Active deep learning method for the discovery of objects of interest in large spectroscopic surveys

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985815%3A_____%2F20%3A00537357" target="_blank" >RIV/67985815:_____/20:00537357 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1051/0004-6361/201936090" target="_blank" >https://doi.org/10.1051/0004-6361/201936090</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1051/0004-6361/201936090" target="_blank" >10.1051/0004-6361/201936090</a>

Result language
angličtina
Original language name
Active deep learning method for the discovery of objects of interest in large spectroscopic surveys
Original language description
After several iterations, the network was able to successfully identify emission-line stars with an error smaller than 6.5%. Using the technology of the Virtual Observatory to visualise the results, we discovered 1013 spectra of 948 new candidates of emission-line objects in addition to 664 spectra of 549 objects that are listed in SIMBAD and 2644 spectra of 2291 objects identified in an earlier paper of a Chinese group led by Wen Hou. The most interesting objects with unusual spectral properties are discussed in detail.
Czech name
—
Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
10308 - Astronomy (including astrophysics,space science)

Project
<a href="/en/project/LD15113" target="_blank" >LD15113: Applications of Artificial Intelligence in Astronomy</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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