arXiv subjects

M. Valentini

Publications and source records attributed to M. Valentini.

At least 19 recordsLinked to original sources

arXiv2025

Abundances of P, S, and K in 58 bulge spheroid stars from APOGEE

We have previously studied several elements in 58 selected bulge spheroid stars, based on spectral lines in the H-band. We now derive the abundances of the less-studied elements phosphorus (P; Z=15), sulphur (S; Z=16), and potassium (K; Z=19). The abundances of P, S, and K in 58 bulge spheroid stars are compared both with the results of a previous analysis of the data from the Apache Point Observatory Galactic Evolution Experiment (APOGEE), and with a few available studies of these elements. We derive the individual abundances through spectral synthesis, using the stellar physical parameters available for our sample from the DR17 release of the APOGEE project. We provide recommendations for the best lines to be used for the studied elements among those in the H-band. We also compare the present results, together with literature data, with chemical-evolution models. Finally, the neutrino-process was taken into account for the suitable fit to the odd-Z elements P and K. We confirm that the H-band has useful lines for the derivation of the elements P, S, and K in moderately metal-poor stars. The abundances, plotted together with literature results from high-resolution spectroscopy, indicate that: moderately enhanced phosphorus stars are found, reminiscent results obtained for thick disk and halo stars of metallicity [Fe/H]~-1.0. Therefore, for the first time, we identify this effect to occur in the old stars from the bulge spheroid. Sulphur is an alpha-element and behaves as such. Potassium and sulphur both exhibit some star-to-star scatter, but fit within the expectations from chemical evolution models.

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arXiv2024

Abundances of iron-peak elements in 58 bulge spheroid stars from APOGEE

Stars presently identified in the bulge spheroid are probably very old, and their abundances can be interpreted as due to the fast chemical enrichment of the early Galactic bulge. The abundances of the iron-peak elements are important tracers of nucleosynthesis processes, in particular oxygen burning, silicon burning, the weak s-process, and alpha-rich freeze-out. Aims. The aim of this work is to derive the abundances of V, Cr, Mn, Co, Ni, and Cu in 58 bulge spheroid stars and to compare them with the results of a previous analysis of data from APOGEE. We selected the best lines for V, Cr, Mn, Co, Ni, and Cu located within the H-band of the spectrum, identifying the most suitable ones for abundance determination, and discarding severe blends. Using the stellar physical parameters available for our sample from the DR17 release of the APOGEE project, we derived the individual abundances through spectrum synthesis. We then complemented these measurements with similar results from different bulge field and globular cluster stars, in order to define the trends of the individual elements and compare with the results of chemical-evolution models. We verify that the H-band has useful lines for the derivation of the elements V, Cr, Mn, Co, Ni, and Cu in moderately metal-poor stars. The resulting abundances indicate that: V, Cr, and Ni vary in lockstep with Fe; Co tends to vary in lockstep with Fe, but could be showing a slight decrease with decreasing metallicity; and Mn and Cu decrease with decreasing metallicity. These behaviours are well reproduced by chemical-evolution models except for Cu, which appears to drop faster than the models predict for moderate metallicities. Finally, abundance indicators combined with kinematical and dynamical criteria appear to show that our 58 sample stars are likely to have originated in situ.

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arXiv2024

Transferring spectroscopic stellar labels to 217 million Gaia DR3 XP stars with SHBoost

In this paper, we explore the feasibility of using machine learning regression as a method of extracting basic stellar parameters and line-of-sight extinctions from spectro-photometric data. We built a stable gradient-boosted random-forest regressor (xgboost), trained on spectroscopic data, capable of producing output parameters with reliable uncertainties from Gaia DR3 data (most notably the low-resolution XP spectra), without ground-based spectroscopic observations. Using Shapley additive explanations, we interpret how the predictions for each star are influenced by each data feature. For the training and testing of the network, we used high-quality parameters obtained from the StarHorse code for a sample of around eight million stars observed by major spectroscopic stellar surveys, complemented by curated samples of hot stars, very metal-poor stars, white dwarfs, and hot sub-dwarfs. The training data cover the whole sky, all Galactic components, and almost the full magnitude range of the Gaia DR3 XP sample of more than 217 million objects that also have reported parallaxes. We have achieved median uncertainties of 0.20 mag in V-band extinction, 0.01 dex in logarithmic effective temperature, 0.20 dex in surface gravity, 0.18 dex in metallicity, and $12\%$ in mass (over the full Gaia DR3 XP sample, with considerable variations in precision as a function of magnitude and stellar type). We succeeded in predicting competitive results based on Gaia DR3 XP spectra compared to classical isochrone or spectral-energy distribution fitting methods we employed in earlier works, especially for parameters $A_V$ and $T_{\rm eff}$, along with the metallicity values. Finally, we showcase some potential applications of this new catalogue, including extinction maps, metallicity trends in the Milky Way, and extended maps of young massive stars, metal-poor stars, and metal-rich stars). [abridged]

