Related papers: proEQUIB: IDL Library for Plasma Diagnostics and A…
The stellar population models dramatically progressed with the arrival of large and complete libraries, ELODIE, CFLIB (=Indo-US) and MILES at a relatively high resolution. We show that the quality of the fits is not anymore limited by the…
The unbounded permutations of biological molecules, including proteins and their constituent peptides, presents a dilemma in identifying the components of complex biosamples. Sequence search algorithms used to identify peptide spectra can…
We present the Empirical Dust Attenuation (EDA) framework -- a flexible prescription for assigning realistic dust attenuation to simulated galaxies based on their physical properties. We use the EDA to forward model synthetic observations…
In this work we report spectrophotometric observations of a planetary nebula sample towards the galactic bulge. A total of 45 PNe was observed and their physical parameters (electron density and temperature) were derived. Ionic abundances…
Accurate molecular identification of metabolites can unlock new areas of the molecular universe and allow greater insight into complex biological and environmental systems than currently possible. Analytical approaches for measuring the…
IFISS is an established MATLAB finite element software package for studying strategies for solving partial differential equations (PDEs). IFISS3D is a new add-on toolbox that extends IFISS capabilities for elliptic PDEs from two to three…
We present two software packages developed to support detector R&D studies for the International Linear Collider. LCDG4 is a full-detector simulator that provides energy deposits from particles traversing the sensitive volumes of the…
In this thesis, wide-field 2D spectroscopy is employed in order to characterise the nebular properties of late-type field galaxies. The observations performed for this dissertation represent the first endeavour to obtain full 2D coverage of…
ThermoLIB is Python/Cython library designed to be used as a post-processing tool for constructing free energy surfaces from the output of molecular simulations, transforming them between different collective variables (CVs) and extracting…
In addition to study extragalactic stellar populations in their integrated light, the detailed analysis of individual resolved objects has become feasible, mainly for luminous giant stars and for extragalactic planetary nebulae (XPNe) in…
A wide variety of feed gases are used to generate low-temperature plasmas for the microelectronics and the sustainability applications. These plasmas often have a complex combination of reactive and non-reactive species which may have…
Graph deep learning models, which incorporate a natural inductive bias for a collection of atoms, are of immense interest in materials science and chemistry. Here, we introduce the Materials Graph Library (MatGL), an open-source graph deep…
The elemental abundances of ten planetary nebulae, derived with high accuracy including ISO and IUE spectra, are analysed with the aid of synthetic evolutionary models for the TP-AGB phase. Model prescriptions are varied until we achieve…
The recent availability of wideband receivers and high sensitivity instruments in the mm and submm wavelengths has opened the possibility of studying in detail the chemistry of the interstellar medium in extragalactic objects. Within the…
The ExoMol database (www.exomol.com) provides molecular data for spectroscopic studies of hot atmospheres. While the data is intended for studies of exoplanets and other astronomical bodies, the dataset is widely applicable. The basic form…
The first observations of deuterium and oxygen in the Local Interstellar Medium (LISM) obtained with the Far Ultraviolet Spectroscopic Explorer (FUSE) can be used to search for local abundance variations. While the very limited sample of…
We study how diffuse interstellar bands (DIBs) measured toward distance-distributed target stars can be used to locate dense interstellar (IS) clouds in the Galaxy and probe a line-of-sight (LOS) kinematical structure, a potential useful…
Machine Learning Interatomic Potentials (MLIP) are a novel in silico approach for molecular property prediction, creating an alternative to disrupt the accuracy/speed trade-off of empirical force fields and density functional theory (DFT).…
While it is well recognized that both the Galactic interstellar extinction curves and the gas-phase abundances of dust-forming elements exhibit considerable variations from one sight line to another, as yet most of the dust extinction…
As the application of large language models in various fields continues to expand, materials science also ushers in opportunities for AI-driven innovation. The traditional way of relying on manual search for materials science-related…