Related papers: Classifying metal-poor stars with machine learning…
The existence of neutron star mergers has been supported since the discovery of the binary pulsar and the observation of its orbital energy loss, consistent with General Relativity. They are considered nucleosynthesis sites of the rapid…
We report a new mechanism for the \textsl{s} in rotating massive metal-poor stars. Our models show that above a critical rotation speed, such stars evolve in a quasi-chemically-homogeneous fashion, which gives rise to a prolific…
We present an automated statistical method that uses medium-resolution spectroscopic observations of a set of stars to select those that show evidence of possessing significant amounts of neutron-capture elements. Our tool was tested…
Recently, there has been an increased interest in the application of machine learning (ML) techniques to a variety of problems in condensed matter physics. In this regard, of particular significance is the characterization of simple and…
We introduce a new method to determine galaxy cluster membership based solely on photometric properties. We adopt a machine learning approach to recover a cluster membership probability from galaxy photometric parameters and finally derive…
First-principles computations are the driving force behind numerous discoveries of hydride-based superconductors, mostly at high pressures, during the last decade. Machine-learning (ML) approaches can further accelerate the future…
Numerical N-body simulations are commonly used to explore stability regions around exoplanets, offering insights into the possible existence of satellites and ring systems. This study aims to utilize Machine Learning (ML) techniques to…
Fast and accurate treatment of collisions in the context of modern N-body planet formation simulations remains a challenging task due to inherently complex collision processes. We aim to tackle this problem with machine learning (ML), in…
The chemical abundances of the metal-poor stars in the stellar stream provide important information for setting constraints on models of neutron-capture processes. The study of these stars could give us a better understanding of r-process…
Electron microscopy is widely used to explore defects in crystal structures, but human detecting of defects is often time-consuming, error-prone, and unreliable, and is not scalable to large numbers of images or real-time analysis. In this…
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…
This work describes a study of elemental abundances for 30 metal-poor stars whose chemical abundances provide excellent information for setting constraints on models of neutron-capture processes. Based on the abundances of main r- and weak…
Machine learning (ML) methods are becoming integral to scientific inquiry in numerous disciplines, such as material sciences. In this manuscript, we demonstrate how ML can be used to predict several properties in solid-state chemistry, in…
This paper illustrates an application of machine learning (ML) within a complex system that performs grade estimation. In surface mining, assay measurements taken from production drilling often provide useful information that allows…
Due to the lack of experimental data on extremely neutron-rich nuclei, theoretical values derived from nuclear physics models are essential for the rapid neutron capture process ($r$-process). Metal-poor stars enriched by the $r$-process…
The mass-metallicity relation (MZR) provides crucial insights into the baryon cycle in galaxies and provides strong constraints on galaxy formation models. We use \jwst\ NIRSpec observations from the UNCOVER program to measure the gas-phase…
The discovery of exoplanets has expanded our understanding of planetary systems and opened new avenues for astronomical research. In this study, we present a machine learning (ML) framework for exoplanet identification using a time-series…
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…
The production of the neutron-capture isotopes beyond iron that we observe today in the solar system is the result of the combined contribution of the r-process, the s- process and possibly the i-process. Low-mass AGB (2 < M/Msun < 3) and…
Besides their intrinsic nuclear-structure value, nuclear mass models are essential for astrophysical applications, such as r-process nucleosynthesis and neutron-star structure. To overcome the intrinsic limitations of existing…