Class Of Variable Stars Research Articles

Period–Luminosity Relations in the Local Group of Galaxies

AbstractLocal Group galaxies, particularly the Large and Small Magellanic Clouds, have historically played and continue to play a unique role in studies of the period–luminosity (PL), period–luminosity–color (PLC), and period–Wesenheit (PW) relations, not just for pulsating stars. In recent years, significant efforts have been devoted to calibrate the PL, PLC, and PW relationships at different wavelengths, including studies of the influence of metallicity and nonlinearities on the accuracy of measured distances. However, the PL diagram has many more astrophysical applications. It serves as a vital tool for classifying different types of pulsating stars and can even facilitate the discovery of new classes of variable stars. Moreover, it aids in distinguishing among various modes of pulsation, facilitates the identification of pulsating stars that are members of binary systems, and enables studies of the three-dimensional structures of neighboring galaxies. In this contribution, I present the latest results on the PL, PLC, and PW relations obeyed by various types of variable stars in Local Group galaxies – from δ Scuti stars to Mira variabless and from close binary systems to the mysterious long secondary periods exhibited by red giant and supergiant stars.

Semi-supervised classification and clustering analysis for variable stars

ABSTRACT The immense amount of time series data produced by astronomical surveys has called for the use of machine learning algorithms to discover and classify several million celestial sources. In the case of variable stars, supervised learning approaches have become commonplace. However, this needs a considerable collection of expert-labelled light curves to achieve adequate performance, which is costly to construct. To solve this problem, we introduce two approaches. First, a semi-supervised hierarchical method, which requires substantially less trained data than supervised methods. Second, a clustering analysis procedure that finds groups that may correspond to classes or subclasses of variable stars. Both methods are primarily supported by dimensionality reduction of the data for visualization and to avoid the curse of dimensionality. We tested our methods with catalogues collected from the Optical Gravitational Lensing Experiment (OGLE), the Catalina Sky Survey (CSS), and the Gaia survey. The semi-supervised method reaches a performance of around 90 per cent for all of our three selected catalogues of variable stars using only $5{{\ \rm per\ cent}}$ of the data in the training. This method is suitable for classifying the main classes of variable stars when there is only a small amount of training data. Our clustering analysis confirms that most of the clusters found have a purity over 90 per cent with respect to classes and 80 per cent with respect to subclasses, suggesting that this type of analysis can be used in large-scale variability surveys as an initial step to identify which classes or subclasses of variable stars are present in the data and/or to build training sets, among many other possible applications.

Classifying Kepler light curves for 12 000 A and F stars using supervised feature-based machine learning

ABSTRACT With the availability of large-scale surveys like Kepler and TESS, there is a pressing need for automated methods to classify light curves according to known classes of variable stars. We introduce a new algorithm for classifying light curves that compares 7000 time-series features to find those that most effectively classify a given set of light curves. We apply our method to Kepler light curves for stars with effective temperatures in the range 6500–10 000 K. We show that the sample can be meaningfully represented in an interpretable 5D feature space that separates seven major classes of light curves (δ Scuti stars, γ Doradus stars, RR Lyrae stars, rotational variables, contact eclipsing binaries, detached eclipsing binaries, and non-variables). We achieve a balanced classification accuracy of 82 per cent on an independent test set of Kepler stars using a Gaussian mixture model classifier. We use our method to classify 12 000 Kepler light curves from Quarter 9 and provide a catalogue of the results. We further outline a confidence heuristic based on probability density to search our catalogue and extract candidate lists of correctly classified variable stars.

Identifying blue large amplitude pulsators from Gaia DR2 and ZTF DR3

ABSTRACT Blue large amplitude pulsators (BLAPs) are hot, subluminous stars undergoing rapid variability with periods of under 60 min. They have been linked with the early stages of pre-white dwarfs and hot subdwarfs. They are a rare class of variable star due to their evolutionary history within interacting binary systems and the short time-scales relative to their lifetime in which they are pulsationally unstable. All currently known BLAPs are relatively faint (15–19 mag) and are located in the Galactic plane. These stars have intrinsically blue colours but the large interstellar extinction in the Galactic plane prevents them from swift identification using colour-based selection criteria. In this paper, we correct the Gaia G-band apparent magnitude and GBP − GRP colours of 89.6 million sources brighter than 19 mag in the Galactic plane with good quality photometry combined with supplementary all-sky data totalling 162.3 million sources. Selecting sources with colours consistent with the known population of BLAPs and performing a cross-match with the Zwicky Transient Facility (ZTF) DR3, we identify 98 short period candidate variables. Manual inspection of the period-folded light curves reveals 22 candidate BLAPs. Of these targets, 6 are consistent with the observed periods and light curves of the known BLAPs, 10 are within the theoretical period range of BLAPs, and 6 are candidate high-gravity BLAPs. We present follow-up spectra of 21 of these candidate sources and propose to classify one of them as a BLAP, and tentatively assign an additional eight of them as BLAPs for future population studies.

