相关论文: AIMS - A new tool for stellar parameter determinat…
We present a novel approach to deriving stellar labels for stars observed in MUSE fields making use of data-driven machine learning methods. Taking advantage of the comparable spectral properties (resolution, wavelength coverage) of the…
Originally proposed as a cosmological probe of the large-scale structure, line intensity mapping (LIM) also offers a unique window into the astrophysics of galaxy evolution. Adding to the astrophysical explorations of LIM technique that…
Agile Earth Observation Satellites (AEOSs) constellations offer unprecedented flexibility for monitoring the Earth's surface, but their scheduling remains challenging under large-scale scenarios, dynamic environments, and stringent…
It has been observed that deep neural networks (DNNs) often use both genuine as well as spurious features. In this work, we propose "Amending Inherent Interpretability via Self-Supervised Masking" (AIM), a simple yet interestingly effective…
Approximate probabilistic inference algorithms are central to many fields. Examples include sequential Monte Carlo inference in robotics, variational inference in machine learning, and Markov chain Monte Carlo inference in statistics. A key…
We present what constraints on opacities can be derived from the analysis of stellar pulsations of BA-type main-sequence stars. This analysis consists of the construction of complex seismic models which reproduce the observed frequencies as…
Cosmological galaxy formation simulations are still limited by their spatial/mass resolution and cannot model from first principles some of the processes, like star formation, that are key in driving galaxy evolution. As a consequence they…
BASE-9 is a Bayesian software suite that recovers star cluster and stellar parameters from photometry. BASE-9 is useful for analyzing single-age, single-metallicity star clusters, binaries, or single stars, and for simulating such systems.…
I introduce an algorithm for estimating parameters from multidimensional data based on forward modelling. In contrast to many machine learning approaches it avoids fitting an inverse model and the problems associated with this. The…
Asteroseismic parameters allow us to measure the basic stellar properties of field giants observed far across the Galaxy. Most of such determinations are, up to now, based on simple scaling relations involving the large frequency…
The continuous progress toward more precise cosmological surveys and experiments has galvanized recent interest into consistency tests on cosmological parameters and models. At the heart of this effort is quantifying the degree of…
AIOps (Artificial Intelligence for IT Operations) solutions leverage the tremendous amount of data produced during the operation of large-scale systems and machine learning models to assist software practitioners in their system operations.…
Asteroseismology provides a powerful way to constrain stellar parameters. Solar-like oscillations have been observed on subgiant stars with the \emph{Kepler\/} mission. The continuous and high-precision time series enables us to carry out a…
We present a unified framework to derive fundamental stellar parameters by combining all available observational and theoretical information for a star. The algorithm relies on the method of Bayesian inference, which for the first time…
Asteroseismology has started to provide constraints on stellar properties that will be essential to accurately reconstruct the history of the Milky Way. Here we look at the information content in data sets representing current and future…
Coding and computation remain major bottlenecks in Markov chain Monte Carlo (MCMC) workflows, especially as modern sampling algorithms have become increasingly complex and existing probabilistic programming systems remain limited in model…
The launch of NASA's Kepler space telescope in 2009 revolutionized the quality and quantity of observational data available for asteroseismic analysis. While Kepler was able to detect solar-like oscillations in hundreds of main-sequence and…
Large-scale stellar surveys coupled with recent developments in magneto-hydrodynamical simulations of the formation of Milky Way-mass galaxies provide an unparalleled opportunity to unveil the physical processes driving the evolution of the…
One of the great challenges in understanding stars is measuring their masses. The best methods for measuring stellar masses include binary interaction, asteroseismology and stellar evolution models, but these methods are not ideal for red…
This thesis focuses on the discovery of stochastic differential equations (SDEs) and stochastic partial differential equations (SPDEs) from noisy and discrete time series. A major challenge is selecting the simplest possible correct model…