EmilioKoprowski, MaciejWyrzykowski, LukaszDiaz, Carlos GonzaloSani, EleonoraBilicki, Maciej AndrzejGALAXIES: ACTIVEINFRARED: GALAXIESINFRARED: STARShttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1We present the results of a programme to search and identify the nature of unusual sources within the All-sky Wide-field Infrared Survey Explorer (WISE) that is based on a machine-learning algorithm for anomaly detection, namely one-class support vector machines (OCSVM). Designed to detect sources deviating from a training set composed of known classes, this algorithm was used to create a model for the expected data based on WISE objects with spectroscopic identifications in the Sloan Digital Sky Survey. Subsequently, it marked as anomalous those sources whose WISE photometry was shown to be inconsistent with this model. We report the results from optical and near-infrared spectroscopy follow-up observations of a subset of 36 bright (gAB < 19.5) objects marked as "anomalous"by the OCSVM code to verify its performance. Among the observed objects, we identified three main types of sources: (i) low redshift (z ∼ 0.03 - 0.15) galaxies containing large amounts of hot dust (53%), including three Wolf-Rayet galaxies; (ii) broad-line quasi-stellar objects (QSOs) (33%) including low-ionisation broad absorption line (LoBAL) quasars and a rare QSO with strong and narrow ultraviolet iron emission; (iii) Galactic objects in dusty phases of their evolution (3%). The nature of four of these objects (11%) remains undetermined due to low signal-to-noise or featureless spectra. The current data show that the algorithm works well at detecting rare but not necessarily unknown objects among the brightest candidates. They mostly represent peculiar sub-types of otherwise well-known sources. To search for even more unusual sources, a more complete and balanced training set should be created after including these rare sub-species of otherwise abundant source classes, such as LoBALs. Such an iterative approach will ideally bring us closer to improving the strategy design for the detection of rarer sources contained within the vast data store of the AllWISE survey.Fil: Solarz, Aleksandra. European Southern Observatory; ChileFil: Thomas, Romain. European Southern Observatory; ChileFil: Montenegro Montes, Francisco. European Southern Observatory; ChileFil: Gromadzki, Mariusz. University of Warsaw. Astronomical Observatory; PoloniaFil: Donoso, Emilio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Ciencias Astronómicas, de la Tierra y del Espacio. Universidad Nacional de San Juan. Instituto de Ciencias Astronómicas, de la Tierra y del Espacio; ArgentinaFil: Koprowski, Maciej. Nicolaus Copernicus. Faculty of Physics, Astronomy and Informatics. Institute of Astronomy; PoloniaFil: Wyrzykowski, Lukasz. University of Warsaw. Astronomical Observatory; PoloniaFil: Diaz, Carlos Gonzalo. Gemini Observatory. Southern Operations Center; Chile. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Ciencias Astronómicas, de la Tierra y del Espacio. Universidad Nacional de San Juan. Instituto de Ciencias Astronómicas, de la Tierra y del Espacio; ArgentinaFil: Sani, Eleonora. European Southern Observatory ; ChileFil: Bilicki, Maciej Andrzej. Polish Academy of Sciences; ArgentinaEDP Sciences2020-10-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/140895Solarz, Aleksandra; Thomas, Romain; Montenegro Montes, Francisco; Gromadzki, Mariusz; Donoso, Emilio; et al.; Spectroscopic observations of the machine-learning selected anomaly catalogue from the AllWISE Sky Survey; EDP Sciences; Astronomy and Astrophysics; 642; A103; 12-10-2020; 1-170004-6361CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1051/0004-6361/202038439info:eu-repo/semantics/altIdentifier/url/https://www.aanda.org/articles/aa/full_html/2020/10/aa38439-20/aa38439-20.htmlinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:44:17Zoai:ri.conicet.gov.ar:11336/140895instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:44:17.956CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Spectroscopic observations of the machine-learning selected anomaly catalogue from the AllWISE Sky Survey
