| 1. |
- Gilmore, G., et al.
(författare)
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The Gaia-ESO Public Spectroscopic Survey : Motivation, implementation, GIRAFFE data processing, analysis, and final data products star
- 2022
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Ingår i: Astronomy and Astrophysics. - : EDP Sciences. - 0004-6361 .- 1432-0746. ; 666
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Tidskriftsartikel (refereegranskat)abstract
- Context. The Gaia-ESO Public Spectroscopic Survey is an ambitious project designed to obtain astrophysical parameters and elemental abundances for 100 000 stars, including large representative samples of the stellar populations in the Galaxy, and a well-defined sample of 60 (plus 20 archive) open clusters. We provide internally consistent results calibrated on benchmark stars and star clusters, extending across a very wide range of abundances and ages. This provides a legacy data set of intrinsic value, and equally a large wide-ranging dataset that is of value for the homogenisation of other and future stellar surveys and Gaia's astrophysical parameters. Aims. This article provides an overview of the survey methodology, the scientific aims, and the implementation, including a description of the data processing for the GIRAFFE spectra. A companion paper introduces the survey results. Methods. Gaia-ESO aspires to quantify both random and systematic contributions to measurement uncertainties. Thus, all available spectroscopic analysis techniques are utilised, each spectrum being analysed by up to several different analysis pipelines, with considerable effort being made to homogenise and calibrate the resulting parameters. We describe here the sequence of activities up to delivery of processed data products to the ESO Science Archive Facility for open use. Results. The Gaia-ESO Survey obtained 202 000 spectra of 115 000 stars using 340 allocated VLT nights between December 2011 and January 2018 from GIRAFFE and UVES. Conclusions. The full consistently reduced final data set of spectra was released through the ESO Science Archive Facility in late 2020, with the full astrophysical parameters sets following in 2022. A companion article reviews the survey implementation, scientific highlights, the open cluster survey, and data products.
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| 2. |
- Randich, S., et al.
(författare)
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The Gaia-ESO Public Spectroscopic Survey : Implementation, data products, open cluster survey, science, and legacy
- 2022
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Ingår i: Astronomy and Astrophysics. - : EDP SCIENCES S A. - 0004-6361 .- 1432-0746. ; 666
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 3. |
- Tempel, E., et al.
(författare)
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An optimized tiling pattern for multiobject spectroscopic surveys : Application to the 4MOST survey
- 2020
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Ingår i: Monthly Notices of the Royal Astronomical Society. - : Oxford University Press (OUP). - 0035-8711 .- 1365-2966. ; 497:4, s. 4626-4643
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Tidskriftsartikel (refereegranskat)abstract
- Large multiobject spectroscopic surveys require automated algorithms to optimize their observing strategy. One of the most ambitious upcoming spectroscopic surveys is the 4MOST survey. The 4MOST survey facility is a fibre-fed spectroscopic instrument on the VISTA telescope with a large enough field of view to survey a large fraction of the southern sky within a few years. Several Galactic and extragalactic surveys will be carried out simultaneously, so the combined target density will strongly vary. In this paper, we describe a new tiling algorithm that can naturally deal with the large target density variations on the sky and which automatically handles the different exposure times of targets. The tiling pattern is modelled as a marked point process, which is characterized by a probability density that integrates the requirements imposed by the 4MOST survey. The optimal tilling pattern with respect to the defined model is estimated by the tiles configuration that maximizes the proposed probability density. In order to achieve this maximization a simulated annealing algorithm is implemented. The algorithm automatically finds an optimal tiling pattern and assigns a tentative sky brightness condition and exposure time for each tile, while minimizing the total execution time that is needed to observe the list of targets in the combined input catalogue of all surveys. Hence, the algorithm maximizes the long-term observing efficiency and provides an optimal tiling solution for the survey. While designed for the 4MOST survey, the algorithm is flexible and can with simple modifications be applied to any other multiobject spectroscopic survey.
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| 4. |
- Guiglion, G., et al.
