arXiv subjects

M. Ambrosch

Publications and source records attributed to M. Ambrosch.

10 recordsLinked to original sources

arXiv2026

The non-LTE abundances of magnesium and yttrium and asteroseismic ages for the chemical clock calibration

Building on our previous study, which demonstrated the importance of accounting for departures from local thermodynamic equilibrium (LTE) in elemental-abundance determinations and of using asteroseismic ages, we investigate spatial variations in the empirical [Y/Mg]-age relation across the Galactic disc using a substantially larger stellar sample. We analysed high-resolution stellar spectra and determined Mg and Y abundances through spectral synthesis of multiple spectral features, rigorously accounting for non-LTE (NLTE) effects. We derived asteroseismic ages for stars exhibiting solar-type oscillations and used cross-checked isochrone-based methods for the remaining stars. We determined atmospheric parameters and Mg and Y abundances for 528 Galactic field stars, together with asteroseismic ages for 307 stars and isochronal ages for 221 stars. We also identified two new triple-lined and nine double-lined spectroscopic systems. Combining the present sample with that of our previous study yielded a total of 736 stars, which we used to examine the [Y/Mg]-age relation across the Galactic disc. The relation shows systematic spatial variations that likely reflect differences in star-formation and chemical-enrichment histories. In general, [Y/Mg] tends to increase with metallicity over the investigated age range. At supersolar metallicity, however, this trend may weaken, and the [Y/Mg]-age relations become flatter than those of solar-metallicity stars, which show lower [Y/Mg] values at young ages and higher values at old ages.

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arXiv2026

Detection of hot subdwarf binaries and sdB stars using machine learning methods and a large sample of Gaia XP spectra

Hot subdwarfs (hot sds) are compact, evolved stars near the Extreme Horizontal Branch (EHB) and are key to understanding stellar evolution and the ultraviolet excess in galaxies. We extend our previous analysis of Gaia XP spectra of hot subdwarf stars to a much larger sample, enabling a comprehensive study of their physical and binary properties. Our goal is to identify patterns in Gaia XP spectra, investigate binarity, and assess the influence of parameters such as temperature, helium abundance, and variability. We analyse approximately 20000 hot subdwarf candidates selected from the literature, combining Gaia XP data with published parameters. We apply Uniform Manifold Approximation and Projection (UMAP) to the XP coefficients, which represent the Gaia XP spectra in a compact, feature-based form, to construct a similarity map. We then use self-organizing maps (SOMs) and convolutional neural networks (CNNs) to classify spectra as binaries or singles, and as cool and helium-poor or hot and helium-rich. The spectra are normalised using asymmetric least squares baseline fitting to emphasise individual spectral features. The BP-RP colour dominates the similarity map, with additional influence from temperature, helium abundance, and variability. Most binaries, identified via the Virtual Observatory SED Analyser (VOSA), cluster in two filaments linked to main sequence companions. CNN classification suggests a strong correlation between variability and binarity, with binary fractions exceeding 60 percent for active hot subdwarfs. Gaia XP spectra combined with dimensionality reduction and machine learning effectively reveal patterns in hot subdwarf properties. Our findings indicate that binarity and environmental density strongly shape the evolutionary paths of hot subdwarfs, and we identify possible contamination by main sequence and cataclysmic variable stars in the base sample.

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arXiv2025

Chromospherically active stars: Lithium and CNO abundances in northern RS CVn stars

Aims: We carried out a detailed investigation of Lithium and CNO abundances, including carbon isotope ratios, in RS CVn stars to assess the role of magnetic activity in the mixing of stellar atmospheres. Methods: We obtained high-resolution spectra at the Moletai Astronomical Observatory. Lithium abundances were determined by spectral synthesis of the 6707 A line and the CNO abundances using the C2 band heads at 5135 and 5635.5 A CN bands at 6470- 6490 A and 7980 to 8005 A, and the [O I] line at 6300 A. By fitting the 13CN band at 8004.7 A, we determined the carbon isotope ratios.Results. We determined the main atmospheric parameters and investigated the chemical composition of 32 RS CVn stars. Lithium abundances were determined for 13 additional stars using archival spectra. We report that *iot Gem and HD 179094 have carbon isotope ratios already affected by extra-mixing, even though they are in the evolutionary stage below the red giant branch luminosity bump. About half of the low-mass giants, for which the lithium abundance was determined, follow the first dredge-up predictions; however, other stars show reduced Lithium abundances, as predicted by thermohaline-induced mixing. The intermediate-mass stars show reduced Lithium abundances reduced, as predicted by rotation-induced mixing. Conclusions. In low-mass, chromospherically active RS CVn stars, extra-mixing of lithium and carbon isotopes may begin earlier than in normal giants. The Li-rich RS CVn giant V*OP And has large C/N and carbon isotope ratios and raises questions about the origin of its lithium enhancement.