New magnetic chemically peculiar stars and candidates in the ATLAS first catalogue of variable stars

ABSTRACT The number of known variable stars has increased by several magnitudes over the last decade, and automated classification routines are becoming increasingly important to cope with this development. Here we show that the ‘upside-down CBH variables’, which were proposed as a potentially new class of variable stars by Heinze et al. in the ATLAS First Catalogue of Variable Stars, are, at least to a high percentage, made up of α2 Canum Venaticorum (ACV) variables – that is, photometrically variable magnetic chemically peculiar (CP2/He-peculiar) stars – with distinct double-wave light curves. Using suitable selection criteria, we identified 264 candidate ACV variables in the ATLAS variable star catalogue. 62 of these objects were spectroscopically confirmed with spectra from the Large Sky Area Multi-Object Fibre Spectroscopic Telescope (all new discoveries except for nine stars) and classified on the MK system. The other 202 stars are here presented as ACV star candidates that require spectroscopic confirmation. The vast majority of our sample of stars are main-sequence objects. Derived masses range from 1.4 M⊙ to 5 M⊙, with half our sample stars being situated in the range from 2 M⊙ to 2.4 M⊙, in good agreement with the spectral classifications. Most stars belong to the thin or thick disc; four objects, however, classify as members of the halo population. With a peak magnitude distribution at around 14th magnitude, the here presented stars are situated at the faint end of the known Galactic mCP star population. Our study highlights the need to consider rare variability classes, like ACV variables, in automated classification routines.

Mysterious, variable, and extremely hot: White dwarfs showing ultra-high excitation lines

Context. About 10% of all stars exhibit absorption lines of ultra-highly excited (UHE) metals (e.g., O VIII) in their optical spectra when entering the white dwarf cooling sequence. This is something that has never been observed in any other astrophysical object, and poses a decades-long mystery in our understanding of the late stages of stellar evolution. The recent discovery of a UHE white dwarf that is both spectroscopically and photometrically variable led to the speculation that the UHE lines might be created in a shock-heated circumstellar magnetosphere. Aims. We aim to gain a better understanding of these mysterious objects by studying the photometric variability of the whole population of UHE white dwarfs, and white dwarfs showing only the He II line problem, as both phenomena are believed to be connected. Methods. We investigate (multi-band) light curves from several ground- and space-based surveys of all 16 currently known UHE white dwarfs (including one newly discovered) and eight white dwarfs that show only the He II line problem. Results. We find that 75−13+8% of the UHE white dwarfs, and 75−19+9% of the He II line problem white dwarfs are significantly photometrically variable, with periods ranging from 0.22 d to 2.93 d and amplitudes from a few tenths to a few hundredths of a magnitude. The high variability rate is in stark contrast to the variability rate amongst normal hot white dwarfs (we find 9−2+4%), marking UHE and He II line problem white dwarfs as a new class of variable stars. The period distribution of our sample agrees with both the orbital period distribution of post-common-envelope binaries and the rotational period distribution of magnetic white dwarfs if we assume that the objects in our sample will spin-up as a consequence of further contraction. Conclusions. We find further evidence that UHE and He II line problem white dwarfs are indeed related, as concluded from their overlap in the Gaia HRD, similar photometric variability rates, light-curve shapes and amplitudes, and period distributions. The lack of increasing photometric amplitudes towards longer wavelengths, as well as the nondetection of optical emission lines arising from the highly irradiated face of a hypothetical secondary in the optical spectra of our stars, makes it seem unlikely that an irradiated late-type companion is the origin of the photometric variability. Instead, we believe that spots on the surfaces of these stars and/or geometrical effects of circumstellar material might be responsible.

