Related papers: Machine Learning Refinements to Metallicity-Depend…
Traditional metrics like accuracy, F1-score, and precision are frequently used to evaluate machine learning models, however they may not be sufficient for evaluating performance on tiny, unbalanced, or high-dimensional datasets. A…
This paper presents the first results from a model for chemical evolution that can be applied to N-body cosmological simulations and quantitatively compared to measured stellar abundances from large astronomical surveys. This model…
We calculate and discuss the chemical evolution of the isotopic silicon abundances in the interstellar medium at distances and times appropriate to the birth of the solar system. This has several objectives, some of which are related to…
We develop a simple analytical model that tracks galactic metallicities governed by star formation and feedback to gain insight from the observed galaxy stellar mass-metallicity relations over a large range of stellar masses and redshifts.…
We carry out theoretical analysis, Monte Carlo simulations and Machine Learning analysis to quantify microscopic rearrangements of dilute dispersions of spherical colloidal particles from coherent scattering intensity. Both monodisperse and…
To fully harvest the rich library of stellar elemental abundance data available, we require reliable models that facilitate our interpretation of them. Galactic chemical evolution (GCE) models are one such set, and a key part of which are…
We applied machine learning to the entire data history of ESO's High Accuracy Radial Velocity Planet Searcher (HARPS) instrument. Our primary goal was to recover the physical properties of the observed objects, with a secondary emphasis on…
Metallicity plays a crucial role in the evolution of massive stars and their final core-collapse supernova (CCSN) explosions. Integral-field-unit (IFU) spectroscopy can provide a spatially resolved view of SN host galaxies and serve as a…
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…
The Chinese Space Station Survey Telescope (CSST) aims to map the universe across an unprecedented dynamic range of stellar densities, spanning from extragalactic voids to the crowded Galactic center (e.g. a few stars and galaxies in the…
Isotonic regression is a nonparametric approach for fitting monotonic models to data that has been widely studied from both theoretical and practical perspectives. However, this approach encounters computational and statistical overfitting…
Identifying the relevant coarse-grained degrees of freedom in a complex physical system is a key stage in developing powerful effective theories in and out of equilibrium. The celebrated renormalization group provides a framework for this…
The first version of the machine learning greybox model i-Melt was trained to predict latent and observed properties of K$_2$O-Na$_2$O-Al$_2$O$_3$-SiO$_2$ melts and glasses. Here, we extend the model compositional range, which now allows…
Machine learning has been effective at detecting patterns and predicting the response of systems that behave free of natural laws. Examples include learning crowd dynamics, recommender systems and autonomous mobility. There also have been…
The phenomenon of multiple populations in globular clusters is still far from understood, with several proposed mechanisms to explain the observed behaviour. The study of elemental and isotopic abundance patterns are crucial for…
We extend our model of chemical evolution that successfully account for the main observables in the solar neighborhood (Alibes, Labay & Canal 2001) to the whole Milky Way halo and disk. We assume an inside-out scenario for the assembling of…
In the context of intelligent manufacturing, this paper conducts a series of experimental studies on the predictive maintenance of industrial milling machine equipment based on the AI4I 2020 dataset. This paper proposes a complete…
In this chapter, we discuss recent advances and new opportunities through methods of machine learning for the field of classical density functional theory, dealing with the equilibrium properties of thermal nano- and micro-particle systems…
Constraining stellar models using asteroseismic and spectroscopic observations is a powerful method for precisely determining the fundamental properties of stars in different kinematic components of our galaxy. We use spectroscopy and…
With a chemically consistent evolutionary synthesis approach we follow the enrichment of individual chemical elements in the ISM. We describe the time With a chemically consistent evolutionary synthesis approach we follow the enrichment of…