Context.
In the last 15 years different ground-based spectroscopic surveys have been started (and completed) with the general aim of delivering stellar parameters and elemental abundances for large ...samples of Galactic stars, complementing
Gaia
astrometry. Among those surveys, the
Gaia
-ESO Public Spectroscopic Survey, the only one performed on a 8m class telescope, was designed to target 100 000 stars using FLAMES on the ESO VLT (both Giraffe and UVES spectrographs), covering all the Milky Way populations, with a special focus on open star clusters.
Aims.
This article provides an overview of the survey implementation (observations, data quality, analysis and its success, data products, and releases), of the open cluster survey, of the science results and potential, and of the survey legacy. A companion article reviews the overall survey motivation, strategy, Giraffe pipeline data reduction, organisation, and workflow.
Methods.
We made use of the information recorded and archived in the observing blocks; during the observing runs; in a number of relevant documents; in the spectra and master catalogue of spectra; in the parameters delivered by the analysis nodes and the working groups; in the final catalogue; and in the science papers. Based on these sources, we critically analyse and discuss the output and products of the Survey, including science highlights. We also determined the average metallicities of the open clusters observed as science targets and of a sample of clusters whose spectra were retrieved from the ESO archive.
Results.
The Gaia-ESO Survey has determined homogeneous good-quality radial velocities and stellar parameters for a large fraction of its more than 110 000 unique target stars. Elemental abundances were derived for up to 31 elements for targets observed with UVES. Lithium abundances are delivered for about 1/3 of the sample. The analysis and homogenisation strategies have proven to be successful; several science topics have been addressed by the
Gaia
-ESO consortium and the community, with many highlight results achieved.
Conclusions.
The final catalogue will be released through the ESO archive in the first half of 2022, including the complete set of advanced data products. In addition to these results, the
Gaia
-ESO Survey will leave a very important legacy, for several aspects and for many years to come.
The Gaia-ESO Survey: Calibration strategy Pancino, E.; Lardo, C.; Altavilla, G. ...
Astronomy and astrophysics (Berlin),
02/2017, Letnik:
598
Journal Article, Web Resource
Recenzirano
Odprti dostop
The Gaia-ESO survey (GES) is now in its fifth and last year of observations and has produced tens of thousands of high-quality spectra of stars in all Milky Way components. This paper presents the ...strategy behind the selection of astrophysical calibration targets, ensuring that all GES results on radial velocities, atmospheric parameters, and chemical abundance ratios will be both internally consistent and easily comparable with other literature results, especially from other large spectroscopic surveys and from Gaia. The calibration of GES is particularly delicate because of (i) the large space of parameters covered by its targets, ranging from dwarfs to giants, from O to M stars; these targets have a large wide of metallicities and also include fast rotators, emission line objects, and stars affected by veiling; (ii) the variety of observing setups, with different wavelength ranges and resolution; and (iii) the choice of analyzing the data with many different state-of-the-art methods, each stronger in a different region of the parameter space, which ensures a better understanding of systematic uncertainties. An overview of the GES calibration and homogenization strategy is also given, along with some examples of the usage and results of calibrators in GES iDR4, which is the fourth internal GES data release and will form the basis of the next GES public data release. The agreement between GES iDR4 recommended values and reference values for the calibrating objects are very satisfactory. The average offsets and spreads are generally compatible with the GES measurement errors, which in iDR4 data already meet the requirements set by the main GES scientific goals.
The new generation of large sky area spectroscopic survey project has produced nearly 10 million low-resolution stellar spectra. Based on these spectroscopic data, this paper introduces a machine ...learning algorithm named The Cannon. This algorithm is completely based on the known spectroscopic data of stellar atmospheric parameters (effective temperature, surface gravity, and metal abundance, etc.), this algorithm builds the characteristic vector by means of data driving, and establishes the functional relation between spectral flux characteristics and stellar parameters. Then it is applied to the observed spectral data to calculate the atmospheric parameters. The main advantage of The Cannon is that it is not directly based on any stellar physical models, it has an even higher applicability. Moreover, because of the use of full-spectrum information, even for the spectra with a low signal-to-noise ratio (SNR), it still can obtain the parameter solutions of high reliability. This algorithm has significant advantages in the data processing and parameter determination of large-scale stellar spectra. In addition, this paper presents two examples of using The Cannon to obtain the stellar parameters of K and M giants from the LAMOST spectral data.
Context. In the era of large high-resolution spectroscopic surveys such as Gaia-ESO and APOGEE, high-quality spectra can contribute to our understanding of the Galactic chemical evolution by ...providing abundances of elements that belong to the different nucleosynthesis channels, and also by providing constraints to one of the most elusive astrophysical quantities: stellar age. Aims. Some abundance ratios, such as C/N, have been proven to be excellent indicators of stellar ages. We aim at providing an empirical relationship between stellar ages and C/N using open star clusters, observed by the Gaia-ESO and APOGEE surveys, as calibrators. Methods. We used stellar parameters and abundances from the Gaia-ESO Survey and APOGEE Survey of the Galactic field and open cluster stars. Ages of star clusters were retrieved from the literature sources and validated using a common set of isochrones. We used the same isochrones to determine for each age and metallicity the surface gravity at which the first dredge-up and red giant branch bump occur. We studied the effect of extra-mixing processes in our sample of giant stars, and we derived the mean C/N in evolved stars, including only stars without evidence of extra mixing. By combining the Gaia-ESO and APOGEE samples of open clusters, we derived a linear relationship between C/N and (logarithmic) cluster ages. Results. We apply our relationship to selected giant field stars in the Gaia-ESO and APOGEE surveys. We find an age separation between thin- and thick-disc stars and age trends within their populations, with an increasing age towards lower metallicity populations. Conclusions. With this empirical relationship, we are able to provide an age estimate for giant stars in which C and N abundances are measured. For giant stars, the isochrone fitting method is indeed less sensitive than for dwarf stars at the turn-off. Our method can therefore be considered as an additional tool to give an independent estimate of the age of giant stars. The uncertainties in their ages is similar to those obtained using isochrone fitting for dwarf stars.