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arXiv2024

MINCE II. Neutron capture elements

The MINCE (Measuring at Intermediate metallicity Neutron-Capture Elements) project aims to gather the abundances of neutron-capture elements but also of light elements and iron peak elements in a large sample of giant stars in this metallicity range. T The aim of this work is to study the chemical evolution of galactic sub-components recently identified (i.e. Gaia Sausage Enceladus (GSE), Sequoia). We used high signal-to-noise ratios, high-resolution spectra and standard 1D LTE spectrum synthesis to determine the detailed abundances. We could determine the abundances for up to 10 neutron-capture elements (Sr, Y, Zr, Ba, La, Ce, Pr, Nd, Sm and Eu) in 33 stars. The general trends of abundance ratios [n-capture element/Fe] versus [Fe/H] are in agreement with the results found in the literature. When our sample is divided in sub-groups depending on their kinematics, we found that the run of [Sr/Ba] vs [Ba/H] for the stars belonging to the GSE accretion event shows a tight anti-correlation. The results for the Sequoia stars, although based on a very limited sample, shows a [Sr/Ba] systematically higher than the [Sr/Ba] found in the GSE stars at a given [Ba/H] hinting at a different nucleosynthetic history. Stochastic chemical evolution models have been computed to understand the evolution of the GSE chemical composition of Sr and Ba. The first conclusions are that the GSE chemical evolution is similar to the evolution of a dwarf galaxy with galactic winds and inefficient star formation. Detailed abundances of neutron-capture elements have been measured in high-resolution, high signal-to-noise spectra of intermediate metal-poor stars, the metallicity range covered by the MINCE project. These abundances have been compared to detailed stochastic models of galactic chemical evolution.

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arXiv2023

K2 results for "young" $\alpha$-rich stars in the Galaxy

The origin of apparently young $\alpha$-rich stars in the Galaxy is still a matter of debate in Galactic archaeology, whether they are genuinely young or might be products of binary evolution and merger/mass accretion. We aim to shed light on the nature of young $\alpha$-rich stars in the Milky Way by studying their distribution in the Galaxy thanks to an unprecedented sample of giant stars that cover different Galactic regions and have precise asteroseismic ages, chemical, and kinematic measurements. We analyze a new sample of $\sim$ 6000 stars with precise ages coming from asteroseismology. Our sample combines the global asteroseismic parameters measured from light curves obtained by the K2 mission with stellar parameters and chemical abundances obtained from APOGEE DR17 and GALAH DR3, then cross-matched with Gaia DR3. We define our sample of young $\alpha$-rich stars and study their chemical, kinematic, and age properties. We investigate young $\alpha$-rich stars in different parts of the Galaxy and we find that the fraction of young $\alpha$-rich stars remains constant with respect to the number of high-$\alpha$ stars at $\sim$ 10%. Furthermore, young $\alpha$-rich stars have kinematic and chemical properties similar to high-$\alpha$ stars, except for [C/N] ratios. This suggests that these stars are not genuinely young, but products of binary evolution and merger/mass accretion. Under that assumption, we find the fraction of these stars in the field to be similar to that found recently in clusters. This fact suggests that $\sim$ 10% of the low-$\alpha$ field stars could also have their ages underestimated by asteroseismology. This should be kept in mind when using asteroseismic ages to interpret results in Galactic archaeology.

astro-ph.GA↗

arXiv2023

Beyond Gaia DR3: Tracing the [$\alpha$/M]-[M/H] bimodality from the inner to the outer Milky Way disc with Gaia-RVS and convolutional neural networks

In June 2022, Gaia DR3 has provided the astronomy community with about one million spectra from the Radial Velocity Spectrometer (RVS) covering the CaII triplet region. 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. 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. 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 [{\alpha}/M]). We trained the CNN with a high-quality training sample based on APOGEE DR17 labels. With this CNN, we derived homogeneous atmospheric parameters and abundances for 886080 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 [{\alpha}/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. 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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arXiv2022

