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Spectral methods have emerged as a simple yet surprisingly effective approach for extracting information from massive, noisy and incomplete data. In a nutshell, spectral methods refer to a collection of algorithms built upon the eigenvalues…
Traditional spectral energy distribution (SED)-fitting methods for stellar mass estimation face persistent challenges including systematic biases and computational constraints. We present a controlled comparison of machine learning (ML) and…
Stellar population synthesis is an important method in the galaxy and star-cluster studies. In the stellar population synthesis models, stellar spectral library is necessary for the integrated spectra of the stellar population. Usually, the…
The scientific community's interest on the stellar parameters of M dwarfs has been increasing over the last few years, with potential applications ranging from galactic characterization to exoplanet detection. The main motivation for this…
Context. As increasingly more spectroscopic data are being delivered by medium- and high-resolving power multi-object spectrographs, more automatic stellar parameter determination softwares are being developed. The quality of the spectra…
Measuring the stellar position angle provides valuable information on binary stellar formation or stellar spin axis evolution. We aim to develop a method for determining the absolute stellar position angle using spectro-astrometric analysis…
Context. The Javalambre Photometric Local Universe Survey (J-PLUS) has obtained precise photometry in twelve specially designed filters for large numbers of Galactic stars. Deriving their precise stellar atmospheric parameters and…
Using the technique of spectral disentangling, it is possible to determine the individual spectra of the components of a multiple star system from composite spectra observed at a range of orbital phases. This method has several advantages:…
During the last decade, a large number of different numerical methods have been proposed to tackle the automatic identification and quantification in {\gamma}-ray spectrometry. However, the lack of common benchmarks, including datasets,…
We present the first models allowing one to explore in a consistent way the influence of changes in the alpha-element-to-iron abundance ratio on the high-resolution spectral properties of evolving stellar populations. The models cover the…
In the current panorama of large surveys, the vast amount of data obtained with different methods, data types, formats, and stellar samples, is making an efficient use of the available information difficult. The Survey of Surveys is a…
Planetary nebulae (PNe) are pivotal for advancing our knowledge of stellar evolution and galactic chemical enrichment. Recent progress in surveys and data analysis has revolutionized PN research, leading to the discovery of new objects and…
We are carrying out a search for planets around a sample of solar twin stars using the HARPS spectrograph. The goal of this project is to exploit the advantage offered by solar twins to obtain chemical abundances of unmatched precision.…
The pixel-to-pixel spectral fitting technique is often used in studies of stellar populations. It enables the user to infer several parameters from integrated light spectra such as ages and chemical abundances. In this paper, we examine the…
The study of photoionised gas in planetary nebulae (PNe) has played a major role in the achievement, over the years, of a better understanding of a number of physical processes, pertinent to a broader range of fields than that of PNe…
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…
In this work, we apply self-supervised learning with instance differentiation to learn a robust, multi-purpose representation for image analysis of resolved extragalactic continuum images. We train a multi-use model which compresses our…
Chemical tagging is a central pursuit of galactic archaeology, but requires sufficiently discriminative abundances to uniquely identify sites of star formation. This task is complicated by intrinsic scatter among conatal stars,…
We present the 4MOST-HR resolution Non-Local Thermal Equilibrium (NLTE) Payne artificial neural network (ANN), trained on $404\,793$ new FGK spectra with 16 elements computed in NLTE. This network will be part of the Stellar Abundances and…
In this paper, we present a deep learning system approach to estimating luminosity, effective temperature, and surface gravity of O-type stars using the optical region of the stellar spectra. In previous work, we compare a set of machine…