相关论文: Large-scale three-dimensional Gaussian process ext…

(Abridged) We use 8 micron Spitzer GLIMPSE images to make extinction maps of 10 IRDCs, selected to be relatively nearby and massive. The extinction mapping technique requires modeling the IR background intensity behind the cloud, which is…

This work uses hierarchical logistic Gaussian processes to infer true redshift distributions of samples of galaxies, through their cross-correlations with spatially overlapping spectroscopic samples. We demonstrate that this method can…

The ability to recognize previously mapped locations is an essential feature for autonomous systems. Unstructured planetary-like environments pose a major challenge to these systems due to the similarity of the terrain. As a result, the…

The ongoing Gaia mission of ESA will provide accurate spatial and kinematical information for a large fraction of stars in the Galaxy. Interstellar extinction and line absorption studies toward a large number of stars at different distances…

We present a method to infer reddenings and distances to stars, based only on their broad-band photometry, and show how this method can be used to produce a three-dimensional dust map of the Galaxy. Our method samples from the full…

We propose a new method for simplification of Gaussian process (GP) models by projecting the information contained in the full encompassing model and selecting a reduced number of variables based on their predictive relevance. Our results…

Gaussian processes are a widely embraced technique for regression and classification due to their good prediction accuracy, analytical tractability and built-in capabilities for uncertainty quantification. However, they suffer from the…

An important aspect of quenching star formation is the removal of the cold interstellar medium (ISM; non-ionised gas and dust) from a galaxy. In addition, dust grains can be destroyed in a hot or turbulent medium. The adopted timescale of…

We introduce a model for the explicit evolution of interstellar dust in a cosmological galaxy formation simulation. We post-process a simulation from the Cosmic Reionization on Computers project (CROC, Gnedin 2014), integrating an ordinary…

In this work we review the application of the theory of Gaussian processes to the modeling of noise in pulsar-timing data analysis, and we derive various useful and optimized representations for the likelihood expressions that are needed in…

Dust extinction law is crucial to recover the intrinsic energy distribution of celestial objects and infer the characteristics of interstellar dust. Based on the traditional pair method, an improved pair method is proposed to model the dust…

High-resolution JWST-MIRI images now allow us to resolve in great detail the multi-scale nature of the emission in nearby star-forming galaxies, from compact star-forming regions to large-scale diffuse emission, giving new insights into…

The accurate reconstruction of dynamic street scenes is critical for applications in autonomous driving, augmented reality, and virtual reality. Traditional methods relying on dense point clouds and triangular meshes struggle with moving…

Gaussian processes are typically used for smoothing and interpolation on small datasets. We introduce a new Bayesian nonparametric framework -- GPatt -- enabling automatic pattern extrapolation with Gaussian processes on large…

Although interstellar dust extinction serves as a powerful distance estimator, the solar system's location within the Galactic plane complicates distance determinations, especially for molecular clouds (MCs) at varying distances along the…

A variety of approximate schemes for modelling the low-density Intergalactic Medium (IGM) in the high-redshift Universe is compared to the results of a large high-resolution hydro-dynamical simulation. These schemes use either an analytical…

This letter describes an incremental multimodal surface mapping methodology, which represents the environment as a continuous probabilistic model. This model enables high-resolution reconstruction while simultaneously compressing spatial…

Gaussian processes are flexible probabilistic regression models which are widely used in statistics and machine learning. However, a drawback is their limited scalability to large data sets. To alleviate this, full-scale approximations…

Gaussian processes (GPs) are flexible, probabilistic, nonparametric models widely used in fields such as spatial statistics and machine learning. A drawback of Gaussian processes is their computational cost, with $O(N^3)$ time and $O(N^2)$…

We reconsider the origin and processing of dust in elliptical galaxies. We theoretically formulate the evolution of grain size distribution, taking into account dust supply from asymptotic giant branch (AGB) stars and dust destruction by…