arXiv subjects

Mykyta Kliapets

Publications and source records attributed to Mykyta Kliapets.

5 recordsLinked to original sources

arXiv2026

Variability classification of TESS targets in LOPS2, the first long-term pointing field of PLATO. Version 1 of the public variability catalogue

The PLAnetary Transits and Oscillations of stars (PLATO) mission is expected to launch in January 2027. A total of 8\% of its data rate will be dedicated to complementary science targets selected from approved Guest Observer proposals. We seek to provide an open-source catalogue of variable stars in PLATO's first long-term observing field, LOPS2. We want to use existing observations from the Transiting Exoplanet Survey Satellite (TESS), which has observed many stars in LOPS2. We classified 38 million calibrated aperture light curves from the TESS-Gaia Light Curve pipeline (TGLC, $G\lesssim17$) for 6 million unique sources in LOPS2 with two machine learning frameworks -- a deep neural network and a feature-based gradient-boosted decision-tree ensemble. We combined their predictions to create this first version of the LOPS2 variability catalogue, performed manual vetting of a sub-sample classified light curves, and a statistical analysis of the results to validate our methodology and to assess the variability properties and parameters of the stars in the catalogue. Our classification resulted in the identification of approximately 72% of the light curves having dominant instrument- or pipeline-induced signal, with the remaining 28% representing 3.6 million individual candidate variable stars, including pulsating, rotating, and eclipsing stars. Candidate pulsators exhibit varied behaviour in terms of their frequencies, amplitudes, rotation, and fundamental parameters. To ensure purity of the samples, filtering on colour, luminosity, the dominant frequency and its amplitude, and presence of close neighbours is helpful. We provide the first version of our PLATO LOPS2 variability catalogue to the community for further study and scrutiny. It is to date one of the largest catalogues of variable stars from an automated classification pipeline.

astro-ph.SR↗

arXiv2026

ASTRAFier: A Novel and Scalable Transformer-based Stellar Variability Classifier

Photometric missions such as Kepler and TESS have generated millions of light curves covering almost the entire sky, offering unprecedented opportunities to study stellar variability and advance our understanding of the Universe. In this data-rich environment, machine learning has emerged as a powerful tool to efficiently and accurately process and classify light curves according to their type of stellar variability. In this work, we introduce ASTRAFier: a novel Transformer-based model for variability classification that integrates Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Neural Networks (CNNs). The model operates directly on time series without requiring feature engineering, creating an easy-to-maintain and efficient end-to-end classification framework. We train and validate our model using both Kepler and TESS light curves and, respectively, achieve a classification accuracy of $94.26\%$ on Kepler and $88.22\%$ on TESS. We demonstrate scalability by deploying our model on $\sim 2.8$ million TESS light curves from sectors 14, 15, and 26 (Kepler Field-of-View) delivered by MIT's Quick-look Pipeline (QLP) and release the resulting stellar variability catalog.

astro-ph.IM↗

arXiv2026

The PLATO Science Calibration and Validation Plan: Targets for the First Long-pointing Field

In order to meet the science goals of the PLATO space mission, an extensive science calibration and validation plan has been designed. This paper describes this plan, as well as the methodology adopted to select the science calibration and validation stars that have entered its input catalogue. This is the so-called {\tt scvPIC}, which is part of the general PLATO Input Catalogue (PIC) for the first selected long pointing field in the Southern Hemisphere known as LOPS2. While many of PLATO's science requirements needed dedicated stars as calibrators as discussed here, its most stringent requirement is the delivery of the age of the host stars of exoplanetary systems with an accuracy better than 10\% for a G0V star of {\it V} = 10 mag, i.e. a nearby Sun-like star. This is presently not within reach for large populations of dwarfs and subgiants in the Milky Way as it requires the models of their stellar interiors to be improved. We discuss how this ambitious age requirement led to the selection of tens of thousands of red giants, and of thousands of main-sequence early F-type gravity-mode pulsators in order to deduce their internal rotation profile across stellar evolution. This asteroseismic observable will then be imported as key information into improved models of dwarfs and subgiants in the Milky Way as optimal modelling tools for ever better age-dating of the exoplanet hosts as the PLATO mission moves along. Additional calibrators and validators included in the {\tt scvPIC} are a few thousands of binaries, a few hundreds of legacy and benchmark stars, a few hundred photometrically stable stars, and six transiting brown dwarfs.

