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Register a new userStellar populations in the ELT perspective
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Added by
Vittorio Francesco Braga
Publication date
2014
fields
Physics
and research's language is
English
Authors
G. Bono
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We discuss the impact that the next generation of Extremely Large Telescopes will have on the open astrophysical problems of resolved stellar populations. In particular, we address the interplay between multiband photometry and spectroscopy.
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Deep observations of the Universe, usually as a part of sky surveys, are one of the symbols of the modern astronomy because they can allow big collaborations, exploiting multiple facilities and shared knowledge. The new generation of extremely large telescopes will play a key role because of their angular resolution and their capability in collecting the light of faint sources. Our simulations combine technical, tomographic and observational information, and benefit of the Global-Multi Conjugate Adaptive Optics (GMCAO) approach, a well demonstrated method that exploits only natural guide stars to correct the scientific field of view from the atmospheric turbulence. By simulating K-band observations of 6000 high redshift galaxies in the Chandra Deep Field South area, we have shown how an ELT can carry out photometric surveys successfully, recovering morphological and structural parameters. We present here a wide statistics of the expected performance of a GMCAO-equipped ELT in 22 well-known surveys in terms of SR.
The delay-time distribution (DTD) is the occurrence rate of a class of objects as a function of time after a hypothetical burst of star formation. DTDs are mainly used as a statistical test of stellar evolution scenarios for supernova progenitors, but they can be applied to many other classes of astronomical objects. We calculate the first DTD for RR Lyrae variables using 29,810 RR Lyrae from the OGLE-IV survey and a map of the stellar-age distribution (SAD) in the Large Magellanic Cloud (LMC). We find that $sim 46%$ of the OGLE-IV RR Lyrae are associated with delay-times older than 8 Gyr (main-sequence progenitor masses less than 1 M$_{odot}$), and consistent with existing constraints on their ages, but surprisingly about $51%$ of RR Lyrae appear have delay times $1.2-8$ Gyr (main-sequence masses between $1 - 2$ M$_{odot}$ at LMC metallicity). This intermediate-age signal also persists outside the Bar-region where crowding is less of a concern, and we verified that without this signal, the spatial distribution of the OGLE-IV RR Lyrae is inconsistent with the SAD map of the LMC. Since an intermediate-age RR Lyrae channel is in tension with the lack of RR Lyrae in intermediate-age clusters (noting issues with small-number statistics), and the age-metallicity constraints of LMC stars, our DTD result possibly indicates that systematic uncertainties may still exist in SAD measurements of old-stellar populations, perhaps stemming from the construction methodology or the stellar evolution models used. We described tests to further investigate this issue.
The vast volume of data generated by modern astronomical surveys offers test beds for the application of machine-learning. It is important to evaluate potential existing tools and determine those that are optimal for extracting scientific knowledge from the available observations. We explore the possibility of using clustering algorithms to separate stellar populations with distinct chemical patterns. Star clusters are likely the most chemically homogeneous populations in the Galaxy, and therefore any practical approach to identifying distinct stellar populations should at least be able to separate clusters from each other. We applied eight clustering algorithms combined with four dimensionality reduction strategies to automatically distinguish stellar clusters using chemical abundances of 13 elements. Our sample includes 18 stellar clusters with a total of 453 stars. We use statistical tests showing that some pairs of clusters are indistinguishable from each other when chemical abundances from the Apache Point Galactic Evolution Experiment (APOGEE) are used. However, for most clusters we are able to automatically assign membership with metric scores similar to previous works. The confusion level of the automatically selected clusters is consistent with statistical tests that demonstrate the impossibility of perfectly distinguishing all the clusters from each other. These statistical tests and confusion levels establish a limit for the prospect of blindly identifying stars born in the same cluster based solely on chemical abundances. We find that some of the algorithms we explored are capable of blindly identify stellar populations with similar ages and chemical distributions in the APOGEE data. Because some stellar clusters are chemically indistinguishable, our study supports the notion of extending weak chemical tagging that involves families of clusters instead of individual clusters
Over the past decade, research in resolved stellar populations has made great strides in exploring the nature of dark matter, in unraveling the star formation, chemical enrichment, and dynamical histories of the Milky Way and nearby galaxies, and in probing fundamental physics from general relativity to the structure of stars. Large surveys have been particularly important to the biggest of these discoveries. In the coming decade, current and planned surveys will push these research areas still further through a large variety of discovery spaces, giving us unprecedented views into the low surface brightness Universe, the high surface brightness Universe, the 3D motions of stars, the time domain, and the chemical abundances of stellar populations. These discovery spaces will be opened by a diverse range of facilities, including the continuing Gaia mission, imaging machines like LSST and WFIRST, massively multiplexed spectroscopic platforms like DESI, Subaru-PFS, and MSE, and telescopes with high sensitivity and spatial resolution like JWST, the ELTs, and LUVOIR. We do not know which of these facilities will prove most critical for resolved stellar populations research in the next decade. We can predict, however, that their chance of success will be maximized by granting use of the data to broad communities, that many scientific discoveries will draw on a combination of data from them, and that advances in computing will enable increasingly sophisticated analyses of the large and complex datasets that they will produce. We recommend that Astro2020 1) acknowledge the critical role that data archives will play for stellar populations and other science in the next decade, 2) recognize the opportunity that advances in computing will bring for survey data analysis, and 3) consider investments in Science Platform technology to bring these opportunities to fruition.
The well-known age-metallicity-attenuation degeneracy does not permit unique and good estimates of basic parameters of stars and stellar populations. The effects of dust can be avoided using spectral line indices, but current methods have not been able to break the age-metallicity degeneracy. Here we show that using at least two new spectral line indices defined and measured on high-resolution (R= 6000) spectra of a signal-to-noise ratio (S/N) > 10 one gets unambiguous estimates of the age and metallicity of intermediate to old stellar populations. Spectroscopic data retrieved with new astronomical facilities, e.g., X-shooter, MEGARA, and MOSAIC, can be employed to infer the physical parameters of the emitting source by means of spectral line index and index--index diagram analysis.
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