MINCE I. Presentation of the project and of the first year sample

In recent years, Galactic archaeology has become a particularly vibrant field of astronomy, with its main focus set on the oldest stars of our Galaxy. In most cases, these stars have been identified as the most metal-poor. However, the struggle to find these ancient fossils has produced an important bias in the observations - in particular, the intermediate metal-poor stars (-2.5<[Fe/H]< -1.5) have been frequently overlooked. The missing information has consequences for the precise study of the chemical enrichment of our Galaxy, in particular for what concerns neutron-capture elements and it will be only partially covered by future multi-object spectroscopic surveys such as WEAVE and 4MOST. Measuring at Intermediate Metallicity Neutron Capture Elements (MINCE) is gathering the first high-quality spectra (high S/N ratio and high resolution) for several hundreds of bright and metal-poor stars, mainly located in our Galactic halo. We compiled our selection mainly on the basis of Gaia data and determined the stellar atmospheres of our sample and the chemical abundances of each star. In this paper, we present the first sample of 59 spectra of 46 stars. We measured the radial velocities and computed the Galactic orbits for all stars. We found that 8 stars belong to the thin disc, 15 to disrupted satellites, and the remaining cannot be associated to the mentioned structures, and we call them halo stars. For 33 of these stars, we provide abundances for the elements up to zinc. We also show the chemical evolution results for eleven chemical elements, based on recent models. Our observational strategy of using multiple telescopes and spectrographs to acquire high S/N and high-resolution spectra has proven to be very efficient since the present sample was acquired over only about one year of observations. Finally, our target selection strategy proved satisfactory for our purposes.

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arXiv2022

The Gaia-ESO Survey: Preparing the ground for 4MOST & WEAVE galactic surveys. Chemical evolution of lithium with machine learning

With its origin coming from several sources (Big Bang, stars, cosmic rays) and given its strong depletion during its stellar lifetime, the lithium element is of great interest as its chemical evolution in the Milky Way is not well understood at present. To help constrain stellar and galactic chemical evolution models, numerous and precise lithium abundances are necessary for a large range of evolutionary stages, metallicities, and Galactic volume. In the age of stellar parametrization on industrial scales, spectroscopic surveys such as APOGEE, GALAH, RAVE, and LAMOST have used data-driven methods to rapidly and precisely infer stellar labels (atmospheric parameters and abundances). To prepare the ground for future spectroscopic surveys such as 4MOST and WEAVE, we aim to apply machine learning techniques to lithium measurements and analyses. We trained a convolution neural network (CNN), coupling Gaia-ESO Survey iDR6 stellar labels (Teff, log(g), [Fe/H], and A(Li)) and GIRAFFE HR15N spectra, to infer the atm parameters and lithium abundances for ~40,000 stars. We show that the CNN properly learns the physics of the stellar labels, from relevant spectral features through a broad range of evolutionary stages and stellar parameters. The Li feature at 6707.8 A is successfully singled out by our CNN, among the thousands of lines. Rare objects such as Li-rich giants are found in our sample. This level of performance is achieved thanks to a meticulously built, high-quality, and homogeneous training sample. The CNN approach is very well adapted for the next generations of spectroscopic surveys aimed at studying (among other elements) lithium, such as the 4MIDABLE-LR/HR (4MOST Milky Way disk and bulge low- and high-resolution) surveys. In this context, the caveats of ML applications should be appropriately investigated, along with the realistic label uncertainties and upper limits for abundances.

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arXiv2022

Abundance analysis of APOGEE spectra for 58 metal-poor stars from the bulge spheroid

The central part of the Galaxy host a multitude of stellar populations, including the spheroidal bulge stars, stars moved to the bulge through secular evolution of the bar, inner halo, inner thick disk, inner thin disk, as well as debris from past accretion events. We identified a sample of 58 candidate stars belonging to the stellar population of the spheroidal bulge, and analyse their abundances. The present calculations of Mg, Ca, and Si lines are in agreement with the APOGEE-ASPCAP abundances, whereas abundances of C, N, O, and Ce are re-examined. We find normal $\alpha$-element enhancements in oxygen, similar to magnesium, Si, and Ca abundances, which are typical of other bulge stars surveyed in the optical in Baade's Window. The enhancement of [O/Fe] in these stars suggests that they do not belong to accreted debris. No spread in N abundances is found, and none of the sample stars is N-rich, indicating that these stars are not second generation stars originated in globular clusters. Ce instead is enhanced in the sample stars, which points to an s-process origin such as due to enrichment from early generations of massive fast rotating stars, the so-called spinstars

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arXiv2022

The Gaia-ESO Public Spectroscopic Survey: Motivation, implementation, GIRAFFE data processing, analysis, and final data products

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 homogenisation of other and future stellar surveys and Gaia's astrophysical parameters. 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 (arXiv:2206.02901) introduces the survey results. 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. 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. 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.

