The ages of solar-like stars have been at the center of many studies such as exoplanet characterization or Galactic-archaeology. While ages are usually computed from stellar evolution models, ...relations linking ages to other stellar properties, such as rotation and magnetic activity, have been investigated. With the large catalog of 55,232 rotation periods, \(P_{\rm rot}\), and photometric magnetic activity index, \(S_{\rm ph}\) from Kepler data, we have the opportunity to look for such magneto-gyro-chronology relations. Stellar ages are obtained with two stellar evolution codes that include treatment of angular momentum evolution, hence using \(P_{\rm rot}\) as input in addition to classical atmospheric parameters. We explore two different ways of predicting stellar ages on three subsamples with spectroscopic observations: solar analogs, late-F and G dwarfs, and K dwarfs. We first perform a Bayesian analysis to derive relations between \(S_{\rm ph}\) and ages between 1 and 5 Gyr, and other stellar properties. For late-F and G dwarfs, and K dwarfs, the multivariate regression favors the model with \(P_{\rm rot}\) and \(S_{\rm ph}\) with median differences of 0.1%.and 0.2% respectively. We also apply Machine Learning techniques with a Random Forest algorithm to predict ages up to 14 Gyr with the same set of input parameters. For late-F, G and K dwarfs together, predicted ages are on average within 5.3% of the model ages and improve to 3.1% when including \(P_{\rm rot}\). These are very promising results for a quick age estimation for solar-like stars with photometric observations, especially with current and future space missions.
Asteroseismology has transformed stellar astrophysics. Red giant asteroseismology is a prime example, with oscillation periods and amplitudes that are readily detectable with time-domain space-based ...telescopes. These oscillations can be used to infer masses, ages and radii for large numbers of stars, providing unique constraints on stellar populations in our galaxy. The cadence, duration, and spatial resolution of the Roman galactic bulge time-domain survey (GBTDS) are well-suited for asteroseismology and will probe an important population not studied by prior missions. We identify photometric precision as a key requirement for realizing the potential of asteroseismology with Roman. A precision of 1 mmag per 15-min cadence or better for saturated stars will enable detections of the populous red clump star population in the Galactic bulge. If the survey efficiency is better than expected, we argue for repeat observations of the same fields to improve photometric precision, or covering additional fields to expand the stellar population reach if the photometric precision for saturated stars is better than 1 mmag. Asteroseismology is relatively insensitive to the timing of the observations during the mission, and the prime red clump targets can be observed in a single 70 day campaign in any given field. Complementary stellar characterization, particularly astrometry tied to the Gaia system, will also dramatically expand the diagnostic power of asteroseismology. We also highlight synergies to Roman GBTDS exoplanet science using transits and microlensing.
The PLATO Mission Ragazzoni, Roberto; Min, Michiel; Werner, Stephanie C ...
arXiv.org,
06/2024
Paper, Journal Article
Odprti dostop
PLATO (PLAnetary Transits and Oscillations of stars) is ESA's M3 mission designed to detect and characterise extrasolar planets and perform asteroseismic monitoring of a large number of stars. PLATO ...will detect small planets (down to <2 R_(Earth)) around bright stars (<11 mag), including terrestrial planets in the habitable zone of solar-like stars. With the complement of radial velocity observations from the ground, planets will be characterised for their radius, mass, and age with high accuracy (5 %, 10 %, 10 % for an Earth-Sun combination respectively). PLATO will provide us with a large-scale catalogue of well-characterised small planets up to intermediate orbital periods, relevant for a meaningful comparison to planet formation theories and to better understand planet evolution. It will make possible comparative exoplanetology to place our Solar System planets in a broader context. In parallel, PLATO will study (host) stars using asteroseismology, allowing us to determine the stellar properties with high accuracy, substantially enhancing our knowledge of stellar structure and evolution. The payload instrument consists of 26 cameras with 12cm aperture each. For at least four years, the mission will perform high-precision photometric measurements. Here we review the science objectives, present PLATO's target samples and fields, provide an overview of expected core science performance as well as a description of the instrument and the mission profile at the beginning of the serial production of the flight cameras. PLATO is scheduled for a launch date end 2026. This overview therefore provides a summary of the mission to the community in preparation of the upcoming operational phases.
