arXiv subjects

M. Bergemann

Publications and source records attributed to M. Bergemann.

At least 19 recordsLinked to original sources

arXiv2026

The PLATO field selection process III. Selection of the Prime Sample for the LOPS2 field

The PLanetary Transits and Oscillations of stars (PLATO) mission will begin its four-year nominal mission in early 2027 by monitoring its Long-duration Observation Phase field at South (LOPS2) for at least two years continuously. The primary aim of PLATO is a very ambitious and challenging one: the discovery of Earth-like planets in the habitable zone of nearby and bright solar analogues. To this purpose, the PLATO Mission Consortium, through its Ground-based Observing Program, will perform the follow-up needed to confirm part of the candidate planets photometrically detected by PLATO and measure their masses through radial velocity curves. For the LOPS2, the Ground-based Observing Program is committed (as part of the PLATO mission) to follow-up the candidate exoplanets discovered orbiting the 15,000 high-quality target subset of the PLATO Input Catalog (PIC) known as the Prime Sample. The Prime Sample will be made public nine months before launch in the context of the first Guest Observer call for proposals to be issued by the European Space Agency. Here, we present the quantitative metrics and thresholds defined to select and prioritize the Prime Sample. Our method is perfectly general and suitable to rank any list of stars surveyed for transiting planets. We also describe the astrophysical properties of the LOPS2 Prime Sample, both in a statistical sense and for some specific targets of interest.

astro-ph.EP↗

arXiv2026

On the Use of Field RR Lyrae as Galactic Probes -- VIII. Early Formation of the Galactic Spheroid

We introduce a new photometric catalog of RR Lyrae variables (RRLs, $\sim$300,000) mainly based on data available in public datasets. We also present the largest and most homogeneous spectroscopic dataset of RRLs and Blue Horizontal Branch [BHB] stars ever collected. This includes radial velocity measurements ($\sim$16,000) and iron abundances ($\Delta$S method for 8,140 RRLs, plus 547 from literature). Elemental abundances based on high-resolution spectra are provided for 487 RRLs and 64 BHB stars. We identified candidate RRLs associated to the main Galactic components and their iron distribution function (IDF) becomes more metal-rich when moving from the Halo ([Fe/H]=-1.56) to the Thick (TCD; [Fe/H]=-1.47) and Thin (TND; [Fe/H]=-0.73) disk. Furthermore, Halo RRLs and RRLs in retrograde orbits are $\alpha$-enhanced ([$\alpha$/Fe]=0.27, $\sigma$=0.18), while TCD RRLs are either $\alpha$-enhanced ([Fe/H]$\le$-1.0) or $\alpha$-poor ([Fe/H]$>$-1.0), and TND RRLs are mainly $\alpha$-poor ([$\alpha$/Fe]=-0.01, $\sigma$=0.20). We also identified RRLs associated to the main stellar streams (Gaia-Sausage-Enceladus [GSE]; Sequoia, Helmi, Sagittarius) and we found that their IDFs are quite similar to Halo RRLs. However, GSE RRLs lack the metal-poor/metal-rich tails and their $\alpha$-element distribution is quite compact. The iron radial gradient in Galactocentric distance for TND, TCD and Halo RRLs is negative and it decreases from -0.026, to -0.010, and to -0.002 dex/kpc. The iron radial gradient based on dry Halo (Halo without substructures) RRLs is, within the errors, equal to the global Halo. We also found a strong similarity between iron and [$\alpha$/Fe] radial gradients of Milky Way RRLs and M31 globular clusters throughout the full range of galactocentric distances covered by the two samples.

astro-ph.GA↗

arXiv2025

Milky Way disc & Bulge in situ populations: ESO white paper - Expanding horizons call

