Related papers: Machine Learning Refinements to Metallicity-Depend…
The Galaxy is in continuous elemental evolution. Since new elements produced by dying stars are delivered to the interstellar medium, the formation of new enerations of stars and planetary systems is influenced by this metal enrichment. We…
Global climate models represent small-scale processes such as clouds and convection using quasi-empirical models known as parameterizations, and these parameterizations are a leading cause of uncertainty in climate projections. A promising…
Materials synthesis optimization is constrained by serial feedback processes that rely on manual tools and intuition across multiple siloed modes of characterization. We automate and generalize feature extraction of reflection high-energy…
We estimate the empirical R parameter in 26 Galactic Globular Clusters covering a wide metallicity range, imaged by WFPC2 on board the HST. The improved spatial resolution permits a large fraction of the evolved stars to be measured and…
Machine learning interatomic potentials (MLIPs) are routinely used to model diverse atomistic phenomena, yet parameterizing them to accurately capture solid-state phase transformations remains difficult. We present error metrics and…
We present a meteorite-calibrated Bayesian framework for searching archival abundance records for chemical technosignatures--operationally, compositional patterns better explained by an idealised "processed" template (endmember) than by the…
Aims. This paper introduces LRPayne, a novel algorithm designed for the efficient determination of stellar parameters and chemical abundances from low-resolution optical spectra, with a primary focus on data from large-scale galactic…
Machine-learning models are increasingly used to predict properties of atoms in chemical systems. There have been major advances in developing descriptors and regression frameworks for this task, typically starting from (relatively) small…
The elemental abundances of symbiotic giants are essential to address the role of chemical composition in the evolution of symbiotic binaries, to map their parent population, and to trace their mass transfer history. However, the number of…
We use a simple one-zone galactic chemical evolution model to quantify the uncertainties generated by the input parameters in numerical predictions, for a galaxy with properties similar to those of the Milky Way. We compiled several studies…
Analysis of bulk meteorite compositions has revealed small isotopic variations due to the presence of material (e.g., stardust) that preserved the signature of nuclear reactions occurring in specific stellar sites. The interpretation of…
We present a new basis for scaling abundances with total metallicity in nebular photoionisation models, based on extensive Milky Way stellar abundance data, to replace the uniform scaling normally used in the analysis of HII regions. Our…
In simulations of multiscale dynamical systems, not all relevant processes can be resolved explicitly. Taking the effect of the unresolved processes into account is important, which introduces the need for paramerizations. We present a…
We present the results of applying automated machine learning techniques to the problem of matching different object catalogues in astrophysics. In this study we take two partially matched catalogues where one of the two catalogues has a…
Laser machining is a highly flexible non-contact manufacturing technique that has been employed widely across academia and industry. Due to nonlinear interactions between light and matter, simulation methods are extremely crucial, as they…
We present Li abundances for 73 stars in the metallicity range -3.5 < [Fe/H] < -1.0 using improved IRFM temperatures (Casagrande et al. 2010) with precise E(B-V) values obtained mostly from interstellar NaI D lines, and high-quality…
Machine-learning potentials (MLPs) for atomistic simulations are a promising alternative to conventional classical potentials. Current approaches rely on descriptors of the local atomic environment with dimensions that increase…
In computational material design, ionic radius is one of the most important physical parameters used to predict material properties. Motivated by the progress in computational materials science and material informatics, we extend the…
Planetary Nebulae (PNe) abundance patterns have long been used to note signatures of nuclear processing and to trace the distribution of metals throughout galaxies. We present abundance gradients and heavy element ratios based upon newly…
Recent advances in the use of High-Resolution Cross-Correlation Spectroscopy (HRCCS) to detect molecular species in exoplanet atmospheres, presents a new challenge for the accuracy of reference spectroscopic line lists. While parent…