In order to understand stellar evolution, it is crucial to efficiently determine stellar surface rotation periods. An efficient tool to automatically determine reliable rotation periods is needed ...when dealing with large samples of stellar photometric datasets. The objective of this work is to develop such a tool. Random forest learning abilities are exploited to automate the extraction of rotation periods in Kepler light curves. Rotation periods and complementary parameters are obtained from three different methods: a wavelet analysis, the autocorrelation function of the light curve, and the composite spectrum. We train three different classifiers: one to detect if rotational modulations are present in the light curve, one to flag close binary or classical pulsators candidates that can bias our rotation period determination, and finally one classifier to provide the final rotation period. We test our machine learning pipeline on 23,431 stars of the Kepler K and M dwarf reference rotation catalog of Santos et al. (2019) for which 60% of the stars have been visually inspected. For the sample of 21,707 stars where all the input parameters are provided to the algorithm, 94.2% of them are correctly classified (as rotating or not). Among the stars that have a rotation period in the reference catalog, the machine learning provides a period that agrees within 10% of the reference value for 95.3% of the stars. Moreover, the yield of correct rotation periods is raised to 99.5% after visually inspecting 25.2% of the stars. Over the two main analysis steps, rotation classification and period selection, the pipeline yields a global agreement with the reference values of 92.1% and 96.9% before and after visual inspection. Random forest classifiers are efficient tools to determine reliable rotation periods in large samples of stars. abridged
The recently published Kepler mission Data Release 25 (DR25) reported on ~197,000 targets observed during the mission. Despite this, no wide search for red giants showing solar-like oscillations have ...been made across all stars observed in Kepler's long-cadence mode. In this work, we perform this task using custom apertures on the Kepler pixel files and detect oscillations in 21,914 stars, representing the largest sample of solar-like oscillating stars to date. We measure their frequency at maximum power, \(\nu_{\mathrm{max}}\), down to \(\nu_{\mathrm{max}}\simeq4\mu\)Hz and obtain \(\log(g)\) estimates with a typical uncertainty below 0.05 dex, which is superior to typical measurements from spectroscopy. Additionally, the \(\nu_{\mathrm{max}}\) distribution of our detections show good agreement with results from a simulated model of the Milky Way, with a ratio of observed to predicted stars of 0.992 for stars with 10\(\mu\)Hz \( <\nu_{\mathrm{max}}<270\mu\)Hz. Among our red giant detections, we find 909 to be dwarf/subgiant stars whose flux signal is polluted by a neighbouring giant as a result of using larger photometric apertures than those used by the NASA Kepler Science Processing Pipeline. We further find that only 293 of the polluting giants are known Kepler targets. The remainder comprises over 600 newly identified oscillating red giants, with many expected to belong to the galactic halo, serendipitously falling within the Kepler pixel files of targeted stars.
We present the third and final data release of the K2 Galactic Archaeology Program (K2 GAP) for Campaigns C1-C8 and C10-C18. We provide asteroseismic radius and mass coefficients, \(\kappa_R\) and ...\(\kappa_M\), for \(\sim 19,000\) red giant stars, which translate directly to radius and mass given a temperature. As such, K2 GAP DR3 represents the largest asteroseismic sample in the literature to date. K2 GAP DR3 stellar parameters are calibrated to be on an absolute parallactic scale based on Gaia DR2, with red giant branch and red clump evolutionary state classifications provided via a machine-learning approach. Combining these stellar parameters with GALAH DR3 spectroscopy, we determine asteroseismic ages with precisions of \(\sim 20-30\%\) and compare age-abundance relations to Galactic chemical evolution models among both low- and high-\(\alpha\) populations for \(\alpha\), light, iron-peak, and neutron-capture elements. We confirm recent indications in the literature of both increased Ba production at late Galactic times, as well as significant contribution to r-process enrichment from prompt sources associated with, e.g., core-collapse supernovae. With an eye toward other Galactic archaeology applications, we characterize K2 GAP DR3 uncertainties and completeness using injection tests, suggesting K2 GAP DR3 is largely unbiased in mass/age and with uncertainties of \(2.9\%\,(\rm{stat.})\,\pm0.1\%\,(\rm{syst.})\) & \(6.7\%\,(\rm{stat.})\,\pm0.3\%\,(\rm{syst.})\) in \(\kappa_R\) & \(\kappa_M\) for red giant branch stars and \(4.7\%\,(\rm{stat.})\,\pm0.3\%\,(\rm{syst.})\) & \(11\%\,(\rm{stat.})\,\pm0.9\%\,(\rm{syst.})\) for red clump stars. We also identify percent-level asteroseismic systematics, which are likely related to the time baseline of the underlying data, and which therefore should be considered in TESS asteroseismic analysis.