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arXiv2025

Carbon and nitrogen as indicators of stellar evolution and age. A homogeneous sample of 44 open clusters from the Gaia-ESO Survey

Context. Low- and intermediate-mass giants undergo a complex chemical evolution that has yet to be observationally probed. The influence of core helium flash on the chemical composition of stellar atmospheres has been an open question since its theoretical prediction 60 years ago. Aims. Based on high-resolution spectral observations of 44 open star clusters in the Gaia-ESO survey, our aim is to perform the first large-scale homogeneous investigation into the carbon and nitrogen photospheric content of low- and intermediate-mass giant stars in different phases of evolution. Methods. We determined carbon and nitrogen abundances using spectral synthesis of the C2 Swan (1,0) band head at 5135 {\AA} and C2 Swan (0,1) band head at 5635.5 {\AA}, 12C14N bands in the interval 6470 - 6490 {\AA}, and the forbidden [O i] line at 6300.31 {\AA}. Results. We revealed differences in C/N abundance ratios between pre- and post-core-He-flash stars. The lower C/N ratios in core He-burning red clump stars are mainly due to the enhancement of nitrogen abundances. We presented calibrations of the relationship between [C/N] and stellar age for solar metallicity low- and intermediate-mass giants taking into account different evolutionary stages. Conclusions. The C/N abundance ratios in the investigated first-ascent giant stars are slightly less affected by the first dredge-up than predicted by the theoretical models. The rotation-induced extra mixing is not as efficient as theoretically predicted. The core helium flash may trigger additional alterations in carbon and nitrogen abundances that are not yet theoretically modelled. We found that the evolutionary stage of stars must be taken into account when using [C/N] as an age indicator.

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arXiv2025

Chromospherically active stars: Chemical composition of photospheres in 20 RS CVn stars

Context. Various element transport processes modify the photospheric chemical composition of low-mass stars during their evolution. The most prominent one is the first dredge-up that occurs at the beginning of the red giant branch. Then, various extra-mixing processes, such as those caused by thermohaline- and/or rotation-induced mixing, come into action. The extent of the influence of stellar magnetic activity on alterations in stellar chemical composition is among the least studied questions. Aims. To investigate how magnetic activity influences mixing in the atmospheres of magnetically active stars, we carried out a detailed study of C, N, and up to ten other chemical element abundances, as well as carbon isotope ratios in a sample of RS CVn stars. Methods. Using a differential model atmosphere method, we analysed high-resolution spectra that had been observed with the VUES spectrograph on the 1.65 m telescope at the Moletai Astronomical Observatory of Vilnius University. Abundances of other chemical elements were determined from equivalent widths or spectral syntheses of unblended spectral lines. Results. We determined the main atmospheric parameters and abundances of up to 12 chemical elements for a sample of 20 RS CVn giants that represented different evolutionary stages. We determined that *29 Dra, *b01 Cyg, and V* V834 Her, which are in the evolutionary stage below the red giant branch luminosity bump, already show evidence of extra-mixing in their lowered carbon isotope ratios. Conclusions. We provide observational evidence that in low-mass chromospherically active RS CVn stars, due to their magnetic activity, extra-mixing processes may start acting below the luminosity bump of the red giant branch.

