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Register a new userSuperWASP Variable Stars: Classifying Light Curves Using Citizen Science
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We present the first analysis of results from the SuperWASP Variable Stars Zooniverse project, which is aiming to classify 1.6 million phase-folded light curves of candidate stellar variables observed by the SuperWASP all sky survey with periods detected in the SuperWASP periodicity catalogue. The resultant data set currently contains $>$1 million classifications corresponding to $>$500,000 object-period combinations, provided by citizen scientist volunteers. Volunteer-classified light curves have $sim$89 per cent accuracy for detached and semi-detached eclipsing binaries, but only $sim$9 per cent accuracy for rotationally modulated variables, based on known objects. We demonstrate that this Zooniverse project will be valuable for both population studies of individual variable types and the identification of stellar variables for follow up. We present preliminary findings on various unique and extreme variables in this analysis, including long period contact binaries and binaries near the short-period cutoff, and we identify 301 previously unknown binaries and pulsators. We are now in the process of developing a web portal to enable other researchers to access the outputs of the SuperWASP Variable Stars project.
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We present optical lightcurves of 428 periodic variable stars coincident with ROSAT X-ray sources, detected using the first run of the SuperWASP photometric survey. Only 68 of these were previously recognised as periodic variables. A further 30 of these objects are previously known pre-main sequence stars, for which we detect a modulation period for the first time. Amongst the newly identified periodic variables, many appear to be close eclipsing binaries, their X-ray emission is presumably the result of RS CVn type behaviour. Others are probably BY Dra stars, pre-main sequence stars and other rapid rotators displaying enhanced coronal activity. A number of previously catalogued pulsating variables (RR Lyr stars and Cepheids) coincident with X-ray sources are also seen, but we show that these are likely to be misclassifications. We identify four objects which are probable low mass eclipsing binary stars, based on their very red colour and light curve morphology.
We present Citizen ASAS-SN, a citizen science project hosted on the Zooniverse platform which utilizes data from the All-Sky Automated Survey for SuperNovae (ASAS-SN). Volunteers are presented with ASAS-SN $g$-band light curves of variable star candidates. The classification workflow allows volunteers to classify these sources into major variable groups, while also allowing for the identification of unique variable stars for additional follow-up.
We have studied over 1600 Am stars at a photometric precision of 1 mmag with SuperWASP photometric data. Contrary to previous belief, we find that around 200 Am stars are pulsating delta Sct and gamma Dor stars, with low amplitudes that have been missed in previous, less extensive studies. While the amplitudes are generally low, the presence of pulsation in Am stars places a strong constraint on atmospheric convection, and may require the pulsation to be laminar. While some pulsating Am stars have been previously found to be delta Sct stars, the vast majority of Am stars known to pulsate are presented in this paper. They will form the basis of future statistical studies of pulsation in the presence of atomic diffusion.
Microlensing is a powerful tool for discovering cold exoplanets, and the The Roman Space Telescope microlensing survey will discover over 1000 such planets. Rapid, automated classification of Romans microlensing events can be used to prioritize follow-up observations of the most interesting events. Machine learning is now often used for classification problems in astronomy, but the success of such algorithms can rely on the definition of appropriate features that capture essential elements of the observations that can map to parameters of interest. In this paper, we introduce tools that we have developed to capture features in simulated Roman light curves of different types of microlensing events, and evaluate their effectiveness in classifying microlensing light curves. These features are quantified as parameters that can be used to decide the likelihood that a given light curve is due to a specific type of microlensing event. This method leaves us with a list of parameters that describe features like the smoothness of the peak, symmetry, the number of peaks, and width and height of small deviations from the main peak. This will allow us to quickly analyze a set of microlensing light curves and later use the resulting parameters as input to machine learning algorithms to classify the events.
We present an analysis of mock X-ray spectra and light curves of magnetic cataclysmic variables using an upgraded version of the 3D CYCLOPS code. This 3D representation of the accretion flow allows us to properly model total and partial occultation of the post-shock region by the white dwarf as well as the modulation of the X-ray light curves due to the phase-dependent extinction of the pre-shock region. We carried out detailed post-shock region modeling in a four-dimensional parameter space by varying the white dwarf mass and magnetic field strength as well as the magnetosphere radius and the specific accretion rate. To calculate the post-shock region temperature and density profiles, we assumed equipartition between ions and electrons, took into account the white dwarf gravitational potential, the finite size of the magnetosphere and a dipole-like magnetic field geometry, and considered cooling by both bremsstrahlung and cyclotron radiative processes. By investigating the impact of the parameters on the resulting X-ray continuum spectra, we show that there is an inevitable degeneracy in the four-dimensional parameter space investigated here, which compromises X-ray continuum spectral fitting strategies and can lead to incorrect parameter estimates. However, the inclusion of X-ray light curves in different energy ranges can break this degeneracy, and it therefore remains, in principle, possible to use X-ray data to derive fundamental parameters of magnetic cataclysmic variables, which represents an essential step toward understanding their formation and evolution.
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