Related papers: Large-scale three-dimensional Gaussian process ext…
Large stellar surveys are sensitive to interstellar dust through the effects of reddening. Using extinctions measured from photometry and spectroscopy, together with three-dimensional (3D) positions of individual stars, it is possible to…
Aims: Highly resolved maps of the local Galactic dust are an important ingredient for sky emission models. In nearly the whole electromagnetic spectrum one can see imprints of dust, many of which originate from dust clouds within 300pc.…
We present a map of the three-dimensional (3D) distribution of dust in the Orion complex. Orion is the closest site of high-mass star formation, making it an excellent laboratory for studying the interstellar medium and star formation. We…
Context. While Gaia enables to probe in great detail the extended local neighbourhood, the thin disk structure at larger distances remains sparsely explored. Aims. We aim here to build a non-parametric 3D model of the thin disc structures…
Gaussian process is a theoretically appealing model for nonparametric analysis, but its computational cumbersomeness hinders its use in large scale and the existing reduced-rank solutions are usually heuristic. In this work, we propose a…
We present a deep, high-angular resolution 3D dust map of the southern Galactic plane over $239^\circ < \ell < 6^\circ$ and $|b| < 10^\circ$ built on photometry from the DECaPS2 survey, in combination with photometry from VVV, 2MASS, and…
Gaussian Processes are widely used for regression tasks. A known limitation in the application of Gaussian Processes to regression tasks is that the computation of the solution requires performing a matrix inversion. The solution also…
We present a novel methodology for mapping dust extinction in nearby galaxies at parsec-scale resolution. We apply it to HST 68 fields within the Small and Large Magellanic Clouds (23 fields in the SMC and 45 fields in the LMC) using…
We present a method for accurately and precisely inferring photometric dust extinction towards stars at mid-to-high Galactic latitudes using probabilistic machine learning to model the colour-magnitude distribution of zero-extinction stars…
Analysis of cosmic shear is an integral part of understanding structure growth across cosmic time, which in-turn provides us with information about the nature of dark energy. Conventional methods generate \emph{shear maps} from which we can…
Interstellar dust corrupts nearly every stellar observation, and accounting for it is crucial to measuring physical properties of stars. We model the dust distribution as a spatially varying latent field with a Gaussian process (GP) and…
Galactic interstellar dust has a profound impact not only on our observations of objects throughout the Universe, but also on the morphology, star formation, and chemical evolution of the Galaxy. The advent of massive imaging and…
We present a method to simultaneously infer the interstellar extinction parameters $A_0$ and $R_0$, stellar effective temperature $T_{\rm eff}$, and distance modulus $\mu$ in a Bayesian framework. Using multi-band photometry from SDSS and…
Dust plays a critical role in the study of the interstellar medium (ISM). Extinction maps derived from optical surveys often fail to capture regions with high column density due to the limited photometric depth in optical wavelengths. To…
Gaussian processes are popular and flexible models for spatial, temporal, and functional data, but they are computationally infeasible for large datasets. We discuss Gaussian-process approximations that use basis functions at multiple…
The Sun is located close to the Galactic mid-plane, meaning that we observe the Galaxy through significant quantities of dust. Moreover, the vast majority of the Galaxy's stars also lie in the disc, meaning that dust has an enormous impact…
Cosmological surveys must correct their observations for the reddening of extragalactic objects by Galactic dust. Existing dust maps, however, have been found to have spatial correlations with the large-scale structure of the Universe.…
The Gaussian process is a powerful and flexible technique for interpolating spatiotemporal data, especially with its ability to capture complex trends and uncertainty from the input signal. This chapter describes Gaussian processes as an…
We present a new version of our analytical model of the spatial interstellar extinction variations within the nearest kiloparsec. This model treats the 3D dust distribution as a superposition of three overlapping layers: (1) the layer along…
Gaussian process is an indispensable tool in clustering functional data, owing to it's flexibility and inherent uncertainty quantification. However, when the functional data is observed over a large grid (say, of length $p$), Gaussian…