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arXiv2024

Advanced Classification of Hot Subdwarf Binaries Using Artificial Intelligence Techniques and Gaia DR3 data

Hot subdwarfs are compact blue evolved objects, burning helium in their cores surrounded by a tiny hydrogen envelope. Most models agree on a common envelope binary evolution scenario in the Red Giant phase. However, the binarity rate for these objects is yet unsolved. We aim to develop a novel classification method for identifying hot subdwarf binaries within large datasets using Artificial Intelligence methods and Gaia DR3 data. The results will be compared with those obtained previously using VOSA (Virtual Observatory Sed Analyzer) on coincident samples. The methods include several machine learning techniques. We used Support Vector Machines (SVM) to classify 3084 hot subdwarf stars based on their color-magnitude properties. Of these, 2815 objects have Gaia Data Release 3 BP/RP spectra, which were classified using Self-Organizing Maps (SOM) and Convolutional Neural Networks (CNN). The findings demonstrate a high agreement level (70-90%) with VOSA's classification, indicating that machine learning methods effectively classify sources with an accuracy comparable to human inspection or non-AI techniques. SVM in a radial basis function achieves 70.97% reproducibility for binary targets using photometry. CNN reaches 84.94% for binary detection using spectroscopy. We also found that the single-binary differences are especially observable on the infrared flux in our GDR3 BP/BR spectra, at wavelengths larger than 700 nm. We found that all our methods are effective in discerning between single and binary systems and are consistent with the results previously obtained with VOSA. In global terms, considering all quality metrics, CNN is the method that provides the best accuracy. The methods are also effective for detecting peculiarities in the spectra. Further research is needed to refine our techniques and enhance automated classification reliability, especially for large-scale surveys.

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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.

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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.

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arXiv2022

Chemical Composition Of Bright Stars In The Northern Hemisphere: Star-Planet Connection

In fulfilling the aims of the planetary and asteroseismic research missions, such as that of the NASA Transiting Exoplanet Survey Satellite (TESS) space telescope, accurate stellar atmospheric parameters and a detailed chemical composition are required as input. We have observed high-resolution spectra for all 848 bright (V<8 mag) stars that are cooler than F5 spectral class in the area up to 12 deg surrounding the northern TESS continuous viewing zone and uniformly determined the main atmospheric parameters, ages, orbital parameters, velocity components, and precise abundances of up to 24 chemical species (C(C2), N(CN), [O I], Na I, Mg I, Al I, Si I, Si I, Ca I, Ca II, Sc I, Sc II, Ti I, Ti II, V I, Cr I, Cr II, Mn I, Fe I, Fe II, Co I, Ni I, Cu I, and Zn I) for 740 slowly rotating stars. The analysis of 25 planet-hosting stars in our sample drove us to the following conclusions: the dwarf stars hosting high-mass planets are more metal rich than those with low-mass planets. We find slightly negative C/O and Mg/Si slopes toward the stars with high-mass planets. All the low-mass planet hosts in our sample show positive $Δ$[El/Fe] versus condensation temperature slopes, in particular, the star with the large number of various planets. The high-mass planet hosts have a diversity of slopes, but in more metal rich, older, and cooler stars, the positive elemental abundance slopes are more common.

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arXiv2021

Gaia-ESO Survey: Detailed elemental abundances in red giants of the peculiar globular cluster NGC 1851

Context. NGC 1851 is one of several globular clusters for which multiple stellar populations of the subgiant branch have been clearly identified and a difference in metallicity detected. A crucial piece of information on the formation history of this cluster can be provided by the sum of A(C+N+O) abundances. However, these values have lacked a general consensus thus far. The separation of the subgiant branch can be based on age and/or A(C+N+O) abundance differences. Aims. Our main aim was to determine carbon, nitrogen, and oxygen abundances for evolved giants in the globular cluster NGC1851 in order to check whether or not the double populations of stars are coeval. Methods. High-resolution spectra, observed with the FLAMES-UVES spectrograph on the ESO VLT telescope, were analysed using a differential model atmosphere method. Results. We provide abundances of up to 29 chemical elements for a sample of 45 giants in NGC 1851. The investigated stars can be separated into two populations with a difference of 0.07 dex in the mean metallicity, 0.3 dex in the mean C/N, and 0.35 dex in the mean s-process dominated element-to-iron abundance ratios [s/Fe]. No significant difference was determined in the mean values of A(C+N+O) as well as in abundance to iron ratios of carbon, alpha- and iron-peak-elements, and of europium. Conclusions. As the averaged A(C+N+O) values between the two populations do not differ, additional evidence is given that NGC 1851 is composed of two clusters, the metal-rich cluster being by about 0.6 Gyr older than the metal-poor one. A global overview of NGC 1851 properties and the detailed abundances of chemical elements favour its formation in a dwarf spheroidal galaxy that was accreted by the Milky Way.

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