The formation and evolution of the Milky Way's disc, bar, and bulge remain fundamentally limited by the lack of a contiguous, Galaxy-wide, high-precision chemo-dynamical map. Key open questions - including the survival or destruction of the primitive discs, the origin of the bulge's multi-component structure, the role of mergers and secular processes, and the coupling between stellar chemistry, dynamics, and the Galactic potential - cannot be fully resolved with current or planned facilities. Existing spectroscopic surveys provide either high resolution for small samples or wide coverage at insufficient resolution and depth, and none can obtain homogeneous abundances, precise 3D kinematics, and reliable ages for the millions of stars required, particularly in the obscured midplane, the far side of the bar, or the outer, low-density disc. A new wide-field, massively multiplexed, large-aperture spectroscopic facility, capable of both high- and low-resolution spectroscopy over tens of thousands of square degrees, is therefore essential. Such a facility would deliver the statistical power, sensitivity, and completeness needed to reconstruct the Galaxy's assembly history, constrain its gravitational potential, and establish the Milky Way as the definitive benchmark for galaxy evolution.

astro-ph.IM↗

arXiv2024

3D NLTE modelling of Y and Eu. Centre-to-limb variation and solar abundances

Context. Abundances of s- and r-process elements in Sun-like stars constrain nucleosynthesis in extreme astrophysical events, such as compact binary mergers and explosions of highly magnetised rapidly rotating massive stars. Aims. We measure solar abundances of yttrium (Y) and europium (Eu) using 3D non-local thermal equilibrium (NLTE) models. We use the model to determine the abundance of Y, and also explore the model's ability to reproduce the solar centre-to-limb variation of its lines. In addition, we determine the Eu abundance using solar disc-centre and integrated flux spectra. Methods. We developed an NLTE model of Eu and updated our model of Y with collisional data from detailed quantum-mechanical calculations. We used the IAG spatially resolved high-resolution solar spectra to derive the solar abundances of Y across the solar disc and of Eu for integrated flux and at disc centre using a set of carefully selected lines and a 3D radiation-hydrodynamics model of the solar atmosphere. Results. We find 3D NLTE solar abundances of A(Y)$_{\textrm{3D NLTE}}$=$2.30 \pm 0.03_{\textrm{stat}} \pm 0.07_{\textrm{syst}}$ dex based on observations at all angles and A(Eu)$_{\textrm{3D NLTE}}$=$0.57 \pm 0.01_{\textrm{stat}} \pm 0.06_{\textrm{syst}}$ dex based on the integrated flux and disc-centre intensity. 3D NLTE modelling offers the most consistent abundances across the solar disc, and resolves the problem of severe systematic bias in Y and Eu abundances inherent to 1D LTE, 1D NLTE, and 3D LTE modelling.

astro-ph.SR↗

arXiv2023

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

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 [α/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 [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. 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.

astro-ph.GA↗

arXiv2023

Observational constraints on the origin of the elements. VIII. Constraining the Barium, Strontium and Yttrium chemical evolution in metal-poor stars

Recently Lian et al. (2023), thanks to Gaia-ESO data, studied the chemical evolution of neutron-capture elements in the regime [Fe/H]>-1. We aim here to complement this study down to [Fe/H]=-3, and focus on Ba, Y, Sr, and abundance ratios of [Ba/Y] and [Sr/Y], which give comprehensive views on s-process nucleosynthesis channels. We measured LTE and NLTE abundances of Ba, Y, and Sr in 323 Galactic metal-poor stars using high-resolution optical spectra with high S/N. We used the spectral fitting code TSFitPy, together with 1D model atmospheres using previously determined LTE and NLTE atmospheric parameters. The NLTE effects are on the order of -0.1 to ~0.2dex depending on the element. T he ratio between heavy and light s-process elements [Ba/Y] varies weakly with [Fe/H] even in the metal-poor regime, consistently with the behavior in the metal-rich regime. The [Ba/Y] scatter at a given metallicity is larger than the abundance measurement uncertainties. Homogeneous chemical evolution models with different yields prescriptions are unable to accurately reproduce the [Ba/Y] scatter at low-[Fe/H]. Adopting the stochastic chemical evolution model by Cescutti & Chaippini (2014) allows to reproduce the observed scatter in the abundance pattern of [Ba/Y] and [Ba/Sr]. With our observations, we rule out the need for an arbitrary scaling of the r-process contribution as previously suggested by the model authors. We have showed how important it is to properly include NLTE effects when measuring chemical abundances, especially in the metal-poor regime. This work shows that the choice of the Galactic chemical evolution model (stochastic vs. 1-zone) is key when comparing models to observations. The upcoming surveys such as 4MOST and WEAVE will deliver high quality spectra of many thousands of metal-poor stars, and this work gives a typical case study of what could be achieved with such surveys.

