Related papers: Stellar Inversion Techniques
Stellar population synthesis can be approached as an inverse problem. The physical information is extracted from the observations through an inverse model. The process requires the transformation of the observational errors into model…
Asteroseismology involves using the resonant frequencies of a star to infer details about its internal structure and evolutionary state. Oscillation frequencies are most useful when accompanied by accurate measurements of the more…
This course reviews the rotational properties of non-degenerate stars as observed from the protostellar stage to the end of the main sequence. It includes an introduction to the various observational techniques used to measure stellar…
In this paper a new method is presented to find the transfer function of the broad-line region in active galactic nuclei. The subtractive optimally localized averages (SOLA) method is a modified version of the Backus-Gilbert method and is…
The solar meridional flow is a crucial ingredient in modern dynamo theory. Seismic estimates of this flow have, however, been contradictory in deeper layers below about $0.9\,R_\odot$. Results from time-distance helioseismology have so far…
Observations of the Sun in the visible spectral range belong to standard measurements obtained by instruments both on the ground and in the space. Nowadays, both nearly continuous full-disc observations with medium resolution and dedicated…
The stellar population synthesis in unresolved composite objects is a very tricky problem. Indeed, it is a degenerate problem since many parameters affect the observables. The stellar population synthesis issue thus deserves a deep and…
Spectral retrieval techniques are currently our best tool to interpret the observed exoplanet atmospheric data. Said techniques retrieve the optimal atmospheric components and parameters by identifying the best fit to an observed…
We study the oscillations of relativistic stars, incorporating key physics associated with internal composition, thermal gradients and crust elasticity. Our aim is to develop a formalism which is able to account for the state-of-the-art…
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…
During the last decades, numerous observational and theoretical efforts in the study of solar oscillations, have brought to a detailed knowledge of the interior of the Sun. While this discipline has not yet exhausted its resources and…
We provide a status report on the determination of stellar ages from asteroseismology for stars of various masses and evolutionary stages. The ability to deduce the ages of stars with a relative precision of typically 10 to 20% is a unique…
Stars play a key role in the evolution of the Universe, as sources of radiation, as dynamical engines, and as chemical factories. Outputs of stellar models are then central to various studies in astrophysics. Stellar physics links…
Regularized kernel methods such as support vector machines (SVM) and support vector regression (SVR) constitute a broad and flexible class of methods which are theoretically well investigated and commonly used in nonparametric…
Eclipsing binary stars have long served as benchmark systems to measure fundamental stellar properties. In the past few decades, asteroseismology - the study of stellar pulsations - has emerged as a new powerful tool to study the structure…
The general context of this study is the inversion of stellar fundamental parameters from high-resolution Echelle spectra. We aim at developing a fast and reliable tool for the post-processing of spectra produced, in particular, by the…
Understanding the properties of interstellar turbulence is a great intellectual challenge and the urge to solve this problem is partially motivated by a necessity to explain the star formation mystery. This review deals with a recently…
In this contribution we briefly review some of the current issues and promises for the future by asteroseismology. We are entering a new phase in this field driven by the wealth of data that has been collected and data that will soon be…
Regression methods based in Machine Learning Algorithms (MLA) have become an important tool for data analysis in many different disciplines. In this work, we use MLA in an astrophysical context; our goal is to measure the mean longitudinal…
Knowing the conserved quantities that a galaxy's stellar orbits conform to is important in helping us understand the stellar distribution and structures within the galaxy. Isolating integrals of motion and resonances are particularly…