Related papers: NutMaat: A Python package for stellar spectral cla…
Spectropolarimetry, the observation of polarization and intensity as a function of wavelength, is a powerful tool in stellar astrophysics. It is particularly useful for characterizing stars and circumstellar material, and for tracing the…
The increasing availability of high-quality optical and near-infrared spectroscopic data, as well as advances in modelling techniques, have greatly expanded the scientific potential of spectroscopic studies. However, the software tools…
Spectral classification is the division of stars into classes based on their spectral characteristics. Different classification systems have existed since the 19th century but the term is used nowadays mostly to refer to the Morgan-Keenan…
The characterization of exoplanet requires reliable determination of the fundamental parameters of their host stars. Spectral fitting plays an important role in this process. For the majority of stellar parameters matching synthetic spectra…
The program package SME (Spectroscopy Made Easy), designed to perform an analysis of stellar spectra using spectral fitting techniques, was updated due to adding new functions (isotopic and hyperfine splittins) in VALD and including grids…
(Abridged) This paper explores the use of k-means clustering as a tool for automated unsupervised classification of massive stellar spectral catalogs. The classification criteria are defined by the data and the algorithm, with no prior…
Stellar abundance analysis relies on flexible, high-performance spectral synthesis. To meet these needs, we present PySME v1.0, an updated Python implementation of Spectroscopy Made Easy (SME) designed for precise and survey-scale modelling…
Motivation: NMR spectra are widely used in metabolomics to obtain metabolite profiles in complex biological mixtures. Common methods used to assign and estimate concentrations of metabolites involve either an expert manual peak fitting or…
We present MASSCLEAN, a new, sophisticated and robust stellar cluster image and photometry simulation package. This visualization tool is able to create color-magnitude diagrams and standard FITS images in any of the traditional optical and…
Automated techniques have been developed to automate the process of classification of objects or their analysis. The large datasets provided by upcoming spectroscopic surveys with dedicated telescopes urges scientists to use these automated…
Spectroscopy is a central pillar of materials characterization, providing useful information on properties like structure, composition, or excited state dynamics of a system. However, many spectroscopic techniques present challenges in…
In modern-day astronomy, near-infrared, optical, and ultraviolet spectroscopy are indispensable for studying a wide range of phenomena, from measuring black hole masses to analyzing chemical abundances in stellar atmospheres. However,…
Small and wide angle x-ray scattering tensor tomography are powerful methods for studying anisotropic nanostructures in a volume-resolved manner, and are becoming increasingly available to users of synchrotron facilities. The analysis of…
This paper explores the application of Probabilistic Neural Network (PNN), Support Vector Machine (SVM) and Kmeans clustering as tools for automated classification of massive stellar spectra.
The deep learning language of choice these days is Python; measured by factors such as available libraries and technical support, it is hard to beat. At the same time, software written in lower-level programming languages like C++ retain…
We present NuMagSANS, a GPU-accelerated software package for calculating nuclear and magnetic small-angle neutron scattering (SANS) cross sections and correlation functions. The program allows users to import position-dependent nuclear…
The plethora of spectra of OB-type stars in observatory archives and the much larger numbers to come from the WEAVE and 4MOST spectroscopic facilities require efficient, but also accurate and precise methods for (semi)automatic quantitative…
Stellar spectroscopic classification has been successfully automated by a number of groups. Automated classification and parameterization work best when applied to a homogeneous data set, and thus these techniques primarily have been…
Computational tools for normal mode analysis, which are widely used in physics and materials science problems, are designed here in a single package called NMscatt (Normal Modes & scattering) that allows arbitrarily large systems to be…
Understanding astrophysical and cosmological processes can be challenging due to their complexity and lack of intuitive analogies. To address this, we present \texttt{AstronomyCalc}, a Python package specifically designed to aid…