Informative Bayesian model selection for RR Lyrae star classifiers

ABSTRACT Machine learning has achieved an important role in the automatic classification of variable stars, and several classifiers have been proposed over the last decade. These classifiers have achieved impressive performance in several astronomical catalogues. However, some scientific articles have also shown that the training data therein contain multiple sources of bias. Hence, the performance of those classifiers on objects not belonging to the training data is uncertain, potentially resulting in the selection of incorrect models. Besides, it gives rise to the deployment of misleading classifiers. An example of the latter is the creation of open-source labelled catalogues with biased predictions. In this paper, we develop a method based on an informative marginal likelihood to evaluate variable star classifiers. We collect deterministic rules that are based on physical descriptors of RR Lyrae stars, and then, to mitigate the biases, we introduce those rules into the marginal likelihood estimation. We perform experiments with a set of Bayesian logistic regressions, which are trained to classify RR Lyraes, and we found that our method outperforms traditional non-informative cross-validation strategies, even when penalized models are assessed. Our methodology provides a more rigorous alternative to assess machine learning models using astronomical knowledge. From this approach, applications to other classes of variable stars and algorithmic improvements can be developed.

Carbon star CGCS 673 identified as a semi-regular variable star

This study reports that the carbon star CGCS 673 is a semi-regular (SR) variable star with a period of 135 d and an amplitude of 0.18 mag in the V-band. The light curve obtained by this study correlates well with the SR classification as the photometric data obtained show noticeable periodicity in the light changes of CGCS 673 that is occasionally interrupted by a period of irregular variability. The derived period and colour index obtained from our data and those from professional databases indicate that the attributes of this star fall within the parameters of the SR class of variable stars. Following our notification of the discovery that this star is a variable source, CGCS 673 has received the AAVSO Unique Identifier of (AAVSO UID) 000-BMZ-492.

Modules for Experiments in Stellar Astrophysics (MESA): Pulsating Variable Stars, Rotation, Convective Boundaries, and Energy Conservation

Abstract We update the capabilities of the open-knowledge software instrument Modules for Experiments in Stellar Astrophysics (MESA). RSP is a new functionality in MESAstar that models the nonlinear radial stellar pulsations that characterize RR Lyrae, Cepheids, and other classes of variable stars. We significantly enhance numerical energy conservation capabilities, including during mass changes. For example, this enables calculations through the He flash that conserve energy to better than 0.001%. To improve the modeling of rotating stars in MESA, we introduce a new approach to modifying the pressure and temperature equations of stellar structure, as well as a formulation of the projection effects of gravity darkening. A new scheme for tracking convective boundaries yields reliable values of the convective core mass and allows the natural emergence of adiabatic semiconvection regions during both core hydrogen- and helium-burning phases. We quantify the parallel performance of MESA on current-generation multicore architectures and demonstrate improvements in the computational efficiency of radiative levitation. We report updates to the equation of state and nuclear reaction physics modules. We briefly discuss the current treatment of fallback in core-collapse supernova models and the thermodynamic evolution of supernova explosions. We close by discussing the new MESA Testhub software infrastructure to enhance source code development.

Classifying Periodic Astrophysical Phenomena from non-survey optimized variable-cadence observational data

Modern time-domain astronomy is capable of collecting a staggeringly large amount of data on millions of objects in real time. Therefore, the production of methods and systems for the automated classification of time-domain astronomical objects is of great importance. The Liverpool Telescope has a number of wide-field image gathering instruments mounted upon its structure, the Small Telescopes Installed at the Liverpool Telescope. These instruments have been in operation since March 2009 gathering data of large areas of sky around the current field of view of the main telescope generating a large dataset containing millions of light sources. The instruments are inexpensive to run as they do not require a separate telescope to operate but this style of surveying the sky introduces structured artifacts into our data due to the variable cadence at which sky fields are resampled. These artifacts can make light sources appear variable and must be addressed in any processing method.The data from large sky surveys can lead to the discovery of interesting new variable objects. Efficient software and analysis tools are required to rapidly determine which potentially variable objects are worthy of further telescope time. Machine learning offers a solution to the quick detection of variability by characterising the detected signals relative to previously seen exemplars. In this paper, we introduce a processing system designed for use with the Liverpool Telescope identifying potentially interesting objects through the application of a novel representation learning approach to data collected automatically from the wide-field instruments. Our method automatically produces a set of classification features by applying Principal Component Analysis on set of variable light curves using a piecewise polynomial fitted via a genetic algorithm applied to the epoch-folded data. The epoch-folding requires the selection of a candidate period for variable light curves identified using a genetic algorithm period estimation method specifically developed for this dataset. A Random Forest classifier is then used to classify the learned features to determine if a light curve is generated by an object of interest. This system allows for the telescope to automatically identify new targets through passive observations which do not affect day-to-day operations as the unique artifacts resulting from such a survey method are incorporated into the methods.We demonstrate the power of this feature extraction method compared to feature engineering performed by previous studies by training classification models on 859 light curves of 12 known variable star classes from our dataset. We show that our new features produce a model with a superior mean cross-validation F1 score of 0.4729 with a standard deviation of 0.0931 compared with the engineered features at 0.3902 with a standard deviation of 0.0619. We show that the features extracted from the representation learning are given relatively high importance in the final classification model. Additionally, we compare engineered features computed on the interpolated polynomial fits and show that they produce more reliable distributions than those fit to the raw light curve when the period estimation is correct.

