O'TRAIN: A robust and flexible 'real or bogus' classifier for the study of the optical transient sky
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68378271%3A_____%2F22%3A00568212" target="_blank" >RIV/68378271:_____/22:00568212 - isvavai.cz</a>
Result on the web
<a href="https://hdl.handle.net/11104/0339546" target="_blank" >https://hdl.handle.net/11104/0339546</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1051/0004-6361/202142952" target="_blank" >10.1051/0004-6361/202142952</a>
Alternative languages
Result language
angličtina
Original language name
O'TRAIN: A robust and flexible 'real or bogus' classifier for the study of the optical transient sky
Original language description
Deep machine learning algorithms have now proven their efficiency in recognising patterns in images. These methods are now used in astrophysics to perform different classification tasks such as identifying bogus from real transient point-like sources. We explore this method to provide a robust and flexible algorithm that could be included in any kind of transient detection pipeline. We built a convolutional neural network (CNN) algorithm in order to perform a `real or bogus' classification task on transient candidate cutouts (subtraction residuals) provided by different kinds of optical telescopes. The training involved human-supervised labelling of the cutouts, which are split into two balanced data sets with `true' and `false' point-like source candidates. We tested our CNN model on the candidates produced by two different transient detection pipelines. In addition, we made use of several diagnostic tools to evaluate the classification performance of our CNN models.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10308 - Astronomy (including astrophysics,space science)
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Astronomy & Astrophysics
ISSN
0004-6361
e-ISSN
1432-0746
Volume of the periodical
664
Issue of the periodical within the volume
Aug
Country of publishing house
FR - FRANCE
Number of pages
18
Pages from-to
A81
UT code for WoS article
000838257200006
EID of the result in the Scopus database
2-s2.0-85136432628