astro-ph.SR↗

arXiv2023

Spectroscopic analysis of hot, massive stars in large spectroscopic surveys with de-idealised models

Upcoming large-scale spectroscopic surveys with e.g. WEAVE and 4MOST will provide thousands of spectra of massive stars, which need to be analysed in an efficient and homogeneous way. Usually, studies of massive stars are limited to samples of a few hundred objects which pushes current spectroscopic analysis tools to their limits because visual inspection is necessary to verify the spectroscopic fit. Often uncertainties are only estimated rather than derived and prior information cannot be incorporated without a Bayesian approach. In addition, uncertainties of stellar atmospheres and radiative transfer codes are not considered as a result of simplified, inaccurate or incomplete/missing physics or, in short, idealised physical models. Here, we address the question of "How to compare an idealised model of complex objects to real data?" with an empirical Bayesian approach and maximum a {\it posterior} approximations. We focus on application to large scale optical spectroscopic studies of complex astrophysical objects like stars. More specifically, we test and verify our methodology on samples of OB stars in 30 Doradus region of the Large Magellanic Clouds using a grid of FASTWIND model atmospheres. Our spectroscopic model de-idealisation analysis pipeline takes advantage of the statistics that large samples provide by determining the model error to account for the idealised stellar atmosphere models, which are included into the error budget. The pipeline performs well over a wide parameter space and derives robust stellar parameters with representative uncertainties.

astro-ph.SR↗

arXiv2023

Oxygen, sulfur, and iron radial abundance gradients of classical Cepheids across the Galactic thin disk

Classical Cepheids (CCs) are solid distance indicators and tracers of young stellar populations. Our aim is to provide iron, oxygen, and sulfur abundances for the largest and most homogeneous sample of Galactic CCs ever analyzed. The current sample covers a wide range in Galactocentric distances (RG), pulsation modes and periods. High-resolution and high S/N spectra collected with different spectrographs were adopted to estimate the atmospheric parameters. Individual distances are based on Gaia trigonometric parallaxes or on near-infrared Period-Luminosity relations. We found that Fe and alpha-element radial gradients based on CCs display a well-defined change in the slope for RG larger than 12 kpc. Radial gradients based on open clusters, covering a wide range in age, display similar trends, meaning that the flattening in the outer disk is an intrinsic feature of the radial gradients since it is independent of age. Empirical evidence indicates that the radial gradient for S is steeper than for Fe. The difference in the slope is a factor of two in the linear fit. We also found that S is, on average, under-abundant compared with O. We performed a detailed comparison with Galactic chemical evolution models and we found that a constant Star Formation Efficiency for RG larger than 12 kpc takes account for the flattening in both Fe and alpha-elements. To further constrain the impact that predicted S yields for massive stars have on radial gradients, we adopted a "toy model" and we found that the flattening in the outermost regions requires a decrease of a factor of four in the current S predictions. Sulfur photospheric abundances, compared with other alpha-elements, have the key advantage of being a volatile element. Therefore, stellar S abundances can be directly compared with nebular S abundances in external galaxies.

astro-ph.GA↗

arXiv2023

Observational constraints on the origin of the elements. V. Non-LTE abundance ratios of [Ni/Fe] in Galactic stars and enrichment by sub-Chandrasekhar mass SNe