Discovery of a second pulsating intermediate helium-enriched sdOB star

We present the discovery of long-period, low-amplitude, g-mode pulsations in the intermediate He-rich hot subdwarf (sdOB) star Feige 46. So far, only one other He-enriched sdOB star (LS IV−14 ° 116) was known to exhibit such pulsations. From our ground-based light curves of Feige 46, we extracted five independent periodicities ranging from 2294 s to 3400 s. We fit our optical spectrum of the star with our grid of non-local thermodynamic equilibrium (NLTE) model atmospheres and derived the following atmospheric parameters: Teff = 36120 ± 230 K, log g = 5.93 ± 0.04, and log N(He)/N(H) = −0.32 ± 0.03 (formal fitting errors only). These parameters are very similar to those of LS IV−14 ° 116 and place Feige 46 well outside of the instability strip where the hydrogen-rich g-mode sdB pulsators are found. We used the Gaia parallax and proper motion of Feige 46 to perform a kinematic analysis of this star and found that it likely belongs to the Galactic halo population. This is most certainly an intriguing and interesting result given that LS IV−14 ° 116 is also a halo object. The mechanism responsible for the pulsations in these two peculiar objects remains unclear, but a possible scenario involves the ϵ-mechanism. Although they are the only two members in their class of variable stars, these pulsators appear to have more in common than just their pulsation properties.

Multiplicity of Galactic Cepheids and RR Lyrae stars from Gaia DR2

Context. Classical Cepheids (CCs) and RR Lyrae stars (RRLs) are important classes of variable stars used as standard candles to estimate galactic and extragalactic distances. Their multiplicity is imperfectly known, particularly for RRLs. Astoundingly, to date only one RRL has convincingly been demonstrated to be a binary, TU UMa, out of tens of thousands of known RRLs. Aims. Our aim is to detect the binary and multiple stars present in a sample of Milky Way CCs and RRLs. Methods. In the present article, we combine the HIPPARCOS and Gaia DR2 positions to determine the mean proper motion of the targets, and we search for proper motion anomalies (PMa) caused by close-in orbiting companions. Results. We identify 57 CC binaries from PMa out of 254 tested stars and 75 additional candidates, confirming the high binary fraction of these massive stars. For 28 binary CCs, we determine the companion mass by combining their spectroscopic orbital parameters and astrometric PMa. We detect 13 RRLs showing a significant PMa out of 198 tested stars, and 61 additional candidates. Conclusions. We determine that the binary fraction of CCs is likely above 80%, while that of RRLs is at least 7%. The newly detected systems will be useful to improve our understanding of their evolutionary states. The discovery of a significant number of RRLs in binary systems also resolves the long-standing mystery of their extremely low apparent binary fraction.

GaiaData Release 2

Context.The ESAGaiamission provides a unique time-domain survey for more than 1.6 billion sources withG≲ 21 mag.Aims.We showcase stellar variability in the Galactic colour-absolute magnitude diagram (CaMD). We focus on pulsating, eruptive, and cataclysmic variables, as well as on stars that exhibit variability that is due to rotation and eclipses.Methods.We describe the locations of variable star classes, variable object fractions, and typical variability amplitudes throughout the CaMD and show how variability-related changes in colour and brightness induce “motions”. To do this, we use 22 months of calibrated photometric, spectro-photometric, and astrometricGaiadata of stars with a significant parallax. To ensure that a large variety of variable star classes populate the CaMD, we crossmatchedGaiasources with known variable stars. We also used the statistics and variability detection modules of theGaiavariability pipeline. Corrections for interstellar extinction are not implemented in this article.Results. Gaiaenables the first investigation of Galactic variable star populations in the CaMD on a similar, if not larger, scale as was previously done in the Magellanic Clouds. Although the observed colours are not corrected for reddening, distinct regions are visible in which variable stars occur. We determine variable star fractions to within the current detection thresholds ofGaia. Finally, we report the most complete description of variability-induced motion within the CaMD to date.Conclusions. Gaiaenables novel insights into variability phenomena for an unprecedented number of stars, which will benefit the understanding of stellar astrophysics. The CaMD of Galactic variable stars provides crucial information on physical origins of variability in a way that has previously only been accessible for Galactic star clusters or external galaxies. FutureGaiadata releases will enable significant improvements over this preview by providing longer time series, more accurate astrometry, and additional data types (time series BP and RP spectra, RVS spectra, and radial velocities), all for much larger samples of stars.

