相关论文: Inferring Stellar Parameters from Iodine-Imprinted…
To accurately interpret the observed properties of exoplanets, it is necessary to first obtain a detailed understanding of host star properties. However, physical models that analyze stellar properties on a per-star basis can become…
We describe a new method to derive clean, iodine-free spectra directly from observations acquired using high-resolution echelle spectrographs equipped with iodine cells. The main motivation to obtain iodine-free spectra is to use portions…
Owing to the remarkable photometric precision of space observatories like Kepler, stellar and planetary systems beyond our own are now being characterized en masse for the first time. These characterizations are pivotal for endeavors such…
In the current era of stellar spectroscopic surveys, synthetic spectral libraries are the basis for the derivation of stellar parameters and chemical abundances. In this paper, we compare the stellar parameters determined using five popular…
(abridged) Elemental abundances of FGK stars can be derived routinely from high-resolution optical spectra, but this remains considerably more difficult for cooler stars. Machine-learning methods offer a practical route to infer otherwise…
A fundamental challenge for wide-field imaging surveys is obtaining follow-up spectroscopic observations: there are > $10^9$ photometrically cataloged sources, yet modern spectroscopic surveys are limited to ~few x $10^6$ targets. As we…
The detection and subsequent characterisation of exoplanets are intimately linked to the characteristics of their host star. Therefore, it is necessary to study the star in detail in order to understand the formation history and…
The study of stellar parameters of planet-hosting stars, such as metallicity and chemical abundances, help us to understand the theory of planet formation and stellar evolution. Here, we present a catalogue of accurate stellar atmospheric…
High-resolution spectroscopic measurements of OB stars are important for understanding processes like stellar evolution, but require labor-intensive observations. In contrast, photometric missions like the Transiting Exoplanet Survey…
The physical properties of transiting exoplanets are connected with the physical properties of their host stars. We present a homogeneous spectroscopic analysis based on spectra of FGK-type stars observed with the Hydra spectrograph on the…
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…
We applied machine learning to the entire data history of ESO's High Accuracy Radial Velocity Planet Searcher (HARPS) instrument. Our primary goal was to recover the physical properties of the observed objects, with a secondary emphasis on…
Machine Learning is an efficient method for analyzing and interpreting the increasing amount of astronomical data that is available. In this study, we show, a pedagogical approach that should benefit anyone willing to experiment with Deep…
Planetary studies demand precise and accurate stellar parameters as input to infer the planetary properties. Different methods often provide different results that could lead to biases in the planetary parameters. In this work, we present a…
Context. An increasing number of high-resolution stellar spectra is available today thanks to many past and ongoing extensive spectroscopic surveys. Consequently, the scientific community needs automatic procedures to derive atmospheric…
A key to understand exoplanets is characterisation of their host stars. One of the most powerful tools to characterise stellar properties like effective temperature, surface gravity and metallicity, is spectroscopy based on observations of…
Context: New spectroscopic surveys will increase the number of astronomical objects requiring characterization by over tenfold.. Machine learning tools are required to address this data deluge in a fast and accurate fashion. Most machine…
With the advent of dedicated photometric space missions, the ability to rapidly process huge catalogues of stars has become paramount. Bellinger and Angelou et al. (2016) recently introduced a new method based on machine learning for…
We present empirical calibrations that estimate stellar metallicity, effective temperature and surface gravity as a function of Lick/IDS indices. These calibrations have been derived from a training set of 261 stars for which (1)…
Stellar parameters for large samples of stars play a crucial role in constraining the nature of stars and stellar populations in the Galaxy. An increasing number of medium-band photometric surveys are presently used in estimating stellar…