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Table · dataset · 2023

Classification of elusive astronomical objects

Listed in IVOA Registry (Virtual Observatory)

The application of machine learning principles in the photometric search of elusive astronomical objects has been a less-explored frontier of research.

Description

Here, we have used three methods, the neural network and two variants of k-nearest neighbour, to identify brown dwarf candidates using the photometric colours of known brown dwarfs. We initially check the efficiencies of these three classification techniques, both individually and collectively, on known objects.

This is followed by their application to three regions in the sky, namely Hercules (2{deg}x2{deg}), Serpens (9{deg}x4{deg}), and Lyra (2{deg}x2{deg}). Testing these algorithms on sets of objects that include known brown dwarfs show a high level of completeness. This includes the Hercules and Serpens regions where brown dwarfs have been detected.

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We use these methods to search and identify brown dwarf candidates towards the Lyra region. We infer that the collective method of classification, also known as ensemble classifier, is highly efficient in the identification of brown dwarf candidates.

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Stated by source
Infrared
Provenance · 1 source records, 8 field assertions
SourceKeyLast seenRaw
IVOA Registry (Virtual Observatory)ivo://cds.vizier/j/mnras/488/226310 d agoJSON v1
FieldAssertionExtractorEvidence
access_levelsource · IVOA Registryconnector:ivoa_registry@1.0.0
concepts[field].local:field:astronomymapping · IVOA Registryconnector:ivoa_registry@1.0.0
concepts[modality].ivoa_waveband:infraredsource · IVOA Registryconnector:ivoa_registry@1.0.0
descriptionsource · IVOA Registryconnector:ivoa_registry@1.0.0rr.resource.res_description
license_textsource · IVOA Registryconnector:ivoa_registry@1.0.0
publication_datesource · IVOA Registryconnector:ivoa_registry@1.0.0
titlesource · IVOA Registryconnector:ivoa_registry@1.0.0rr.resource.res_title
updated_datesource · IVOA Registryconnector:ivoa_registry@1.0.0