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Register a new userMyGIsFOS: an automated code for parameter determination and detailed abundance analysis in cool stars
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The current and planned high-resolution, high-multiplexity stellar spectroscopic surveys, as well as the swelling amount of under-utilized data present in public archives have led to an increasing number of efforts to automate the crucial but slow process to retrieve stellar parameters and chemical abundances from spectra. We present MyGIsFOS, a code designed to derive atmospheric parameters and detailed stellar abundances from medium - high resolution spectra of cool (FGK) stars. We describe the general structure and workings of the code, present analyses of a number of well studied stars representative of the parameter space MyGIsFOS is designed to cover, and examples of the exploitation of MyGIsFOS very fast analysis to assess uncertainties through Montecarlo tests. MyGIsFOS aims to reproduce a ``traditional manual analysis by fitting spectral features for different elements against a precomputed grid of synthetic spectra. Fe I and Fe II lines can be employed to determine temperature, gravity, microturbulence, and metallicity by iteratively minimizing the dependence of Fe I abundance from line lower energy and equivalent width, and imposing Fe I - Fe II ionization equilibrium. Once parameters are retrieved, detailed chemical abundances are measured from lines of other elements. MyGIsFOS replicates closely the results obtained in similar analyses on a set of well known stars. It is also quite fast, performing a full parameter determination and detailed abundance analysis in about two minutes per star on a mainstream desktop computer. Currently, its preferred field of application are high-resolution and/or large spectral coverage data (e.g UVES, X-Shooter, HARPS, Sophie).
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With recent developments in imaging and computer technology the amount of available astronomical data has increased dramatically. Although most of these data sets are not dedicated to the study of variable stars much of it can, with the application of proper software tools, be recycled for the discovery of new variable stars. Fits Viewer and Data Retrieval System is a new software package that takes advantage of modern computer advances to search astronomical data for new variable stars. More than 200 new variable stars have been found in a data set taken with the Calvin College Rehoboth Robotic telescope using FVDRS. One particularly interesting example is a very fast subdwarf B with a 95 minute orbital period, the fastest currently known of the HW Vir type.
We aimed to assess the accuracy of the Gaia teff and logg estimates as derived with current models and observations. We assessed the validity of several inference techniques for deriving the physical parameters of ultra-cool dwarf stars. We used synthetic spectra derived from ultra-cool dwarf models to construct (train) the regression models. We derived the intrinsic uncertainties of the best inference models and assessed their validity by comparing the estimated parameters with the values derived in the bibliography for a sample of ultra-cool dwarf stars observed from the ground. We estimated the total number of ultra-cool dwarfs per spectral subtype, and obtained values that can be summarised (in orders of magnitude) as 400000 objects in the M5-L0 range, 600 objects between L0 and L5, 30 objects between L5 and T0, and 10 objects between T0 and T8. A bright ultra-cool dwarf (with teff=2500 K and logg=3.5 will be detected by Gaia out to approximately 220 pc, while for teff=1500 K (spectral type L5) and the same surface gravity, this maximum distance reduces to 10-20 pc. The RMSE of the prediction deduced from ground-based spectra of ultra-cool dwarfs simulated at the Gaia spectral range and resolution, and for a Gaia magnitude G=20 is 213 K and 266 K for the models based on k-nearest neighbours and Gaussian process regression, respectively. These are total errors in the sense that they include the internal and external errors, with the latter caused by the inability of the synthetic spectral models (used for the construction of the regression models) to exactly reproduce the observed spectra, and by the large uncertainties in the current calibrations of spectral types and effective temperatures.
An analysis of H alpha and H beta spectra in a sample of 30 cool dwarf and subgiant stars is presented using MARCS model atmospheres based on the most recent calculations of the line opacities. A detailed quantitative comparison of the solar flux spectra with model spectra shows that Balmer line profile shapes, and therefore the temperature structure in the line formation region, are best represented under the mixing length theory by any combination of a low mixing-length parameter alpha and a low convective structure parameter y. A slightly lower effective temperature is obtained for the sun than the accepted value, which we attribute to errors in models and line opacities. The programme stars span temperatures from 4800 to 7100 K and include a small number of population II stars. Effective temperatures have been derived using a quantitative fitting method with a detailed error analysis. Our temperatures find good agreement with those from the Infrared Flux Method (IRFM) near solar metallicity but show differences at low metallicity where the two available IRFM determinations themselves are in disagreement. Comparison with recent temperature determinations using Balmer lines by Fuhrmann (1998, 2000), who employed a different description of the wing absorption due to self-broadening, does not show the large differences predicted by Barklem et al. (2000). In fact, perhaps fortuitously, reasonable agreement is found near solar metallicity, while we find significantly cooler temperatures for low metallicity stars of around solar temperature.
We report on two distinct computational approaches to self-consistently measure photospheric properties of large samples of stars. Both procedures consist of a set of several semi-integrated tasks based on shell and Python scripts, which efficiently run either our own codes or open source software commonly adopted by the astronomical community. One approach aims to derive the main stellar photospheric parameters and abundances of a few elements by analysing high-resolution spectra from a given public library homogeneously constructed. The other one is applied to recover the abundance of a single element in stars with known photospheric parameters by using mid-resolution spectra from another open homogeneous database and calibrating derived abundances. Both semi-automated computational approaches provide homogeneity and objectivity to every step of the process and represent a fast way to reach partial and final results as well as to estimate measurement errors, making possible to systematically evaluate and improve the distinct steps.
We present a machine learning package for the classification of periodic variable stars. Our package is intended to be general: it can classify any single band optical light curve comprising at least a few tens of observations covering durations from weeks to years, with arbitrary time sampling. We use light curves of periodic variable stars taken from OGLE and EROS-2 to train the model. To make our classifier relatively survey-independent, it is trained on 16 features extracted from the light curves (e.g. period, skewness, Fourier amplitude ratio). The model classifies light curves into one of seven superclasses - Delta Scuti, RR Lyrae, Cepheid, Type II Cepheid, eclipsing binary, long-period variable, non-variable - as well as subclasses of these, such as ab, c, d, and e types for RR Lyraes. When trained to give only superclasses, our model achieves 0.98 for both recall and precision as measured on an independent validation dataset (on a scale of 0 to 1). When trained to give subclasses, it achieves 0.81 for both recall and precision. In order to assess classification performance of the subclass model, we applied it to the MACHO, LINEAR, and ASAS periodic variables, which gave recall/precision of 0.92/0.98, 0.89/0.96, and 0.84/0.88, respectively. We also applied the subclass model to Hipparcos periodic variable stars of many other variability types that do not exist in our training set, in order to examine how much those types degrade the classification performance of our target classes. In addition, we investigate how the performance varies with the number of data points and duration of observations. We find that recall and precision do not vary significantly if the number of data points is larger than 80 and the duration is more than a few weeks. The classifier software of the subclass model is available from the GitHub repository (https://goo.gl/xmFO6Q).
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