Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userExploring the Morphology of RAVE Stellar Spectra
92
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
The RAdial Velocity Experiment (RAVE) is a medium resolution R~7500 spectroscopic survey of the Milky Way which already obtained over half a million stellar spectra. They present a randomly selected magnitude-limited sample, so it is important to use a reliable and automated classification scheme which identifies normal single stars and discovers different types of peculiar stars. To this end we present a morphological classification of 350,000 RAVE survey stellar spectra using locally linear embedding, a dimensionality reduction method which enables representing the complex spectral morphology in a low dimensional projected space while still preserving the properties of the local neighborhoods of spectra. We find that the majority of all spectra in the database ~90-95% belong to normal single stars, but there is also a significant population of several types of peculiars. Among them the most populated groups are those of various types of spectroscopic binary and chromospherically active stars. Both of them include several thousands of spectra. Particularly the latter group offers significant further investigation opportunities since activity of stars is a known proxy of stellar ages. Applying the same classification procedure to the sample of normal single stars alone shows that the shape of the projected manifold in two dimensional space correlates with stellar temperature, surface gravity and metallicity.
rate research
Read More
We apply the twin method to determine parallaxes to 232,545 stars of the RAVE survey using the parallaxes of Gaia DR1 as a reference. To search for twins in this large dataset, we apply the t-stochastic neighbour embedding t-SNE projection which distributes the data according to their spectral morphology on a two dimensional map. From this map we choose the twin candidates for which we calculate a chi^2 to select the best sets of twins. Our results show a competitive performance when compared to other model-dependent methods relying on stellar parameters and isochrones. The power of the method is shown by finding that the accuracy of our results is not significantly affected if the stars are normal or peculiar since the method is model free. We find twins for 60% of the RAVE sample which is not contained in TGAS or that have TGAS uncertainties which are larger than 20%. We could determine parallaxes with typical errors of 28%. We provide a complementary dataset for the RAVE stars not covered by TGAS, or that have TGAS uncertainties which are larger than 20%, with model-free parallaxes scaled to the Gaia measurements.
In the context of large spectroscopic surveys of stars, data-driven methods are key in deducing physical parameters for millions of spectra in a short time. Convolutional neural networks (CNNs) enable us to connect observables (e.g. spectra, stellar magnitudes) to physical properties (atmospheric parameters, chemical abundances, or labels in general). We trained a CNN, adopting stellar atmospheric parameters and chemical abundances from APOGEE DR16 (resolution R=22500) data as training set labels. As input, we used parts of the intermediate-resolution RAVE DR6 spectra (R~7500) overlapping with the APOGEE DR16 data as well as broad-band ALL_WISE and 2MASS photometry, together with Gaia DR2 photometry and parallaxes. We derived precise atmospheric parameters Teff, log(g), and [M/H] along with the chemical abundances of [Fe/H], [alpha/M], [Mg/Fe], [Si/Fe], [Al/Fe], and [Ni/Fe] for 420165 RAVE spectra. The precision typically amounts to 60K in Teff, 0.06 in log(g) and 0.02-0.04 dex for individual chemical abundances. Incorporating photometry and astrometry as additional constraints substantially improves the results in terms of the accuracy and precision of the derived labels. We provide a catalogue of CNN-trained atmospheric parameters and abundances along with their uncertainties for 420165 stars in the RAVE survey. CNN-based methods provide a powerful way to combine spectroscopic, photometric, and astrometric data without the need to apply any priors in the form of stellar evolutionary models. The developed procedure can extend the scientific output of RAVE spectra beyond DR6 to ongoing and planned surveys such as Gaia RVS, 4MOST, and WEAVE. We call on the community to place a particular collective emphasis and on efforts to create unbiased training samples for such future spectroscopic surveys.
We present the Spitzer Atlas of Stellar Spectra (SASS), which includes 159 stellar spectra (5 to 32 mic; R~100) taken with the Infrared Spectrograph on the Spitzer Space Telescope. This Atlas gathers representative spectra of a broad section of the Hertzsprung-Russell diagram, intended to serve as a general stellar spectral reference in the mid-infrared. It includes stars from all luminosity classes, as well as Wolf-Rayet (WR) objects. Furthermore, it includes some objects of intrinsic interest, like blue stragglers and certain pulsating variables. All the spectra have been uniformly reduced, and all are available online. For dwarfs and giants, the spectra of early-type objects are relatively featureless, dominated by Hydrogen lines around A spectral types. Besides these, the most noticeable photospheric features correspond to water vapor and silicon monoxide in late-type objects and methane and ammonia features at the latest spectral types. Most supergiant spectra in the Atlas present evidence of circumstellar gas. The sample includes five M supergiant spectra, which show strong dust excesses and in some cases PAH features. Sequences of WR stars present the well-known pattern of lines of HeI and HeII, as well as forbidden lines of ionized metals. The characteristic flat-top shape of the [Ne III] line is evident even at these low spectral resolutions. Several Luminous Blue Variables and other transition stars are present in the Atlas and show very diverse spectra, dominated by circumstellar gas and dust features. We show that the [8]-[24] Spitzer colors (IRAC and MIPS) are poor predictors of spectral type for most luminosity classes.
The rotational evolution of cool dwarfs is poorly constrained after around 1-2 Gyr due to a lack of precise ages and rotation periods for old main-sequence stars. In this work we use velocity dispersion as an age proxy to reveal the temperature-dependent rotational evolution of low-mass Kepler dwarfs, and demonstrate that kinematic ages could be a useful tool for calibrating gyrochronology in the future. We find that a linear gyrochronology model, calibrated to fit the period-Teff relationship of the Praesepe cluster, does not apply to stars older than around 1 Gyr. Although late-K dwarfs spin more slowly than early-K dwarfs when they are young, at old ages we find that late-K dwarfs rotate at the same rate or faster than early-K dwarfs of the same age. This result agrees qualitatively with semi-empirical models that vary the rate of surface-to-core angular momentum transport as a function of time and mass. It also aligns with recent observations of stars in the NGC 6811 cluster, which indicate that the surface rotation rates of K dwarfs go through an epoch of inhibited evolution. We find that the oldest Kepler stars with measured rotation periods are late-K and early-M dwarfs, indicating that these stars maintain spotted surfaces and stay magnetically active longer than more massive stars. Finally, based on their kinematics, we confirm that many rapidly rotating GKM dwarfs are likely to be synchronized binaries.
Empirical stellar spectral libraries have applications in both extragalactic and stellar studies, and they have an advantage over theoretical libraries because they naturally include all relevant chemical species and physical processes. During recent years we see a stream of new high quality sets of spectra, but increasing the spectral resolution and widening the wavelength coverage means resorting to multi-order echelle spectrographs. Assembling the spectra from many pieces results in lower fidelity of their shapes. We aim to offer the community a library of high signal-to-noise spectra with reliable continuum shapes. Furthermore, the using an integral field unit (IFU) alleviates the issue of slit losses. Our library was build with the MUSE (Multi-Unit Spectroscopic Explorer) IFU instrument. We obtained spectra over nearly the entire visual band (lambda~4800-9300 Ang). We assembled a library of 35 high-quality MUSE spectra for a subset of the stars from the X-shooter Spectral Library. We verified the continuum shape of these spectra with synthetic broad band colors derived from the spectra. We also report some spectral indices from the Lick system, derived from the new observations. We offer a high-fidelity set of stellar spectra that covers the Hertzsprung-Russell diagram. It can be useful for both extragalactic and stellar work and demonstrates that the IFUs are excellent tools for building reliable spectral libraries.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