title Spectroscopic observations of the machine-learning selected anomaly catalogue from the AllWISE Sky Survey
spellingShingle Spectroscopic observations of the machine-learning selected anomaly catalogue from the AllWISE Sky Survey
Solarz, Aleksandra
GALAXIES: ACTIVE
INFRARED: GALAXIES
INFRARED: STARS
title_short Spectroscopic observations of the machine-learning selected anomaly catalogue from the AllWISE Sky Survey
title_full Spectroscopic observations of the machine-learning selected anomaly catalogue from the AllWISE Sky Survey
title_fullStr Spectroscopic observations of the machine-learning selected anomaly catalogue from the AllWISE Sky Survey
title_full_unstemmed Spectroscopic observations of the machine-learning selected anomaly catalogue from the AllWISE Sky Survey
title_sort Spectroscopic observations of the machine-learning selected anomaly catalogue from the AllWISE Sky Survey
dc.creator.none.fl_str_mv Solarz, Aleksandra
Thomas, Romain
Montenegro Montes, Francisco
Gromadzki, Mariusz
Donoso, Emilio
Koprowski, Maciej
Wyrzykowski, Lukasz
Diaz, Carlos Gonzalo
Sani, Eleonora
Bilicki, Maciej Andrzej
author Solarz, Aleksandra
author_facet Solarz, Aleksandra
Thomas, Romain
Montenegro Montes, Francisco
Gromadzki, Mariusz
Donoso, Emilio
Koprowski, Maciej
Wyrzykowski, Lukasz
Diaz, Carlos Gonzalo
Sani, Eleonora
Bilicki, Maciej Andrzej
author_role author
author2 Thomas, Romain
Montenegro Montes, Francisco
Gromadzki, Mariusz
Donoso, Emilio
Koprowski, Maciej
Wyrzykowski, Lukasz
Diaz, Carlos Gonzalo
Sani, Eleonora
Bilicki, Maciej Andrzej
author2_role author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv GALAXIES: ACTIVE
INFRARED: GALAXIES
INFRARED: STARS
topic GALAXIES: ACTIVE
INFRARED: GALAXIES
INFRARED: STARS
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.3
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv We present the results of a programme to search and identify the nature of unusual sources within the All-sky Wide-field Infrared Survey Explorer (WISE) that is based on a machine-learning algorithm for anomaly detection, namely one-class support vector machines (OCSVM). Designed to detect sources deviating from a training set composed of known classes, this algorithm was used to create a model for the expected data based on WISE objects with spectroscopic identifications in the Sloan Digital Sky Survey. Subsequently, it marked as anomalous those sources whose WISE photometry was shown to be inconsistent with this model. We report the results from optical and near-infrared spectroscopy follow-up observations of a subset of 36 bright (gAB < 19.5) objects marked as "anomalous"by the OCSVM code to verify its performance. Among the observed objects, we identified three main types of sources: (i) low redshift (z ∼ 0.03 - 0.15) galaxies containing large amounts of hot dust (53%), including three Wolf-Rayet galaxies; (ii) broad-line quasi-stellar objects (QSOs) (33%) including low-ionisation broad absorption line (LoBAL) quasars and a rare QSO with strong and narrow ultraviolet iron emission; (iii) Galactic objects in dusty phases of their evolution (3%). The nature of four of these objects (11%) remains undetermined due to low signal-to-noise or featureless spectra. The current data show that the algorithm works well at detecting rare but not necessarily unknown objects among the brightest candidates. They mostly represent peculiar sub-types of otherwise well-known sources. To search for even more unusual sources, a more complete and balanced training set should be created after including these rare sub-species of otherwise abundant source classes, such as LoBALs. Such an iterative approach will ideally bring us closer to improving the strategy design for the detection of rarer sources contained within the vast data store of the AllWISE survey.
