Light curve modelling of close eclipsing binaries

Fedurco, M.; Čokina, M.; Parimucha, Š.

doi:10.31577/caosp.2020.50.2.563

Cited by 2 publications

(2 citation statements)

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“…As it is difficult to obtain a sufficient number of data set objects by annotating observational data of eclipsing binaries, we used the software ELISa (Fedurco et al, 2020;Čokina et al, 2021) (created by the co-authors of our paper), which is available online 1 .…”

Section: Synthetic Data Generator For Light Curves -Elisamentioning

confidence: 99%

Automatic classification of eclipsing binary stars using deep learning methods

Čokina,

Maslej-Krešňáková,

Butka

et al. 2021

Preprint

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In the last couple of decades, tremendous progress has been achieved in developing robotic telescopes and, as a result, sky surveys (both terrestrial and space) have become the source of a substantial amount of new observational data. These data contain a lot of information about binary stars, hidden in their light curves. With the huge amount of astronomical data gathered, it is not reasonable to expect all the data to be manually processed and analyzed. Therefore, in this paper, we focus on the automatic classification of eclipsing binary stars using deep learning methods. Our classifier provides a tool for the categorization of light curves of binary stars into two classes: detached and over-contact. We used the ELISa software to obtain synthetic data, which we then used for the training of the classifier. For evaluation purposes, we collected 100 light curves of observed binary stars, in order to evaluate a number of classifiers. We evaluated semi-detached eclipsing binary stars as detached.The best-performing classifier combines bidirectional Long Short-Term Memory (LSTM) and a one-dimensional convolutional neural network, which achieved 98% accuracy on the evaluation set. Omitting semi-detached eclipsing binary stars, we could obtain 100% accuracy in classification.

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Section: Synthetic Data Generator For Light Curves -Elisamentioning

confidence: 99%

Automatic classification of eclipsing binary stars using deep learning methods

Čokina,

Maslej-Krešňáková,

Butka

et al. 2021

Preprint

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show abstract

“…The CCD images were reduced in the usual way (bias and dark subtraction, flat-field correction) utilizing the software package CoLiTecVS (Savanevych et al 2017;Parimucha et al 2019). This package was also used for aperture photometry, calculation of differential magnitudes according to artificial comparison star as well as calibration to the standard photometric system.…”

Section: Introductionmentioning

confidence: 99%

A photometric study of two neglected eclipsing binaries

Кудак

Fedurco

Perig

et al. 2021

Res. Astron. Astrophys.

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We present the first BVR photometry, period variation and photometric light curve analysis of two poorly studied eclipsing binaries, V1321 Cyg and CR Tau. Observations were carried out from November 2017 to January 2020 at the observatory of Uzhhorod National University. Period variations were studied using all available early published as well as our minima times. We used the newly developed ELISa code for the light curve analysis and determination of photometric parameters for both systems. We found that V1321 Cyg is a close detached eclipsing system with a low photometric mass ratio of q = 0.28 which suggests that the binary is a post-mass transfer system. No significant period changes in this system are detected. CR Tau is, on the other hand, a semi-detached system where the secondary component almost fills its Roche lobe. We detected a long-term period increase at a rate of 1.49 × 10−7 d yr−1, which supports mass transfer from the lower mass secondary component to the more massive primary.

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Light curve modelling of close eclipsing binaries

Cited by 2 publications

References 0 publications

Automatic classification of eclipsing binary stars using deep learning methods

Automatic classification of eclipsing binary stars using deep learning methods

A photometric study of two neglected eclipsing binaries

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