Ultralow-amplitude RR Lyrae Stars in M4

Abstract We report evidence for a new class of variable star, which we dub millimagnitude RR Lyrae (mmRR). From K2 observations of the globular cluster M4, we find that out of 24 horizontal branch stars not previously known to be RR Lyrae variables, two show photometric variability with periods and shapes consistent with those of first-overtone RR Lyrae variables. The variability of these two stars, however, has amplitudes of only one part in a thousand, which is ∼200 times smaller than for any RR Lyrae variable in the cluster, and much smaller than any known RR Lyrae variable generally. The periods and amplitudes are 0.33190704 day with 1.0 mmag amplitude, and 0.31673414 day with 0.3 mmag amplitude. The stars lie just outside the instability strip, one blueward and one redward. The star redward of the instability strip also exhibits significant multi-periodic variability at lower frequencies. We examine potential blend scenarios and argue that they are all either physically implausible or highly improbable. Stars such as these are likely to shed valuable light on many aspects of stellar physics, including the mechanism(s) that set amplitudes of RR Lyrae variables.

Identifying blue large-amplitude pulsators in the Galactic plane using Gaia DR2: a case study

Blue large-amplitude pulsators (BLAPs) are blue stars emitting high-amplitude (> 0.2 mag) pulsations on a timescale of a few tens of minutes. Recently discovered using OGLE data, they form a new class of variable star and have inspired a number of investigations searching for the origin of their pulsations. This short study presents the Gaia DR2 data for ten BLAPs for which parallax measurements are available. We have dereddened their colours using Gaia DR2 data from the stars in their immediate field and find that six show absolute magnitude and intrinsic colour consistent with expectations, whilst four stars have a less certain classification. This work highlights the extra information that Gaia DR2 data can provide to help classify those variable stars for which moderate-resolution optical spectra are not currently available. We also show how Gaia DR2 can make searches for BLAPs in wide-field high-cadence surveys more systematic and robust.

Comparing the asteroseismic properties of pulsating pre-extremely low mass white dwarf and δ Scuti stars

Context. Pulsating extremely low-mass pre-white dwarf stars (pre-ELMV), with masses between ~0.15 M⊙ and ~0.30 M⊙, constitute a new class of variable stars showing g- and possibly p-mode pulsations with periods between 320 and 6000 s (frequencies between 14.4 and 270 c/d), driven by the κ mechanism operating in the second He ionization zone. On the other hand, main sequence δ Scuti stars, with masses between 1.2 and 2.5 M⊙, pulsate in low-order g and p modes with periods in the range [700–28 800] s (frequencies in the range [3–123] c/d), driven by the κ mechanism operating in the He II ionization zone and the turbulent pressure acting in the HI ionization layer. Interestingly enough, the instability strips of pre-ELM white dwarf and δ Scuti stars nearly overlap in the Teff vs. log g diagram, leading to a degeneracy when spectroscopy is the only tool to classify the stars and pulsation periods only are considered. Aims. Pre-ELM white dwarf and δ Scuti stars are in very different stages of evolution and therefore their internal structure is very distinct. This is mirrored in their pulsational behavior, thus employing asteroseismology should allow us to distinguish between these groups of stars despite their similar atmospheric parameters. Methods. We have employed adiabatic and non-adiabatic pulsation spectra for models of pre-ELM white dwarfs and δ Scuti stars, and compare their pulsation periods, period spacings, and rates of period change. Results. Unsurprisingly, we found substantial differences in the period spacing of δ Scuti and pre-ELM white dwarf models. Even when the same period range is observed in both classes of pulsating stars, the modes have distinctive signature in the period spacing and period difference values. For instance, the mean period difference of p-modes of consecutive radial orders for δ Scuti model are at least four times longer than the mean period spacing for the pre-ELM white dwarf model in the period range [2000–4600] s (frequency range [18.78–43.6] c/d). In addition, the rate of period change is two orders of magnitudes larger for the pre-ELM white dwarfs compared to δ Scuti stars. In addition, we also report the discovery of a new variable star, SDSSJ075738.94+144827.50, located in the region of the Teff versus log g diagram where these two kind of stars coexist. Conclusions.The characteristic spacing between modes of consecutive radial orders (p as well as g modes) and the large differences found in the rates of period change for δ Scuti and pre-ELM white dwarf stars suggest that asteroseismology can be employed to discriminate between these two groups of variable stars. Furthermore, we found that SDSSJ075738.94+144827.50 exhibits a period difference between p modes characteristic of a δ Sct star, assuming consecutive radial order for the observed periods.

