Related papers: Machine Learning Refinements to Metallicity-Depend…
The research focuses on determining the metallicity ([Fe/H]) predicted in the solar twin stars by using various regression modeling techniques which are, Random Forest, Linear Regression, Decision Tree, Support Vector, and Gradient…
The Milky Way's metal-poor stars are nearby ancient objects that are used to study early chemical evolution and the assembly and structure of the Milky Way. Here we present reliable metallicities of $\sim280,000$ stars with $-3.75 \lesssim$…
Machine learning (ML) has become a standard tool for the exploration of chemical space. Much of the performance of such models depends on the chosen database for a given task. Here, this aspect is investigated for "chemical tasks" including…
In this paper, we investigate the energy minimization model of the ensemble Kohn-Sham density functional theory for metallic systems, in which a pseudo-eigenvalue matrix and a general smearing approach are involved. We study the invariance…
Recent work has used spectra of about 5000 stars in NGC 6397 that were extracted from a MUSE mosaic to determine the atmospheric parameters for these stars by fitting the spectra against the Goettingen Spectral Library. A significant change…
We present Galactic mean metallicity maps derived from the first year of the SDSS-III APOGEE experiment. Mean abundances in different zones of Galactocentric radius (0 < R < 15 kpc) at a range of heights above the plane (0 < |z| < 3 kpc),…
The characterization of interstellar chemical inventories provides valuable insight into the chemical and physical processes in astrophysical sources. The discovery of new interstellar molecules becomes increasingly difficult as the number…
Machine Learning (ML) techniques are revolutionizing the way to perform efficient materials modeling. Nevertheless, not all the ML approaches allow for the understanding of microscopic mechanisms at play in different phenomena. To address…
Some studies of stars' multi-element abundance distributions suggest at least 5-7 significant dimensions, but others show that many elemental abundances can be predicted to high accuracy from [Fe/H] and [Mg/Fe] (or [Fe/H] and age) alone. We…
Interpreting the scaling relations followed by galaxies is a fundamental tool for assessing how well we understand galaxy formation and evolution. Several scaling relations involving the galaxy metallicity have been discovered through the…
I present a review of chemical evolution models of the solar neighborhood. I give special attention to the necessary ingredients to reproduce the observed [Xi/Fe] ratios in nearby metal and super metal rich stars, and to the chemical…
Constraining parameters such as the initial mass function high-mass slope and the frequency of type Ia supernovae is of critical importance in the ongoing quest to understand galactic physics and create realistic hydrodynamical simulations.…
As a tool to interpret nearby and high redshift galaxy data from optical to K-band we present our chemically consistent spectrophotometric evolutionary synthesis models. These models take into account the increasing initial metallicity of…
The abundance patterns of $r$-process-enhanced stars contain key information required to constrain the astrophysical site(s) of $r$-process nucleosynthesis, and to deepen our understanding of the chemical evolution of our Galaxy. In order…
Machine learning models are routinely integrated into process mining pipelines to carry out tasks like data transformation, noise reduction, anomaly detection, classification, and prediction. Often, the design of such models is based on…
The study of stellar parameters of planet-hosting stars, such as metallicity and chemical abundances, help us to understand the theory of planet formation and stellar evolution. Here, we present a catalogue of accurate stellar atmospheric…
We introduce three stochastic cooperative models for particle deposition and evaporation relevant to ionic self-assembly of nanoparticles with applications in surface fabrication and nanomedicine. We present a method for mapping a…
This paper presents machine learning experiments performed over results of galaxy classification into elliptical (E) and spiral (S) with morphological parameters: concetration (CN), assimetry metrics (A3), smoothness metrics (S3), entropy…
We present results of model atmosphere/line formation calculations which quantitatively test how the 21 classical and four higher-order Balmer-line Lick/IDS indices (Worthey et al. 1994; Worthey & Ottaviani 1997) depend on individual…
Superlevel partitioning is combined with a simple relaxation procedure to construct an iterative technique for solving equations of statistical equilibrium. In treating an $N$-level model atom, the technique avoids the $N^{3}$ scaling in…