We constrain the role of different SN Ia channels in the chemical enrichment of the Galaxy by studying the abundances of nickel in Galactic stars. We investigate four different SN Ia sub-classes, including the classical single-degenerate near-Chandrasekhar mass SN Ia, the fainter SN Iax systems associated with He accretion from the companion, as well as two sub-Ch mass SN Ia channels. The latter include the double-detonation of a white dwarf accreting helium-rich matter and violent white dwarf mergers. NLTE models of Fe and Ni are used in the abundance analysis. In the GCE models, we include new delay time distributions arising from the different SN Ia channels, as well as recent yields for core-collapse supernovae and AGB stars. The data-model comparison is performed using a Markov chain Monte Carlo framework that allows us to explore the entire parameter space allowed by the diversity of explosion mechanisms and the Galactic SN Ia rate, taking into account the uncertainties of the observed data. We show that NLTE effects have a non-negligible impact on the observed [Ni/Fe] ratios in the Galactic stars. The NLTE corrections to Ni abundances are not large, but strictly positive, lifting the [Ni/Fe] ratios by ~+0.15 dex at [Fe/H] =-2. We find that that the distributions of [Ni/Fe] in LTE and in NLTE are very tight, with a scatter of < 0.1 dex at all metallicities, supporting earlier work. In LTE, most stars have scaled-solar Ni abundances, [Ni/Fe] = 0, with a slight tendency for sub-solar [Ni/Fe] ratios at lower [Fe/H]. In NLTE, however, we find a mild anti-correlation between [Ni/Fe] and metallicity, and a slightly elevated [Ni/Fe] ratios at [Fe/H] < -1.0. The NLTE data can be explained by the GCE models calculated with a substantial, ~ 75%, fraction of sub-Ch SN Ia.

astro-ph.GA↗

arXiv2023

The Gaia-ESO Survey: homogenisation of stellar parameters and elemental abundances

The Gaia-ESO Survey is a public spectroscopic survey that has targeted $\gtrsim10^5$ stars covering all major components of the Milky Way from the end of 2011 to 2018, delivering its public final 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 ($T_\mathrm{eff} \sim 30,000-52,000$~K) all the way to K-M ($\gtrsim$3,500~K). The physics throughout these stellar regimes varies significantly, which has previously prohibited any detailed comparisons between stars of significantly different type. 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, e.g., 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, which specialise in 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 to arrive at a single set of recommended results for all Survey collaborators to use. We also present some general quality and consistency checks performed over all Survey results.

astro-ph.SR↗

arXiv2023

Solar oxygen abundance using SST/CRISP center-to-limb observations of the O I 7772 Å line

Solar oxygen abundance measurements based on the O I near-infrared triplet have been a much-debated subject for several decades since non-local thermodynamic equilibrium (NLTE) calculations with 3D radiation-hydrodynamics model atmospheres introduced a large change to the 1D LTE modelling. In this work, we aim to test solar line formation across the solar disk using new observations obtained with the SST/CRISP instrument. The observed dataset is based on a spectroscopic mosaic stretching from disk center to the solar limb. By comparing the state-of-the-art 3D NLTE models with the data, we find that the 3D NLTE models provide an excellent description of line formation across the disk. We obtain an abundance value of $A(\mathrm{O}) = (8.73 \pm 0.03)$ dex, with a very small angular dispersion across the disk. We conclude that spectroscopic mosaics are excellent probes for geometric and physical properties of hydrodynamics models and non-LTE line formation.

astro-ph.SR↗

arXiv2023

Gaia-ESO Survey: massive stars in the Carina Nebula. A new census of OB stars

The Gaia-ESO survey sample of massive OB stars in the Carina Nebula consists of 234 stars. The addition of brighter sources from the Galactic O-Star Spectroscopic Survey and additional sources from the literature allows us to create the most complete census of massive OB stars done so far in the region. It contains a total of 316 stars, being 18 of them in the background and four in the foreground. Of the 294 stellar systems in Car OB1, 74 are of O type, 214 are of non-supergiant B type and 6 are of WR or non-O supergiant (II to Ia) spectral class. We identify 20 spectroscopic binary systems with an O-star primary, of which 6 are reported for the first time, and another 18 with a B-star primary, of which 13 are new detections. The average observed double-lined binary fraction of O-type stars in the surveyed region is 0.35, which represents a lower limit. We find a good correlation between the spectroscopic n-qualifier and the projected rotational velocity of the stars. The fraction of candidate runaways among the stars with and without the n-qualifier is 4.4% and 2.4%, respectively, although non resolved double-lined binaries can be contaminating the fast rotators sample.

