Related papers: Machine Learning Refinements to Metallicity-Depend…
We develop, discuss, and compare several inference techniques to constrain theory parameters in collider experiments. By harnessing the latent-space structure of particle physics processes, we extract extra information from the simulator.…
Machine learning (ML) can be used to construct surrogate models for the fast prediction of a property of interest. ML can thus be applied to chemical projects, where the usual experimentation or calculation techniques can take hours or days…
A fundamental challenge for wide-field imaging surveys is obtaining follow-up spectroscopic observations: there are > $10^9$ photometrically cataloged sources, yet modern spectroscopic surveys are limited to ~few x $10^6$ targets. As we…
We present an innovative of artificial intelligence with column chromatography, aiming to resolve inefficiencies and standardize data collection in chemical separation and purification domain. By developing an automated platform for precise…
We use machine learning algorithms to detect the crystalline phase in undercooled melts in molecular dynamics simulations. Our classification method is based on local conformation and environmental fingerprints of individual monomers. In…
The knowledge of isotopic and elemental abundances of the pristine solar system material provides a fundamental test of galactic chemical evolution models, while the composition of the solar photosphere is a reference pattern to understand…
We present a chemical analysis of selected HII regions from the PHANGS-MUSE nebular catalogue. Our intent is to empirically re-calibrate strong-line diagnostics of gas-phase metallicity, applicable across a wide range of metallicities…
Due to the wide range of timescales that are present in macromolecular systems, hierarchical multiscale strategies are necessary for their computational study. Coarse-graining (CG) allows to establish a link between different system…
Isolating the features associated with different materials growth conditions is important to facilitate the tuning of these conditions for effective materials growth and characterization. This study presents machine learning models for…
In order to build more realistic single stellar population (SSP) models with variable alpha-enhancement, we have recently determined [Mg/Fe] in a uniform scale with a precision of about 0.1 dex for 752 stars in the MILES empirical library.…
We develop a novel method based on machine learning principles to achieve optimal initiation of CPU-intensive computations for forward asteroseismic modeling in a multi-D parameter space. A deep neural network is trained on a precomputed…
We present spectra of 14 A-type supergiants in the metal-rich spiral galaxy M83. We derive stellar parameters and metallicities, and measure a spectroscopic distance modulus m-M = 28.47 +\- 0.10 (4.9 +\- 0.2 Mpc), in agreement with other…
Machine learning interatomic potentials have revolutionized complex materials design by enabling rapid exploration of material configurational spaces via crystal structure prediction with ab initio accuracy. However, critical challenges…
We consider the problem of learning the structure of ferromagnetic Ising models Markov on sparse Erdos-Renyi random graph. We propose simple local algorithms and analyze their performance in the regime of correlation decay. We prove that an…
The processes that disperse the products of massive stars from their birth sites play a fundamental role in determining the observed abundances. I discuss parameterizations for element dispersal and their roles in chemical evolution, with…
Metal-organic frameworks (MOFs) are highly interesting and tunable materials. By incorporating spatial defects into their atomic structure, MOFs can be finetuned to exhibit precise chemical functionalities, extending their applicability in…
Accurately predicting industrial aging processes makes it possible to schedule maintenance events further in advance, ensuring a cost-efficient and reliable operation of the plant. So far, these degradation processes were usually described…
To study the chemical evolution across cosmic epochs, we investigate Ne, S, Cl, and Ar abundance patterns in the COS Legacy Archive Spectroscopic SurveY (CLASSY). CLASSY comprises local star-forming galaxies (0.02 < z < 0.18) with enhanced…
The isotopic abundances of r-process elements in the solar system are traditionally derived as residuals from the subtraction of s-process contributions from total solar abundances. However, the uncertainties in s-process nucleosynthesis --…
We combine spectroscopic metallicity values with integrated narrowband continuum colors to explore the internal metallicity distribution in early-type galaxies. The different techniques for determining metallicity (indices versus colors)…