Identification of Interesting Objects in Large Spectral Surveys Using Highly Parallelized Machine Learning

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985815%3A_____%2F17%3A00485674" target="_blank" >RIV/67985815:_____/17:00485674 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1017/S1743921317000047" target="_blank" >http://dx.doi.org/10.1017/S1743921317000047</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1017/S1743921317000047" target="_blank" >10.1017/S1743921317000047</a>

Result language
angličtina
Original language name
Identification of Interesting Objects in Large Spectral Surveys Using Highly Parallelized Machine Learning
Original language description
We present results of Spark-based semi-supervised machine learning of LAMOST spectra attempting to automatically identify the single and double-peak emission of H.alpha. line typical for Be and B[e] stars. The labelled sample was obtained from archive of 2m Perek telescope at Ondřejov observatory. A simple physical model of spectrograph resolution was used in domain adaptation to LAMOST training domain. The resulting list of candidates contains dozens of Be stars (some are likely yet unknown), but also a bunch of interesting objects resembling spectra of quasars and even blazars, as well as many instrumental artefacts. The verification of a nature of interesting candidates benefited considerably from cross-matching and visualisation in the Virtual Observatory environment.
Czech name
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Czech description
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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
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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