Related papers: Stellar Spectral Interpolation using Machine Learn…
One of the key ingredients in establishing the relation between input signal and output flux from a spectrometer is accurate determination of the spectrophotometric calibration. In the case of spectrometers onboard satellites, the accuracy…
For applications in population synthesis, libraries of theoretical stellar spectra are often considered an alternative to template libraries of observed spectra, because they allow a complete sampling of stellar parameters. Most attention…
Global Stellar Formation Rates or SFRs are crucial to constrain theories of galaxy formation and evolution. SFR's are usually estimated via spectroscopic observations which require too much previous telescope time and therefore cannot match…
We present new, high-to-intermediate spectral resolution stellar population models, based on four popular libraries of empirical stellar spectra, namely Pickles, ELODIE, STELIB and MILES. These new models are the same as our previous…
Effective verification and validation techniques for modern scientific machine learning workflows are challenging to devise. Statistical methods are abundant and easily deployed, but often rely on speculative assumptions about the data and…
High spectral resolution evolutionary synthesis models have become a routinely used ingredient in extragalactic work, and as such deserve thorough testing. Star clusters are ideal laboratories for such tests. This paper applies the spectral…
Uncertainties in stellar population models, both in terms of stellar evolution and stellar spectra, translate into uncertainties in our interpretation of stellar populations in galaxies, since stars are the source of most of the light we…
Spectral-spatial processing has been increasingly explored in remote sensing hyperspectral image classification. While extensive studies have focused on developing methods to improve the classification accuracy, experimental setting 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…
(Aims) We present a number of improvements to the MILES library and stellar population models. We correct some small errors in the radial velocities of the stars, measure the spectral resolution of the library and models more accurately,…
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…
Determining the properties of old stellar systems using evolutionary population synthesis requires a library of reliable model stellar fluxes. Empirical libraries are limited to spectra of stars in the solar neighborhood, with nearly solar…
Theoretical stellar libraries have been increasingly used to overcome limitations of empirical libraries, e.g. by exploring atmospheric parameter spaces not well represented in the latter. This work presents a new theoretical library which…
High resolution spectral models for simple stellar populations (SSP) developed in the past few years have become a standard ingredient in studies of stellar population of galaxies. As more such models become available, it becomes…
We extend the spectral range of our stellar population synthesis models based on the MILES and CaT empirical stellar spectral libraries. For this purpose we combine these two libraries with the Indo-U.S. to construct composite stellar…
The interiors of neutron stars reach densities and temperatures beyond the limits of terrestrial experiments, providing vital laboratories for probing nuclear physics. While the star's interior is not directly observable, its pressure and…
I describe very briefly the new libraries of empirical spectra of stars covering wide ranges of values of the atmospheric parameters Teff, log g, [Fe/H], as well as spectral type, that have become available in the recent past, among them…
Physical and chemical conditions (kinetic temperature, volume density, molecular composition) of interstellar clouds are inherent in their mm-submm line spectra, making spectral line profiles powerful diagnostics of source conditions. We…
We apply machine learning techniques in an attempt to predict and classify stellar properties from noisy and sparse time series data. We preprocessed over 94 GB of Kepler light curves from MAST to classify according to ten distinct physical…
We describe a accurate and fast pixel-based statistical method to interpolate fields of arbitrary spin on the sphere. We call this method Fast and Lean Interpolation on the Sphere (FLINTS). The method predicts the optimal interpolated…