O'TRAIN: A robust and flexible 'real or bogus' classifier for the study of the optical transient sky

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

O'TRAIN: A robust and flexible 'real or bogus' classifier for the study of the optical transient sky

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

O'TRAIN: A robust and flexible 'real or bogus' classifier for the study of the optical transient sky

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10308 - Astronomy (including astrophysics,space science)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Astronomy & Astrophysics

ISSN

0004-6361

e-ISSN

1432-0746

Svazek periodika

664

Číslo periodika v rámci svazku

Aug

Stát vydavatele periodika

FR - Francouzská republika

Počet stran výsledku

18

Strana od-do

A81

Kód UT WoS článku

000838257200006

EID výsledku v databázi Scopus

2-s2.0-85136432628