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arXiv2022

The Gaia-ESO Public Spectroscopic Survey: Implementation, data products, open cluster survey, science, and legacy

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 (GES), 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. 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 (Gilmore et al.) reviews the overall survey motivation, strategy, Giraffe pipeline data reduction, organisation, and workflow. The GES 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. The final catalogue has been released through the ESO archive at the end of May 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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arXiv2022

Industrially Microfabricated Ion Trap with 1 eV Trap Depth

Scaling trapped-ion quantum computing will require robust trapping of at least hundreds of ions over long periods, while increasing the complexity and functionality of the trap itself. Symmetric 3D structures enable high trap depth, but microfabrication techniques are generally better suited to planar structures that produce less ideal conditions for trapping. We present an ion trap fabricated on stacked 8-inch wafers in a large-scale MEMS microfabrication process that provides reproducible traps at a large volume. Electrodes are patterned on the surfaces of two opposing wafers bonded to a spacer, forming a 3D structure with 2.5 micrometer standard deviation in alignment across the stack. We implement a design achieving a trap depth of 1 eV for a calcium-40 ion held at 200 micrometers from either electrode plane. We characterize traps, achieving measurement agreement with simulations to within +/-5% for mode frequencies spanning 0.6--3.8 MHz, and evaluate stray electric field across multiple trapping sites. We measure motional heating rates over an extensive range of trap frequencies, and temperatures, observing 40 phonons/s at 1 MHz and 185 K. This fabrication method provides a highly scalable approach for producing a new generation of 3D ion traps.

quant-ph↗

arXiv2021

Barium lines in high-quality spectra of two metal-poor giants in the Galactic halo

Context. Theoretical results showed the possibility that neutron capture elements were produced in the early Universe by two different sources: a frequent s-process source hosted by rotating massive stars, and a rare r-process source hosted most likely by neutron star mergers. The two sources produce barium with different isotopic compositions. Aims. We aim to investigate the lines of barium in two halo stars, HD 6268 and HD 4306. The spectra present an exquisite quality, both in terms of resolution (R > 100'000) and signal-to-noise (400). Due to hyperfine splitting (hfs) effects, barium lines are expected to show slightly different profiles depending on the barium isotopic fraction. Methods. We applied a standard local thermodynamic equilibrium synthesis of the barium lines. We compared the synthetic results assuming an s-process isotopic pattern or an r-process isotopic pattern for the two barium lines for each star that exhibited hfs. We also applied a methodology, less dependent on the accuracy of the theoretical Ba hfs structure, that transforms the lines of HD 4306 into those we would observe if its atmospheric parameter values (i.e. Teff, log g, micro- and macro-turbulence, Vsin i, and Ba abundance) were the same as those of HD 6268. Results. With both methods, our results show that the barium lines with hfs effects of HD 4306 are in agreement with an s-process composition and the lines in HD 6268 have a different profile, which is most likely linked to the presence of an r-process isotopic pattern. Conclusions. Two lines of barium of HD 6268 and HD 4306 seem to confirm the theoretical expectation that both r-process events and also s-process contribution by rotating massive stars have polluted the ancient halo of our Galaxy.

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arXiv2021

The formation history of the Milky Way disc with high-resolution cosmological simulations

We analyse from an observational perspective the formation history and kinematics of a Milky Way-like galaxy from a high-resolution zoom-in cosmological simulation that we compare to those of our Galaxy as seen by Gaia DR2 to better understand the origin and evolution of the Galactic thin and thick discs. The cosmological simulation was carried out with the GADGET-3 TreePM+SPH code using the MUlti Phase Particle Integrator (MUPPI) model. We disentangle the complex overlapping of stellar generations that rises from the top-down and inside-out formation of the galactic disc. We investigate cosmological signatures in the phase-space of mono-age populations and highlight features stemming from past and recent dynamical perturbations. In the simulation, we identify a satellite with a stellar mass of $1.2 \times 10^9$ M$_\odot$, i.e. stellar mass ratio $\Delta \sim 5.5$ per cent at the time, accreted at $z \sim 1.6$, which resembles the major merger Gaia-Sausage-Enceladus that produced the Galactic thick disc, i.e. $\Delta \sim 6$ per cent. We found at $z \sim 0.5-0.4$ two merging satellites with a stellar mass of $8.8 \times 10^8$ M$_\odot$ and $5.1 \times 10^8$ M$_\odot$ that are associated to a strong starburst in the Star Formation History, which appears fairly similar to that recently found in the Solar Neighbourhood. Our findings highlight that detailed studies of coeval stellar populations kinematics, which are made available by current and future Gaia data releases and in synergy with simulations, are fundamental to unravel the formation and evolution of the Milky Way discs.

