Related papers: Machine Learning Refinements to Metallicity-Depend…
All stellar evolution models for nucleosynthesis require an initial isotopic abundance set to use as a starting point. Generally, our knowledge of isotopic abundances of stars is fairly incomplete except for the Solar System. We present a…
A growing number of indicators are now being used with some confidence to measure the metallicity(Z) of photoionisation regions in planetary nebulae, galactic HII regions(GHIIRs), extra-galactic HII regions(EGHIIRs) and HII galaxies(HIIGs).…
Astrochemical modelling of the interstellar medium typically makes use of complex computational codes with parameters whose values can be varied. It is not always clear what the exact nature of the relationship is between these input…
Stellar evolution models require initial isotopic abundance sets as input, but these abundances are incomplete outside the solar neighborhood, are challenging to infer from elemental observations, and are galaxy specific. Compositions…
We present a machine learning method to assign stellar parameters (temperature, surface gravity, metallicity) to the photometric data of large photometric surveys such as SDSS and SKYMAPPER. The method makes use of our previous effort in…
$\mathrm{\gamma}$-ray spectroscopy is a quantitative, non-destructive technique that may be utilized for the identification and quantitative isotopic estimation of radionuclides. Traditional methods of isotopic determination have various…
The ``2-process Model'' is a promising technique for interpreting stellar chemical abundance data from large-scale surveys (e.g., SDSS-IV/V, GALAH), enabling more quantitative empirical studies of differences in chemical enrichment history…
Investigating the main determinants of the mechanical performance of metals is not a simple task. Already known physical inspired qualitative relations between 2D microstructure characteristics and 3D mechanical properties can act as the…
We used a one-zone chemical evolution model to address the question of how many masses and metallicities are required in grids of massive stellar models in order to ensure reliable galactic chemical evolution predictions. We used a set of…
In modern astrophysics, the machine learning has increasingly gained more popularity with its incredibly powerful ability to make predictions or calculated suggestions for large amounts of data. We describe an application of the supervised…
Metallicity is one of four free parameters typically considered when fitting isochrones to the cluster sequence. Unfortunately, this parameter is often ignored or assumed to be solar in most papers. Hence an unknown bias is introduced in…
Observations of elemental abundances in the Galaxy have repeatedly shown an intrinsic scatter as a function of time and metallicity. The standard approach to chemical evolution does not attempt to address this scatter in abundances since…
We present a comprehensive study of the abundance evolution of the elements from H to U in the Milky Way halo and local disk. We use a consistent chemical evolution model, metallicity dependent isotopic yields from low and intermediate mass…
Machine-learning of atomic-scale properties amounts to extracting correlations between structure, composition and the quantity that one wants to predict. Representing the input structure in a way that best reflects such correlations makes…
We develop a new method to account for the finite lifetimes of stars and trace individual abundances within a semi-analytic model of galaxy formation. At variance with previous methods, based on the storage of the (binned) past star…
We present a study of the stellar parameters and iron abundances of 18 giant stars in 6 open clusters. The analysis was based on high-resolution and high-S/N spectra obtained with the UVES spectrograph (VLT-UT2). The results complement our…
Materials properties depend strongly on chemical composition, i.e., the relative amounts of each chemical element. Changes in composition lead to entirely different chemical arrangements, which vary in complexity from perfectly ordered…
Despite the fundamental progress in autonomous molecular and materials discovery, data scarcity throughout chemical compound space still severely hampers the use of modern ready-made machine learning models as they rely heavily on the…
The legacy Hitomi telescope has delivered the precise measurements of the chemical abundances in the Perseus Cluster, covering the Si-group (Si, S, Ar, Ca) and Fe-group elements (Cr, Mn, Ni). In Paper I (Leung et al., ApJ 2025), we examined…
(abridged) Elemental abundances of FGK stars can be derived routinely from high-resolution optical spectra, but this remains considerably more difficult for cooler stars. Machine-learning methods offer a practical route to infer otherwise…