The NASA Transiting Exoplanet Survey Satellite (TESS) is observing tens of millions of stars with time spans ranging from \(\sim\) 27 days to about 1 year of continuous observations. This vast amount ...of data contains a wealth of information for variability, exoplanet, and stellar astrophysics studies but requires a number of processing steps before it can be fully utilized. In order to efficiently process all the TESS data and make it available to the wider scientific community, the TESS Data for Asteroseismology working group, as part of the TESS Asteroseismic Science Consortium, has created an automated open-source processing pipeline to produce light curves corrected for systematics from the short- and long-cadence raw photometry data and to classify these according to stellar variability type. We will process all stars down to a TESS magnitude of 15. This paper is the next in a series detailing how the pipeline works. Here, we present our methodology for the automatic variability classification of TESS photometry using an ensemble of supervised learners that are combined into a metaclassifier. We successfully validate our method using a carefully constructed labelled sample of Kepler Q9 light curves with a 27.4 days time span mimicking single-sector TESS observations, on which we obtain an overall accuracy of 94.9%. We demonstrate that our methodology can successfully classify stars outside of our labeled sample by applying it to all \(\sim\) 167,000 stars observed in Q9 of the Kepler space mission.
Studies of Galactic structure and evolution have benefitted enormously from Gaia kinematic information, though additional, intrinsic stellar parameters like age are required to best constrain ...Galactic models. Asteroseismology is the most precise method of providing such information for field star populations \(\textit{en masse}\), but existing samples for the most part have been limited to a few narrow fields of view by the CoRoT and Kepler missions. In an effort to provide well-characterized stellar parameters across a wide range in Galactic position, we present the second data release of red giant asteroseismic parameters for the K2 Galactic Archaeology Program (GAP). We provide \(\nu_{\mathrm{max}}\) and \(\Delta \nu\) based on six independent pipeline analyses; first-ascent red giant branch (RGB) and red clump (RC) evolutionary state classifications from machine learning; and ready-to-use radius & mass coefficients, \(\kappa_R\) & \(\kappa_M\), which, when appropriately multiplied by a solar-scaled effective temperature factor, yield physical stellar radii and masses. In total, we report 4395 radius and mass coefficients, with typical uncertainties of \(3.3\% \mathrm{\ (stat.)} \pm 1\% \mathrm{\ (syst.)}\) for \(\kappa_R\) and \(7.7\% \mathrm{\ (stat.)} \pm 2\% \mathrm{\ (syst.)}\) for \(\kappa_M\) among RGB stars, and \(5.0\% \mathrm{\ (stat.)} \pm 1\% \mathrm{\ (syst.)}\) for \(\kappa_R\) and \(10.5\% \mathrm{\ (stat.)} \pm 2\% \mathrm{\ (syst.)}\) for \(\kappa_M\) among RC stars. We verify that the sample is nearly complete -- except for a dearth of stars with \(\nu_{\mathrm{max}} \lesssim 10-20\mu\)Hz -- by comparing to Galactic models and visual inspection. Our asteroseismic radii agree with radii derived from Gaia Data Release 2 parallaxes to within \(2.2 \pm 0.3\%\) for RGB stars and \(2.0 \pm 0.6\%\) for RC stars.
Since the onset of the `space revolution' of high-precision high-cadence photometry, asteroseismology has been demonstrated as a powerful tool for informing Galactic archaeology investigations. The ...launch of the NASA TESS mission has enabled seismic-based inferences to go full sky -- providing a clear advantage for large ensemble studies of the different Milky Way components. Here we demonstrate its potential for investigating the Galaxy by carrying out the first asteroseismic ensemble study of red giant stars observed by TESS. We use a sample of 25 stars for which we measure their global asteroseimic observables and estimate their fundamental stellar properties, such as radius, mass, and age. Significant improvements are seen in the uncertainties of our estimates when combining seismic observables from TESS with astrometric measurements from the Gaia mission compared to when the seismology and astrometry are applied separately. Specifically, when combined we show that stellar radii can be determined to a precision of a few percent, masses to 5-10% and ages to the 20% level. This is comparable to the precision typically obtained using end-of-mission Kepler data
Over the course of its history, the Milky Way has ingested multiple smaller
satellite galaxies. While these accreted stellar populations can be
forensically identified as kinematically distinct ...structures within the Galaxy,
it is difficult in general to precisely date the age at which any one merger
occurred. Recent results have revealed a population of stars that were accreted
via the collision of a dwarf galaxy, called \textit{Gaia}-Enceladus, leading to
a substantial pollution of the chemical and dynamical properties of the Milky
Way. Here, we identify the very bright, naked-eye star $\nu$\,Indi as a probe
of the age of the early in situ population of the Galaxy. We combine
asteroseismic, spectroscopic, astrometric, and kinematic observations to show
that this metal-poor, alpha-element-rich star was an indigenous member of the
halo, and we measure its age to be $11.0 \pm 0.7$ (stat) $\pm 0.8$ (sys)$\,\rm
Gyr$. The star bears hallmarks consistent with it having been kinematically
heated by the \textit{Gaia}-Enceladus collision. Its age implies that the
earliest the merger could have begun was 11.6 and 13.2 Gyr ago at 68 and 95%
confidence, respectively. Input from computations based on hierarchical
cosmological models tightens (i.e. reduces) slightly the above limits.