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arXiv2020

The RAdial Velocity Experiment (RAVE): Parameterisation of RAVE spectra based on convolutional neural networks

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.

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arXiv2019

From the bulge to the outer disc: StarHorse stellar parameters, distances, and extinctions for stars in APOGEE DR16 and other spectroscopic surveys

We combine high-resolution spectroscopic data from APOGEE-2 Survey Data Release 16 (DR16) with broad-band photometric data from several sources, as well as parallaxes from {\it Gaia} Data Release 2 (DR2). Using the Bayesian isochrone-fitting code {\tt StarHorse}, we derive distances, extinctions and astrophysical parameters for around 388,815 APOGEE stars, achieving typical distance uncertainties of $\sim 6\%$ for APOGEE giants, $\sim 2\%$ for APOGEE dwarfs, as well as extinction uncertainties of $\sim 0.07$ mag when all photometric information is available, and $\sim 0.17$ mag if optical photometry is missing. {\tt StarHorse} uncertainties vary with the input spectroscopic catalogue, with the available photometry, and with the parallax uncertainties. To illustrate the impact of our results, we show that, thanks to {\it Gaia} DR2 and the now larger sky coverage of APOGEE-2 (including APOGEE-South), we obtain an extended map of the Galactic plane, providing unprecedented coverage of the disk close to the Galactic mid-plane ($|Z_{Gal}|<1$ kpc) from the Galactic Centre out to $R_{\rm Gal}\sim 20$ kpc. The improvements in statistics as well as distance and extinction uncertainties unveil the presence of the bar in stellar density, as well as the striking chemical duality in the innermost regions of the disk, now clearly extending to the inner bulge. We complement this paper with distances and extinctions for stars in other public released spectroscopic surveys: 324,999 in GALAH DR2, 4,928,715 in LAMOST DR5, 408,894 in RAVE DR6, and 6,095 in GES DR3

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arXiv2019

The K2 Galactic Caps Project -- Going Beyond the \textit{Kepler} Field and Ageing the Galactic Disc

Analyses of data from spectroscopic and astrometric surveys have led to conflicting results concerning the vertical characteristics of the Milky Way. Ages are often used to provide clarity, but typical uncertainties of $>$ 40\,\% restrict the validity of the inferences made. Using the \textit{Kepler} APOKASC sample for context, we explore the global population trends of two K2 campaign fields (3 and 6), which extend further vertically out of the Galactic plane than APOKASC. We analyse the properties of red giant stars utilising three asteroseismic data analysis methods to cross-check and validate detections. The Bayesian inference tool PARAM is used to determine the stellar masses, radii and ages. Evidence of a pronounced red giant branch bump and an [$\alpha$/Fe] dependence on the position of the red clump is observed from the radii distribution of the K2 fields. Two peaks in the age distribution centred at $\sim$5 and and $\sim$12 Gyr are found using a sample with $\sigma_{\rm{age}}$ $<$ 35\,\%. In a comparison with \textit{Kepler}, we find the older peak to be more prominent for K2. This age bimodality is also observed based on a chemical selection of low- ($\leq$ 0.1) and high- ($>$ 0.1) [$\alpha$/Fe] stars. As a function of vertical distance from the Galactic mid-plane ($|Z|$), the age distribution shows a transition from a young to old stellar population with increasing $|Z|$ for the K2 fields. Further coverage of campaign targets with high resolution spectroscopy is required to increase the yield of precise ages achievable with asteroseismology.

astro-ph.SR↗

arXiv2019

4MOST Scientific Operations

The 4MOST instrument is a multi-object spectrograph that will address Galactic and extragalactic science cases simultaneously by observing targets from a large number of different surveys within each science exposure. This parallel mode of operation and the survey nature of 4MOST require some distinct 4MOST-specific operational features within the overall operations model of ESO. The main feature is that the 4MOST Consortium will deliver, not only the instrument, but also contractual services to the user community, which is why 4MOST is also described as a facility. This white paper concentrates on information particularly useful to answering the forthcoming Call for Letters of Intent.

astro-ph.IM↗