The
Gaia
-ESO Survey is a public spectroscopic survey that targeted ≳10
5
stars covering all major components of the Milky Way from the end of 2011 to 2018, delivering its final public release in May ...2022. Unlike other spectroscopic surveys,
Gaia
-ESO is the only survey that observed stars across all spectral types with dedicated, specialised analyses: from O (
T
eff
~ 30 000–52 000 K) all the way to K-M (≳3500 K). The physics throughout these stellar regimes varies significantly, which has previously prohibited any detailed comparisons between stars of significantly different types. In the final data release (internal data release 6) of the
Gaia
-ESO Survey, we provide the final database containing a large number of products, such as radial velocities, stellar parameters and elemental abundances, rotational velocity, and also, for example, activity and accretion indicators in young stars and membership probability in star clusters for more than 114 000 stars. The spectral analysis is coordinated by a number of working groups (WGs) within the survey, each specialised in one or more of the various stellar samples. Common targets are analysed across WGs to allow for comparisons (and calibrations) amongst instrumental setups and spectral types. Here we describe the procedures employed to ensure all survey results are placed on a common scale in order to arrive at a single set of recommended results for use by all survey collaborators. We also present some general quality and consistency checks performed on the entirety of the survey results.
Data-Driven Convergence Prediction of Astrobots Swarms Macktoobian, Matin; Basciani, Francesco; Gillet, Denis ...
IEEE transactions on automation science and engineering,
2022-April, 2022-4-00, Letnik:
19, Številka:
2
Journal Article
Odprti dostop
Astrobots are robotic artifacts whose swarms are used in astrophysical studies to generate the map of the observable universe. These swarms have to be coordinated with respect to various desired ...observations. Such coordination is so complicated that distributed swarm controllers cannot always coordinate enough astrobots to fulfill the minimum data desired to be obtained in the course of observations. Thus, a convergence verification is necessary to check the suitability of coordination before its execution. However, a formal verification method does not exist for this purpose. In this article, we instead use machine learning to predict the convergence of astrobots swarm. As the first solution to this problem, we propose a weighted <inline-formula> <tex-math notation="LaTeX">k </tex-math></inline-formula>-NN-based algorithm that requires the initial status of a swarm and its observational targets to predict its convergence. Our algorithm learns to predict based on the coordination data obtained from previous coordination of the desired swarm. This method first generates a convergence probability for each astrobot based on a distance metric. Then, these probabilities are transformed to either a complete or an incomplete categorical result. The method is applied to two typical swarms, including 116 and 487 astrobots. It turns out that the correct prediction of successful coordination may be up to 80% of overall predictions. Thus, these results witness the efficient accuracy of our predictive convergence analysis strategy. Note to Practitioners -Observatories involved in the generation of spectroscopic surveys always encounter limited resources to check the throughputs of their planned observations before their executions. The information yielded by an observation directly depends on the convergence rate of the observatory's astrobots in that particular observation. Namely, if the astrobots' convergence rate is below a minimum, then the observation has to be revoked and replanned. Thus, one may define another observation that fulfills the minimum-information requirement. There has been yet no analytical tool developed to verify the convergence rate of the coordination computed by the state-of-the-art trajectory planners of astrobots swarms. Thus, we propose to use a machine learning scheme to predict the desired convergence rate instead of involving in the infeasible process of finding its exact value. This method is a supervised method that requires the target-to-astrobot assignments table of an observation. The algorithm also needs a data set including previous coordination results of various observations of a particular swarm. The simulated scenarios manifest magnificent accuracies in the convergence predictions of some astrobots swarms corresponding to modern spectroscopic surveys, such as SDSS-V (including ~500 astrobots). Our strategy is based on the smallest subset of the astrobots' features that have a pivotal role in convergence rates, say, the projected positions of targets on a hosting telescope's focal plane. We argue that more explorations have to be considered to find other important features, such as the motion direction of each astrobot, which may even further improve the obtained prediction accuracies.
The wide‐field spectroscopic survey telescope (WST) is proposed to become the next large optical/near infrared facility for the European southern observatory (ESO) once the extremely large telescope ...(ELT) has become operational. While the latter is optimized for unprecedented sensitivity and adaptive‐optics assisted image quality over a small field of view, WST addresses the need for large survey volumes in spectroscopy with the light‐collecting power of a 10‐m class telescope. Its unique layout will feature the combination of multi‐object and integral field spectroscopy simultaneously. For the intended capacity of this layout, a very large number of detectors are needed. The complexity of the detector systems presents a number of challenges that are discussed with a focus on novel approaches and innovative detector designs that can be expected to emerge over the anticipated 20‐year timeline of this project.
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