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Found 4 papers, 4 papers with code
Hierarchical Inducing Point Gaussian Process for Inter-domain Observations
In this work, we introduce the hierarchical inducing point GP (HIP-GP), a scalable inter-domain GP inference method that enables us to improve the approximation accuracy by increasing the number of inducing points to the millions.
Modeling the Gaia Color-Magnitude Diagram with Bayesian Neural Flows to Constrain Distance Estimates
We demonstrate an algorithm for learning a flexible color-magnitude diagram from noisy parallax and photometry measurements using a normalizing flow, a deep neural network capable of learning an arbitrary multi-dimensional probability distribution.
Statistical properties of paired fixed fields
We quantify the statistical improvement brought by these simulations, over standard ones, on different power spectra such as matter, halos, CDM, gas, stars, black-holes and magnetic fields, finding that they can reduce their variance by factors as large as $10^6$.
Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics
Improving \textsl{Gaia} parallax precision with a data-driven model of stars
Usually this prior represents beliefs about the stellar density distribution of the Milky Way.
Astrophysics of Galaxies
