相关论文: Denoising Milky Way stellar survey data with norma…
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.…
The formation and structure of the Milky Way has a fundamental role in our understanding of the universe and its evolution, and thanks to the Gaia mission and large spectroscopic surveys, we live an exceptional moment of data availability,…
Denoising diffusions are state-of-the-art generative models exhibiting remarkable empirical performance. They work by diffusing the data distribution into a Gaussian distribution and then learning to reverse this noising process to obtain…
Normalizing flow is a generative modeling approach with efficient sampling. However, Flow-based models suffer two issues: 1) If the target distribution is manifold, due to the unmatch between the dimensions of the latent target distribution…
The Gaia mission will have a profound impact on our understanding of the structure and dynamics of the Milky Way. Gaia is providing an exhaustive census of stellar parallaxes, proper motions, positions, colors and radial velocities, but…
The large-scale structure in cosmology is highly non-Gaussian at late times and small length scales, making it difficult to describe analytically. Parameter inference, data reconstruction, and data generation tasks in cosmology are greatly…
We explore the vicinity of the Milky Way through the use of spectro-photometric data from the Sloan Digital Sky Survey and high-quality proper motions derived from multi-epoch positions extracted from the Guide Star Catalogue II database.…
The Gaia astrometric mission may offer an unprecedented opportunity to discover new tidal streams in the Galactic halo. To test this, we apply nGC3, a great-circle-cell count method that combines position and proper motion data to identify…
The future space based gravitational wave detector LISA (Laser Interferometer Space Antenna) will observe millions of Galactic binaries constantly present in the data stream. A small fraction of this population (of the order of several…
Measuring the density profile of dark matter in the Solar neighborhood has important implications for both dark matter theory and experiment. In this work, we apply autoregressive flows to stars from a realistic simulation of a Milky…
As a class of generative artificial intelligence frameworks inspired by statistical physics, diffusion models have shown extraordinary performance in synthesizing complicated data distributions through a denoising process gradually guided…
Subject of this paper is the simplification of Markov chain Monte Carlo sampling as used in Bayesian statistical inference by means of normalising flows, a machine learning method which is able to construct an invertible and differentiable…
In observational astronomy, noise obscures signals of interest. Large-scale astronomical surveys are growing in size and complexity, which will produce more data and increase the workload of data processing. Developing automated tools, such…
We have combined observations of Galactic high-velocity HI from two surveys: a very sensitive survey from the Green Bank 140ft Telescope with limited sky coverage, and the less sensitive but complete Galactic All Sky Survey from the 64m…
This paper shows how a self-consistent dynamical model can be obtained by fitting the gravitational potential of the Milky Way to the stellar kinematics and densities from Gaia data. Using the Besancon Galaxy Model we derive a potential and…
Normalizing flows transform a simple base distribution into a complex target distribution and have proved to be powerful models for data generation and density estimation. In this work, we propose a novel type of normalizing flow driven by…
We present SkyCURTAINs, a data driven and model agnostic method to search for stellar streams in the Milky Way galaxy using data from the Gaia telescope. SkyCURTAINs is a weakly supervised machine learning algorithm that builds a background…
Supervised Gaussian denoisers exhibit limited generalization when confronted with out-of-distribution noise, due to the diverse distributional characteristics of different noise types. To bridge this gap, we propose a histogram matching…
We construct a dynamical model of the Milky Way disk from a data set, which combines Gaia EDR3 and APOGEE data throughout Galactocentric radii between $5.0\leq R\leq19.5$ kpc. We make use of the spherically-aligned Jeans Anisotropic Method…
The Milky Way halo is one of the few galactic haloes that provides a unique insight into galaxy formation by resolved stellar populations. Here, we present a catalogue of $\sim$47 million halo stars selected independent of parallax and…