(författare)
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Beyond Gaia DR3 : Tracing the [α/M] - [M/H] bimodality from the inner to the outer Milky Way disc with Gaia-RVS and convolutional neural networks
- 2024
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Ingår i: Astronomy and Astrophysics. - 0004-6361 .- 1432-0746. ; 682
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Tidskriftsartikel (refereegranskat)abstract
- Context. In June 2022, Gaia DR3 provided the astronomy community with about one million spectra from the Radial Velocity Spectrometer (RVS) covering the CaII triplet region. In the next Gaia data releases, we anticipate the number of RVS spectra to successively increase from several 10 million spectra to eventually more than 200 million spectra. Thus, stellar spectra are projected to be produced on an ‘industrial scale’, with numbers well above those for current and anticipated ground-based surveys. However, one-third of the published spectra have 15 ≤ S /N ≤ 25 per pixel such that they pose problems for classical spectral analysis pipelines, and therefore, alternative ways to tap into these large datasets need to be devised.Aims. We aim to leverage the versatility and capabilities of machine learning techniques for supercharged stellar parametrisation by combining Gaia-RVS spectra with the full set of Gaia products and high-resolution, high-quality ground-based spectroscopic reference datasets.Methods. We developed a hybrid convolutional neural network (CNN) that combines the Gaia DR3 RVS spectra, photometry (G, G_BP, G_RP), parallaxes, and XP coefficients to derive atmospheric parameters (Teff, log(g) as well as overall [M/H]) and chemical abundances ([Fe/H] and [α/M]). We trained the CNN with a high-quality training sample based on APOGEE DR17 labels.Results. With this CNN, we derived homogeneous atmospheric parameters and abundances for 886 080 RVS stars that show remarkable precision and accuracy compared to external datasets (such as GALAH and asteroseismology). The CNN is robust against noise in the RVS data, and we derive very precise labels down to S/N =15. We managed to characterise the [α/M] - [M/H] bimodality from the inner regions to the outer parts of the Milky Way, which has never been done using RVS spectra or similar datasets.Conclusions. This work is the first to combine machine learning with such diverse datasets and paves the way for large-scale machine learning analysis of Gaia-RVS spectra from future data releases. Large, high-quality datasets can be optimally combined thanks to the CNN, thereby realising the full power of spectroscopy, astrometry, and photometry.
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| 5. |
- Guiglion, G., et al.
(författare)
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The RAdial Velocity Experiment (RAVE) : Parameterisation of RAVE spectra based on convolutional neural networks
- 2020
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Ingår i: Astronomy and Astrophysics. - : EDP Sciences. - 0004-6361 .- 1432-0746. ; 644
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Tidskriftsartikel (refereegranskat)abstract
- Context Data-driven methods play an increasingly important role in the field of astrophysics 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). Aims. We test whether it is possible to transfer the labels derived from a high-resolution stellar survey to intermediate-resolution spectra of another survey by using a CNN. Methods. We trained a CNN, adopting stellar atmospheric parameters and chemical abundances from APOGEE DR16 (resolution Ra22 500) 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 ALLWISE and 2MASS photometry, together with Gaia DR2 photometry and parallaxes. Results. We derived precise atmospheric parameters Teff, log(g), and [M/H], along with the chemical abundances of [Fe/H], [α/M], [Mg/Fe], [Si/Fe], [Al/Fe], and [Ni/Fe] for 420 165 RAVE spectra. The precision typically amounts to 60 K 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, as long as we operate in those parts of the parameter space that are well-covered by the training sample. Scientific validation confirms the robustness of the CNN results. We provide a catalogue of CNN-Trained atmospheric parameters and abundances along with their uncertainties for 420 165 stars in the RAVE survey. Conclusions. 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.
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| 6. |
- Tempel, E., et al.
(författare)
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Probabilistic fibre-to-target assignment algorithm for multi-object spectroscopic surveys
- 2020
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Ingår i: Astronomy and Astrophysics. - : EDP Sciences. - 0004-6361 .- 1432-0746. ; 635
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Tidskriftsartikel (refereegranskat)abstract
- Context. Several new multi-object spectrographs are currently planned or under construction that are capable of observing thousands of Galactic and extragalactic objects simultaneously. Aims. In this paper we present a probabilistic fibre-to-target assignment algorithm that takes spectrograph targeting constraints into account and is capable of dealing with multiple concurrent surveys. We present this algorithm using the 4-m Multi-Object Spectroscopic Telescope (4MOST) as an example. Methods. The key idea of the proposed algorithm is to assign probabilities to fibre-target pairs. The assignment of probabilities takes the fibre positioner's capabilities and constraints into account. Additionally, these probabilities include requirements from surveys and take the required exposure time, number density variation, and angular clustering of targets across each survey into account. The main advantage of a probabilistic approach is that it allows for accurate and easy computation of the target selection function for the different surveys, which involves determining the probability of observing a target, given an input catalogue. Results. The probabilistic fibre-to-target assignment allows us to achieve maximally uniform completeness within a single field of view. The proposed algorithm maximises the fraction of successfully observed targets whilst minimising the selection bias as a function of exposure time. In the case of several concurrent surveys, the algorithm maximally satisfies the scientific requirements of each survey and no specific survey is penalised or prioritised. Conclusions. The algorithm presented is a proposed solution for the 4MOST project that allows for an unbiased targeting of many simultaneous surveys. With some modifications, the algorithm may also be applied to other multi-object spectroscopic surveys.
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