astro-ph.SR↗

arXiv2023

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.

astro-ph.GA↗

arXiv2022

NLTE analysis of the methylidyne radical (CH) molecular lines in metal-poor stellar atmospheres

An analysis of the CH molecule in non-local thermodynamic equilibrium (NLTE) is performed for the physical conditions of cool stellar atmospheres typical of red giants (log g = 2.0, Teff = 4500 K) and the Sun. The aim of the present work is to explore whether the G-band of the CH molecule, which is commonly used in abundance diagnostics of Carbon-Enhanced Metal-Poor (CEMP) stars, is sensitive to NLTE effects. LTE and NLTE theoretical spectra are computed with the MULTI code. We use one-dimensional (1D) LTE hydrostatic MARCS model atmospheres with parameters representing eleven red giant stars with metallicities ranging from [Fe/H] = -4.0 to [Fe/H] = 0.0 and carbon-to-iron ratios [C/Fe] = 0.0, +0.7, +1.5, and +3.0. The CH molecule model is represented by 1981 energy levels, 18377 radiative bound-bound transitions, and 932 photo-dissociation reactions. The rates due to transitions caused by collisions with free electrons and hydrogen atoms are computed using classical recipes. Our calculations suggest that NLTE effects in the statistical equilibrium of the CH molecule are significant and cannot be neglected for precision spectroscopic analysis of C abundances. The NLTE effects in the G-band increase with decreasing metallicity. We show that the C abundances are always under-estimated if LTE is assumed. The NLTE corrections to C abundance inferred from the CH feature range from +0.04 dex for the Sun to +0.21 dex for a red giant with metallicity [Fe/H] = -4.0. Departures from the LTE assumption in the CH molecule are non-negligible and NLTE effects have to be taken into account in the diagnostic spectroscopy based on the CH lines. We show here that the NLTE effects in the optical CH lines are non-negligible for the Sun and red giant stars, but further calculations are warranted to investigate the effects in other regimes of stellar parameters.

astro-ph.SR↗

arXiv2022

The Gaia-ESO Survey: Old super-metal-rich visitors from the inner Galaxy

We report the identification of a set of old super metal-rich dwarf stars with orbits of low eccentricity that reach a maximum height from the Galactic plane between ~0.5-1.5 kpc. We discuss their properties to understand their origins. We use data from the internal data release 6 of the Gaia-ESO Survey. We selected stars observed at high resolution with abundances of 21 species of 18 individual elements. We apply hierarchical clustering to group the stars with similar chemical abundances within the complete chemical abundance space. According to their chemical properties, this set of super metal-rich stars can be arranged into five subgroups. Four seem to follow a chemical enrichment flow, where nearly all abundances increase in lockstep with Fe. The fifth subgroup shows different chemical characteristics. All subgroups have the following features: median ages of the order of 7-9 Gyr, Solar or sub-Solar [Mg/Fe] ratios, maximum height between 0.5-1.5 kpc, low eccentricities, and a detachment from the expected metallicity gradient with guiding radius. The high metallicity of our stars is incompatible with a formation in the Solar neighbourhood. Their dynamic properties agree with theoretical expectations that these stars travelled from the inner Galaxy due to blurring and, most importantly, to churning. We suggest that most of this population's stars originated in the Milky Way's inner regions (inner disc and/or the bulge) and later migrated to the Solar neighbourhood. The region from where the stars originated had a complex chemical enrichment history, with contributions from supernovae types Ia and II and possibly asymptotic giant branch stars.

astro-ph.GA↗

arXiv2022

The Gaia-ESO survey: mapping the shape and evolution of the radial abundance gradients with open clusters

