Related papers: Gaussian Process Methods for Very Large Astrometri…
The Gaussian process (GP) is a widely used probabilistic machine learning method with implicit uncertainty characterization for stochastic function approximation, stochastic modeling, and analyzing real-world measurements of nonlinear…
The vertical density distribution of stars in a galactic disc is traditionally obtained by assuming an isothermal vertical velocity dispersion of stars. Recent observations from SDSS, LAMOST, RAVE, Gaia etc show that this dispersion…
Extrinsic Gaussian process regression methods, such as wrapped Gaussian process, have been developed to analyze manifold data. However, there is a lack of intrinsic Gaussian process methods for studying complex data with manifold-valued…
High-quality novel view synthesis for large-scale scenes presents a challenging dilemma in 3D computer vision. Existing methods typically partition large scenes into multiple regions, reconstruct a 3D representation using Gaussian splatting…
The distribution of galaxies in position and velocity around the centers of galaxy clusters encodes important information about cluster mass and structure. Using the maxBCG galaxy cluster catalog identified from imaging data obtained in the…
In the last years, crowdsourcing is transforming the way classification training sets are obtained. Instead of relying on a single expert annotator, crowdsourcing shares the labelling effort among a large number of collaborators. For…
We derive unbiased distance estimates for the Gaia-TGAS dataset by correcting for the bias due to the distance dependence of the selection function, which we measure directly from the data. From these distances and proper motions, we…
The declining response rates in probability surveys along with the widespread availability of unstructured data has led to growing research into non-probability samples. Existing robust approaches are not well-developed for non-Gaussian…
Among dynamical modeling techniques, the made-to-measure (M2M) method for modeling steady-state systems is among the most flexible, allowing non-parametric distribution functions in complex gravitational potentials to be modeled efficiently…
Selection effects, such as interstellar extinction and varying survey depth, complicate efforts to determine the gravitational potential - and thus the distribution of baryonic and dark matter - throughout the Milky Way galaxy using stellar…
Nonparametric regression for massive numbers of samples (n) and features (p) is an increasingly important problem. In big n settings, a common strategy is to partition the feature space, and then separately apply simple models to each…
Spatial-temporal Gaussian process regression is a popular method for spatial-temporal data modeling. Its state-of-art implementation is based on the state-space model realization of the spatial-temporal Gaussian process and its…
Detecting and measuring a non-Gaussian signature of primordial origin in the density field is a major science goal of next-generation galaxy surveys. The signal will permit us to determine primordial physics processes and constrain models…
Edge transport barriers (ETBs) in magnetically confined fusion plasmas, commonly known as pedestals, play a crucial role in achieving high confinement plasmas. However, their defining characteristic, a steep rise in plasma pressure over…
This paper is an initial stage of consideration of the general problem of joint modeling of the vertical structure of a Galactic flat subsystem and the average surface of the disk of the Galaxy, taking into account the natural and…
Gaussian process regression (GPR) is a useful technique to predict composition--property relationships in glasses as the method inherently provides the standard deviation of the predictions. However, the technique remains restricted to…
Gaussian processes are rich distributions over functions, which provide a Bayesian nonparametric approach to smoothing and interpolation. We introduce simple closed form kernels that can be used with Gaussian processes to discover patterns…
Gaussian processes (GPs) are a class of Kernel methods that have shown to be very useful in geoscience and remote sensing applications for parameter retrieval, model inversion, and emulation. They are widely used because they are simple,…
We investigate a kinematic scaling relation between the baryonic mass and the flat velocity dispersion, i.e. mass-velocity dispersion relation (MVDR), from the brightest cluster galaxies (BCGs) to the galaxy clusters. In our studies, the…
The circular velocity curve traced by stars provides a direct means of investigating the potential and mass distribution of the Milky Way. Recent measurements of the Galaxy's rotation curve have revealed a significant decrease in velocity…