Related papers: PySSED: an automated method of collating and fitti…
One of the few ways that we can understand the environment around dusty stars and how much material they contribute back to the Universe, is by fitting their brightness at different wavelengths with models that account for how the energy…
Asteroseismology is well-established in astronomy as the gold standard for determining precise and accurate fundamental stellar properties like masses, radii, and ages. Several tools have been developed for asteroseismic analyses but many…
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…
Analyses of stellar spectra often begin with the determination of a number of parameters that define a model atmosphere. This work presents a prototype for an automated spectral classification system that uses a 15 nm-wide region around…
Spectral energy distributions for models of arbitrarily rotating stars are computed using two dimensional rotating stellar models, NLTE plane parallel model atmospheres, and a code to integrate the appropriately weighted intensities over…
We introduce the STAR-MELT Python package that we developed to facilitate the analysis of time-resolved emission line spectroscopy of young stellar objects. STAR-MELT automatically extracts, identifies and fits emission lines. We summarise…
We present a method to analyze the spectral energy distributions (SEDs) of young stellar objects (YSOs). Our approach is to fit data with pre-computed 2-D radiation transfer models spanning a large region of parameter space. This allows us…
In this paper, I present a new set of synthetic spectral energy distributions (SEDs) for young stellar objects (YSOs) spanning a wide range of evolutionary stages, from the youngest deeply embedded protostars to pre-main-sequence stars with…
Accurately measuring stellar parameters is a key goal to increase our understanding of the observable universe. However, current methods are limited by many factors, in particular, the biases and physical assumptions that are the basis for…
We present a newly developed version of BayeSED, a general Bayesian approach to the spectral energy distribution (SED) fitting of galaxies. The new BayeSED code has been systematically tested on a mock sample of galaxies. The comparison…
In the era of large time-domain spectro-photometric surveys, surface variations such as starspots, chemical inhomogeneities, pulsations, rotational distortions, and binary interactions can now be directly detected and modelled. Accurately…
Fitting the spectral energy distributions (SEDs) of galaxies is an almost universally used technique that has matured significantly in the last decade. Model predictions and fitting procedures have improved significantly over this time,…
We present piXedfit, pixelized spectral energy distribution (SED) fitting, a Python package that provides tools for analyzing spatially resolved properties of galaxies using multiband imaging data alone or in combination with integral field…
We developed a Python based framework for astronomical image processing and analysis. Astronomical image loading, normalizing, stacking, and filtering processes represent visible range images from grayscale. Besides, the blending process…
Stellar spectra contain a large amount of information about the conditions in stellar atmospheres. However, extracting this information is challenging and demands comprehensive numerical modelling. Here, we present stellar spectra…
Libraries of stellar spectra, such as ELODIE (Prugniel & Soubiran 2001), CFLIB (Valdes et al. 2004), or MILES (S\'anchez-Bl\'azquez et al. 2006), are used for a variety of applications, and especially in modelling stellar populations (e. g.…
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…
We report on two distinct computational approaches to self-consistently measure photospheric properties of large samples of stars. Both procedures consist of a set of several semi-integrated tasks based on shell and Python scripts, which…
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…
The spectral energy distributions (SEDs) of galaxies are shaped by nearly every physical property of the system, including the star formation history, metal content, abundance pattern, dust mass, grain size distribution, star-dust geometry,…