A Data-driven Study of RR Lyrae Near-IR Light Curves: Principal Component Analysis, Robust Fits, and Metallicity Estimates

Abstract RR Lyrae variables are widely used tracers of Galactic halo structure and kinematics, but they can also serve to constrain the distribution of the old stellar population in the Galactic bulge. With the aim of improving their near-infrared photometric characterization, we investigate their near-infrared light curves, as well as the empirical relationships between their light curve and metallicities using machine learning methods. We introduce a new, robust method for the estimation of the light-curve shapes, hence the average magnitudes of RR Lyrae variables in the K S band, by utilizing the first few principal components (PCs) as basis vectors, obtained from the PC analysis of a training set of light curves. Furthermore, we use the amplitudes of these PCs to predict the light-curve shape of each star in the J-band, allowing us to precisely determine their average magnitudes (hence colors), even in cases where only one J measurement is available. Finally, we demonstrate that the K S-band light-curve parameters of RR Lyrae variables, together with the period, allow the estimation of the metallicity of individual stars with an accuracy of ∼0.2–0.25 dex, providing valuable chemical information about old stellar populations bearing RR Lyrae variables. The methods presented here can be straightforwardly adopted for other classes of variable stars, bands, or for the estimation of other physical quantities.

Variable stars in young open star clusters

We have carried out time series photometry of the some young open clusters NGC 1893, Be 59, NGC 7380, Stock 8 and NGC 281 to search for pre-main-sequence variable stars. We have detected several pre-main-sequence variables associated to the young open clusters with periods ranging from ~0.1 to ~50 days. These pre-main-sequence variables could be T Tauri stars. The association of these variable stars with the cluster has been established on the basis of two colour diagrams, a colour-magnitude diagram and spectral energy distributions. The brightness of classical T Tauri stars is found to vary with a larger amplitude in comparison to weak line T Tauri stars. It is found that the amplitude of pre-main-sequence variables decreases with increasing mass, which could be due to the dispersal of discs of massive stars. We have also detected several stars as main-sequence variables, which are classified either as slowly pulsating B stars, β Cep stars, δ Scuti stars or members of a new class of variable stars.

Searching for Pulsating Stars Using Clustering Algorithms

AbstractClustering algorithms constitute a multi-disciplinary analytical tool commonly used to summarise large data sets. Astronomical classifications are based on similarity, where celestial objects are assigned to a specific class according to specific physical features. The aim of this project is to obtain relevant information from high-dimensional data (at least three input variables in a data-frame) derived from stellar light-curves using a number of clustering algorithms such as K-means and Expectation Maximisation. In addition to identifying the best performing algorithm, we also identify a subset of features that best define stellar groups. Three methodologies are applied to a sample of Kepler time series in the temperature range 6500–19,000 K. In that spectral range, at least four classes of variable stars are expected to be found: δ Scuti, γ Doradus, Slowly Pulsating B (SPB), and (the still equivocal) Maia stars.

25 Years of the Southern Skies Monitoring by OGLE

AbstractThe Optical Gravitational Lensing Experiment (OGLE) started at Las Campanas Observatory in 1992 with a pilot monitoring programme of two million stars in the Galactic Bulge. It is still operating today, collecting time-domain photometric data of a billion stars from the densest regions in the southern sky. Among its main achievements are discoveries of thousands of microlensing events, a few dozen extrasolar planets and candidates for black holes, a million variable stars, and thousands of quasars and supernovæ. It has made a major contribution to the studies of the dark-matter content of the Milky Way halo, the structure of the Galactic Bulge, the Magellanic Clouds, and new classes of variable stars. In this its 25th anniversary year, we presented a selection of the major scientific highlights of OGLE.