Fil: Solarz, Aleksandra. European Southern Observatory; Chile
Fil: Thomas, Romain. European Southern Observatory; Chile
Fil: Montenegro Montes, Francisco. European Southern Observatory; Chile
Fil: Gromadzki, Mariusz. University of Warsaw. Astronomical Observatory; Polonia
Fil: Donoso, Emilio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Ciencias Astronómicas, de la Tierra y del Espacio. Universidad Nacional de San Juan. Instituto de Ciencias Astronómicas, de la Tierra y del Espacio; Argentina
Fil: Koprowski, Maciej. Nicolaus Copernicus. Faculty of Physics, Astronomy and Informatics. Institute of Astronomy; Polonia
Fil: Wyrzykowski, Lukasz. University of Warsaw. Astronomical Observatory; Polonia
Fil: Diaz, Carlos Gonzalo. Gemini Observatory. Southern Operations Center; Chile. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Ciencias Astronómicas, de la Tierra y del Espacio. Universidad Nacional de San Juan. Instituto de Ciencias Astronómicas, de la Tierra y del Espacio; Argentina
Fil: Sani, Eleonora. European Southern Observatory ; Chile
Fil: Bilicki, Maciej Andrzej. Polish Academy of Sciences; Argentina
description We present the results of a programme to search and identify the nature of unusual sources within the All-sky Wide-field Infrared Survey Explorer (WISE) that is based on a machine-learning algorithm for anomaly detection, namely one-class support vector machines (OCSVM). Designed to detect sources deviating from a training set composed of known classes, this algorithm was used to create a model for the expected data based on WISE objects with spectroscopic identifications in the Sloan Digital Sky Survey. Subsequently, it marked as anomalous those sources whose WISE photometry was shown to be inconsistent with this model. We report the results from optical and near-infrared spectroscopy follow-up observations of a subset of 36 bright (gAB < 19.5) objects marked as "anomalous"by the OCSVM code to verify its performance. Among the observed objects, we identified three main types of sources: (i) low redshift (z ∼ 0.03 - 0.15) galaxies containing large amounts of hot dust (53%), including three Wolf-Rayet galaxies; (ii) broad-line quasi-stellar objects (QSOs) (33%) including low-ionisation broad absorption line (LoBAL) quasars and a rare QSO with strong and narrow ultraviolet iron emission; (iii) Galactic objects in dusty phases of their evolution (3%). The nature of four of these objects (11%) remains undetermined due to low signal-to-noise or featureless spectra. The current data show that the algorithm works well at detecting rare but not necessarily unknown objects among the brightest candidates. They mostly represent peculiar sub-types of otherwise well-known sources. To search for even more unusual sources, a more complete and balanced training set should be created after including these rare sub-species of otherwise abundant source classes, such as LoBALs. Such an iterative approach will ideally bring us closer to improving the strategy design for the detection of rarer sources contained within the vast data store of the AllWISE survey.
publishDate 2020
dc.date.none.fl_str_mv 2020-10-12
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/140895
Solarz, Aleksandra; Thomas, Romain; Montenegro Montes, Francisco; Gromadzki, Mariusz; Donoso, Emilio; et al.; Spectroscopic observations of the machine-learning selected anomaly catalogue from the AllWISE Sky Survey; EDP Sciences; Astronomy and Astrophysics; 642; A103; 12-10-2020; 1-17
0004-6361
CONICET Digital
CONICET
url http://hdl.handle.net/11336/140895
identifier_str_mv Solarz, Aleksandra; Thomas, Romain; Montenegro Montes, Francisco; Gromadzki, Mariusz; Donoso, Emilio; et al.; Spectroscopic observations of the machine-learning selected anomaly catalogue from the AllWISE Sky Survey; EDP Sciences; Astronomy and Astrophysics; 642; A103; 12-10-2020; 1-17
0004-6361
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1051/0004-6361/202038439
info:eu-repo/semantics/altIdentifier/url/https://www.aanda.org/articles/aa/full_html/2020/10/aa38439-20/aa38439-20.html
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv EDP Sciences
publisher.none.fl_str_mv EDP Sciences
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.070432

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