astro-ph.SR↗

arXiv2025

Populations of tidal and pulsating variables in eclipsing binaries

In this work, we seek to characterise a large sample of 14377 main sequence eclipsing binaries in terms of their stellar, asteroseismic, and orbital properties. We conduct manual vetting on a 4000-target subset of our full 14377-target sample to identify targets with pressure or gravity modes. We infer stellar properties including the mass, convective core mass, radius, and central H fraction for the primary using Gaia Data Release 3 effective temperature and luminosity estimates and a grid of asteroseismically calibrated stellar models. We use surface brightness ratio and radius ratio estimates from previous eclipse analysis to study the effect of binarity on our results. Our manual vetting identifies 751 candidate g-mode pulsators, 131 p-mode pulsators, and a further 48 hybrid pulsators. The inferred stellar properties of the hybrid and p-mode pulsators are highly correlated, while the orbital properties of the hybrid pulsators align best with the g-mode pulsators. The g-mode pulsators themselves show a distribution that peaks around the classical g dor instability region but extends continuously towards higher masses, with no detectable divide between the classical g dor and SPB instability regions. There is evidence at the population level for a heightened level of tidal efficiency in stars showing g-mode or hybrid variability. Correcting the primary mass inference for binarity based on eclipse measurements of the surface brightness and radius ratios results in a relatively small shift towards lower masses. This work provides a working initial characterisation of this sample from which more detailed analyses folding in asteroseismic information can be built. It also provides a foundational understanding of the limitations and capabilities of this kind of rapid, scalable analysis that will be highly relevant in planning the exploitation of future large-scale binary surveys.

astro-ph.SR↗

arXiv2025

Automated all-sky detection of γ Doradus / δ Scuti hybrids in TESS data from positive unlabelled (PU) learning

The Transiting Exoplanet Survey Satellite (TESS) mission has observed hundreds of millions of stars, substantially contributing to the available pool of high-precision photometric space data. Among them are the relatively rare $γ$ Doradus / $δ$ Scuti ($γ$ Dor / $δ$ Sct) hybrid pulsators, which have been previously studied using Kepler data. These stars are perfect laboratories to probe both inner and outer interior stellar layers thanks to them exhibiting both pressure and gravity modes. We seek to classify an all-sky sample of AF stars observed by TESS to find previously undiscovered hybrid pulsators and supply them in a catalogue of candidates. We also aim to compare the light curves produced with the TESS-Gaia Light Curve (TGLC) pipeline, currently underused in variability studies, with other publicly available light curves. We compared dominant and secondary frequencies of confirmed hybrid pulsators in Kepler, extended mission Quick Look Pipeline (QLP) data, and nominal and extended mission TGLC data. We then used a feature-based positive unlabelled (PU) learning classifier to search for new hybrid pulsators amongst TESS AF stars and investigated the properties of the detected populations. We find that the variability of confirmed hybrids in TGLC agrees well with the one occurring in QLP light curves and has a high recovery rate of \kepler-extracted frequencies. Our `smart binning' method allows for robust extraction of hybrids from large unlabelled datasets, with an average out-of-bag prediction for test set hybrids at 93.04\%. The analysis of dominant frequencies in high-probability candidates shows that we find more pressure-mode dominant hybrids. Our catalogue includes 62,026 new candidate light curves from the nominal and extended TESS missions, with individual probabilities of being a hybrid in each available sector.

astro-ph.SR↗