The spatial distribution of elemental abundances and their time evolution are among the major constraints to disentangle the scenarios of formation and evolution of the Galaxy. We used the sample of open clusters available in the final release of the Gaia-ESO survey to trace the Galactic radial abundance and abundance to iron ratio gradients, and their time evolution. We selected member stars in 62 open clusters, with ages from 0.1 to about 7~Gyr, located in the Galactic thin disc at Galactocentric radii from about 6 to 21~kpc. We analysed the shape of the resulting [Fe/H] gradient, the average gradients [El/H] and [El/Fe] combining elements belonging to four different nucleosynthesis channels, and their individual abundance and abundance ratio gradients. We also investigated the time evolution of the gradients dividing open clusters in three age bins. The[Fe/H] gradient has a slope of -0.054 dex~kpc-1. We saw different behaviours for elements belonging to different channels. We found that the youngest clusters in the inner disc have lower metallicity than their older counterpart and they outline a flatter gradient. We considered some possible explanations, including the effects of gas inflow and migration. We suggested that it might be a bias introduced by the standard spectroscopic analysis producing lower metallicities in low gravity stars. To delineate the shape of the `true' gradient, we should limit our analysis to stars with low surface gravity logg>2.5 and xi<1.8 km~s-1. Based on this reduced sample, we can conclude that the gradient has minimally evolved over the time-frame outlined by the open clusters, indicating a slow and stationary formation of the thin disc in the latest Gyr. We found a secondary role of clusters' migration in shaping the gradient, with a more prominent role of migration for the oldest clusters.

astro-ph.GA↗

arXiv2022

The Gaia-ESO Survey: Lithium measurements and new curves of growth

The Gaia-ESO Survey (GES) is a large public spectroscopic survey that was carried out using the multi-object FLAMES spectrograph at the Very Large Telescope. The survey provides accurate radial velocities, stellar parameters, and elemental abundances for ~115,000 stars in all Milky Way components. In this paper we describe the method adopted in the final data release to derive lithium equivalent widths (EWs) and abundances. Lithium EWs were measured using two different approaches for FGK and M-type stars, to account for the intrinsic differences in the spectra. For FGK stars, we fitted the lithium line using Gaussian components, while direct integration over a predefined interval was adopted for M-type stars. Care was taken to ensure continuity between the two regimes. Abundances were derived using a new set of homogeneous curves of growth that were derived specifically for GES, and which were measured on a synthetic spectral grid consistently with the way the EWs were measured. The derived abundances were validated by comparison with those measured by other analysis groups using different methods. Lithium EWs were measured for ~40,000 stars, and abundances could be derived for ~38,000 of them. The vast majority of the measures (80%) have been obtained for stars in open cluster fields. The remaining objects are stars in globular clusters, or field stars in the Milky Way disc, bulge, and halo. The GES dataset of homogeneous lithium abundances described here will be valuable for our understanding of several processes, from stellar evolution and internal mixing in stars at different evolutionary stages to Galactic evolution.

astro-ph.SR↗

arXiv2022

The Gaia-ESO Survey: Chemical evolution of Mg and Al in the Milky Way with Machine-Learning

We aim to prepare the machine-learning ground for the next generation of spectroscopic surveys, such as 4MOST and WEAVE. Our goal is to show that convolutional neural networks can predict accurate stellar labels from relevant spectral features in a physically meaningful way. We built a neural network and trained it on GIRAFFE spectra with associated stellar labels from the sixth internal Gaia-ESO data release. Our neural network predicts the atmospheric parameters Teff and log(g) as well as the chemical abundances [Mg/Fe], [Al/Fe], and [Fe/H] for 30115 stellar spectra. The scatter of predictions from eight slightly different network models shows a high internal precision of the network results: 24 K for Teff, 0.03 for log(g), 0.02 dex for [Mg/Fe], 0.03 dex for [Al/Fe], and 0.02 dex for [Fe/H]. The network gradients reveal that the network is inferring the labels in a physically meaningful way from spectral features. Validation with benchmark stars and several scientific applications confirm that our network predictions are accurate for individual stars and recover the properties of different stellar populations in the Milky Way galaxy. Such a study provides very good insights into the application of machine-learning for the spectral analysis of large-scale spectroscopic surveys, such as WEAVE and 4MIDABLE-LR and -HR (4MOST Milky Way disk and bulge low- and high-resolution). The community will have to put a substantial effort into building proactive training sets for machine-learning methods to minimize the possible systematics.

astro-ph.GA↗