Search Results (6)

RR Lyrae stars are pulsating variables crucial for distance determination and galactic structure studies. Metallicities of fundamental-mode (RRab) RR Lyrae stars are commonly derived from photometry using empirical relations involving the Fourier parameter ${\varphi }_{31}$ and the pulsation period. We present a new, calibrated G-band relationship between pulsation period P, Fourier parameter ${\varphi }_{31}$, and metallicity [Fe/H] for galactic RR Lyrae stars from the Gaia survey. A set of 72 fundamental mode RR Lyrae stars were identified for deriving the relation in the G-band after visual examination of their light curves. Unlike recent large-scale calibrations, our relation prioritizes calibration purity by anchoring exclusively to a homogeneously analyzed sample of high-resolution spectroscopic metallicities from the literature. Our best fit relation is $[\mathrm{Fe}/\mathrm{H}]=(-6.93\pm 0.58)-(6.04\pm 0.37)P+(1.65\pm 0.11){\varphi }_{31}$. We compare the [Fe/H] predicted by our relation for the stars in our calibration sample with that obtained from previously established relations in the G-band using different approaches. Our calibrated G-band P-${\varphi }_{31}$-[Fe/H] relationship demonstrates high reliability when validated against spectroscopic data, achieving a negligible bias of 0.00 dex and an empirical RMS scatter of 0.26 dex. Furthermore, by applying an Orthogonal Distance Regression (ODR) routine that fully propagates parameter covariance, we establish a mathematically strict empirical baseline whose theoretical uncertainties perfectly align with this observed dispersion. We find that the inclusion of the $R_{21}$ Fourier parameter offers no significant improvement in metallicity estimation. Comparisons with literature confirm that our linear relation aligns closely with other Gaia DR3-based studies, while offering improved precision over older DR2-based relations. Full article

Focusing on the discovery of new $\delta$ Scuti and $\gamma$ Doradus stars, we analyzed the Transiting Exoplanet Survey Satellite (TESS) light curves for 193,940 A-F stars selected from four legacy catalogs—the Henry Draper Catalogue (HD), the Smithsonian Astrophysical Observatory (SAO) Star Catalog, the Positions and Proper Motions Catalog (PPM), and the Bonner Durchmusterung (BD, including its extensions). Through visual inspection of light curve morphologies and periodograms, combined with evaluation of stellar parameters, we identified over 51,850 previously unreported variable stars. These include 15,380 $\delta$ Scuti, 18,560 $\gamma$ Doradus, 28 RR Lyrae stars, 260 heartbeat candidates, and 2645 eclipsing binaries, along with thousands of other variable types. Notably, over 4145 variables exhibit hybrid $\delta$ Scuti-$\gamma$ Doradus pulsations, and more than 380 eclipsing binaries feature pulsating primary components. This study reveals a substantial population of bright, previously undetected variables, providing a valuable resource for ensemble asteroseismology, binary evolution studies, and Galactic structure research. Our results also highlight the surprising richness in variability still hidden within well-known stellar catalogs and the continued importance of high-precision, time-domain surveys such as TESS. Full article

Determining the physical parameters of pulsating variable stars such as RR Lyrae is essential for understanding their internal structure, pulsation mechanisms, and evolutionary state. In this study, we present a machine learning framework that uses feedforward artificial neural networks (ANNs) to infer stellar parameters—mass (M), luminosity (log($L/L_{\odot }$)), effective temperature (log($T_{\mathrm{eff}}$)), and metallicity (Z)—directly from Transiting Exoplanet Survey Satellite (TESS) light curves. The network is trained on a synthetic grid of RRab light curves generated from hydrodynamical pulsation models spanning a broad range of physical parameters. We validate the model using synthetic self-inversion tests and demonstrate that the ANN accurately recovers the input parameters with minimal bias. We then apply the trained model to RRab stars observed by the TESS. The observed light curves are phase-folded, corrected for extinction, and passed through the ANN to derive physical parameters. Based on these results, we construct an empirical period–luminosity–metallicity (PLZ) relation: log($L/L_{\odot }$) = (1.458 ± 0.028) log(P/days) + (–0.068 ± 0.007) [Fe/H] + (2.040 ± 0.007). This work shows that ANN-based light-curve inversion offers an alternative method for extracting stellar parameters from single-band photometry. The approach can be extended to other classes of pulsators such as Cepheids and Miras. Full article

The physical origin of the Blazhko effect (BL), a phenomenon of a single or multiple periodic modulation(s) of the light curve, is under debate. Efficiently identifying and characterizing the BL is essential in understanding its origins and accounting for its effect on numerous applications of RRabs in the era of large time-domain surveys. In this study, we make use of Resnet 34, a well-known convolutional neural network (CNN) architecture, to identify RRab stars with BL from phased light curves collected from OGLE. Using reliably classified RRabs from frequency analysis to train, validate, and test our model, we show that our CNN method reaches accuracies up to 94%. We then applied our CNN method to some additional RRabs located in the Magellanic Cloud (MC) and the Galactic Bulge (GB), leading to the discovery of 113 and 2496 BL candidates, respectively. The identification accuracy for the MC Sample is estimated to be 91% after cross-matching the CNN classification results with those from frequency analysis. Similarly, the light-curve parameters of these classified BL/non-BL candidates by our CNN method from the GB region resemble those observed in the literature, confirming the reliability of our CNN classifications. Our CNN method is subject to issues related to light-curve quality and sampling, but its overall reliance on light-curve quality is comparable to that of frequency analysis. Furthermore, we find that BL modulation could be primarily characterized by variations in light-curve structure. Full article

We discuss the use and importance of pulsating variable stars as population tracers in Local Group galaxies. Among bright variable crossing the classical instability strip, we mostly focus on RR Lyrae stars and Anomalous Cepheids. We discuss their pulsational properties and how it is possible to use them to constrain the evolution and star formation history of the host galaxy. We discuss RR Lyrae stars as tracers of the old population, and how they can be used to trace the accretion history of large galaxies such as the Milky Way and M31, and also the early chemical evolution. Moreover, we show that the frequency of Anomalous Cepheids follows different relations, and therefore trace the intermediate-age star formation. Finally, we discuss the different methods to derive distances and the impact of the Gaia mission. Full article

Globular clusters are both primary fossils of galactic evolution and formation and are ideal laboratories for constraining the evolution of low-mass and metal-poor stars. RR Lyrae and type II Cepheid variables are low-mass, radially pulsating stars that trace old-age stellar populations. These stellar standard candles in globular clusters are crucial for measuring their precise distances and, in turn, absolute ages, and for the calibration of the extragalactic distance scale. Herein, the evolutionary stages of RR Lyrae and type II Cepheids are discussed, and their pulsation properties, including the light curves, color–magnitude and period–amplitude diagrams, and period–luminosity relations in globular clusters at optical and infrared wavelengths are presented. The RR Lyrae visual magnitude–metallicity relation and the multiband period–luminosity–metallicity relations in globular clusters covering a wide metallicity range are also discussed in detail for their application to the RR Lyrae-based distance scale. Full article