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We discuss prospects of identifying and characterizing black hole (BH) companions to normal stars on tight but detached orbits, using photometric data from the Transiting Exoplanet Survey Satellite (TESS). We focus on the following two…
Circumbinary planets and brown dwarfs form in complex gravitational environments, offering insights into formation, orbital stability, and habitability prospects. However, they remain underrepresented, with only 60 confirmed or candidate…
We present a simple, differentiable method for predicting emission line strengths from rest-frame optical continua using an empirically-determined mapping. Extensive work has been done to develop mock galaxy catalogues that include robust…
In the identification of new planetary candidates in transit surveys, the employment of Deep Learning models proved to be essential to efficiently analyse a continuously growing volume of photometric observations. To further improve the…
Theories of stellar convective core overshoot can be examined through analysis of pulsating stars. Better accuracy can be achieved by obtaining external constraints such as those provided by observing pulsating stars in eclipsing binary…
The discovery of exoplanets has expanded our understanding of planetary systems and opened new avenues for astronomical research. In this study, we present a machine learning (ML) framework for exoplanet identification using a time-series…
In this work, we apply an exploratory joint Bayesian transit detector (Taaki et al. 2020), previously evaluated using Kepler data, to the 2 min simple aperture photometry light curve data in the continuous viewing zone for the Transiting…
With only two functional reaction wheels, Kepler cannot maintain stable pointing at its original target field and entered a new mode of observation called K2. Our method is based on many years of experience in planet hunting for the CoRoT…
We develop a novel method based on machine learning principles to achieve optimal initiation of CPU-intensive computations for forward asteroseismic modeling in a multi-D parameter space. A deep neural network is trained on a precomputed…
Due to the recent increase in the availability of photometric time-series databases, the characterisation of low-mass eclipsing binaries for the study of their orbital and physical parameters is now possible in large samples and with good…
This work investigates the spectrum parameterization problem using deep neural networks (DNNs). The proposed scheme consists of the following procedures: first, the configuration of a DNN is initialized using a series of autoencoder neural…
It has recently been demonstrated that deep learning has significant potential to automate parts of the exoplanet detection pipeline using light curve data from satellites such as Kepler \cite{borucki2010kepler} \cite{koch2010kepler} and…
Light curves produced by wide-field exoplanet transit surveys such as CoRoT, Kepler, and TESS are affected by sensor-wide systematic noise which is correlated both spatiotemporally and with other instrumental parameters such as photometric…
Twenty eclipsing binaries were selected for an analysis from a huge database of observations made by the INTEGRAL/OMC camera. The photometric data were processed and analyzed, resulting in a first light-curve study of these neglected…
Detached eclipsing binary stars (DEBS) are currently the best source of accurate and precise fundamental stellar parameters. This makes DEBS crucial targets for constraining the impact of various physical processes on stellar structure and…
Light curve analysis of W UMa-type contact binary systems using MCMC or MC methods can be time-consuming, primarily because the repeated generation of synthetic light curves tends to be relatively slow during the fitting process. Although…
Exoplanetary atmospheric observations require an exquisite precision in the measurement of the relative flux among wavelengths. In this paper, we aim to provide a new adaptive method to treat light curves before fitting transit parameters…
We present the results of an automated variability analysis of the Kepler public data measured in the first quarter (Q1) of the mission. In total, about 150 000 light curves have been analysed to detect stellar variability, and to identify…
Spotted stars in eclipsing binary systems allow us to gather significant information about the stellar surface inhomogeneities that is otherwise impossible from only photometric data. Starspots can be scanned using the eclipse (or transit)…
We have begun a programme to obtain high-precision photometry of bright detached eclipsing binary (dEB) stars with the Wide field InfraRed Explorer (WIRE) satellite. Due to the small aperture of WIRE only stars brighter than V=6 can be…