Transfer Learning in Large Spectroscopic Surveys

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985815%3A_____%2F22%3A00562021" target="_blank" >RIV/67985815:_____/22:00562021 - isvavai.cz</a>
Result on the web
<a href="http://www.aspbooks.org/publications/532/235.pdf" target="_blank" >http://www.aspbooks.org/publications/532/235.pdf</a>
DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Transfer Learning in Large Spectroscopic Surveys
Original language description
Transfer learning is a machine learning method that can reuse knowledge across spectroscopic archives with different distributions of observations. We applied transfer learning based on a convolutional neural network to spectra from Large Sky Area Multi-Object Fiber Spectroscopic Telescope and Sloan Digital Sky Survey archives. Taking advantage of known quasars in LAMOST DR5 version 3, we wanted to discover yet unseen quasars in SDSS DR14. Our transfer learning approach reaches 99.6% precision and 98.9% recall. We found examples of quasars previously classified as stars.
Czech name
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Czech description
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Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Astronomical Data Analysis Software and System XXX
ISBN
978-1-58381-934-0
ISSN
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e-ISSN
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Number of pages
4
Pages from-to
235-238
Publisher name
Astronomical Society of the Pacific
Place of publication
San Francisco
Event location
on-line
Event date
Nov 8, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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