Related papers: Generative Models of Multi-channel Data from a Sin…
Galactic dust emission is often accounted for in cosmic microwave background (CMB) analyses by fitting a simple two-parameter modified blackbody (MBB) model in each pixel, which nominally accounts for the temperature and opacity of the…
Deep generative models such as diffusion and flow matching are powerful machine learning tools capable of learning and sampling from high-dimensional distributions. They are particularly useful when the training data appears to be…
Understanding the dynamics of climate variables is paramount for numerous sectors, like energy and environmental monitoring. This study focuses on the critical need for a precise mapping of environmental variables for national or regional…
We study the spectral and morphological characteristics of the diffuse Galactic emission in the WMAP temperature data using a template-based multi-linear regression, and obtain the following results. 1. We confirm previous observations of a…
We have combined infrared data with HI, H2 and HII surveys in order to spatially decompose the observed dust emission into components associated with different phases of the gas. An inversion technique is applied. For the decomposition, we…
Generating samples from limited information is a fundamental problem across scientific domains. Classical maximum entropy methods provide principled uncertainty quantification from moment constraints but require sampling via MCMC or…
We present a method, based on Bayesian statistics, to fit the dust emission parameters in the far-infrared and submillimeter wavelengths. The method estimates the dust temperature and spectral emissivity index, plus their relationship,…
We present the Planck Sky Model (PSM), a parametric model for the generation of all-sky, few arcminute resolution maps of sky emission at submillimetre to centimetre wavelengths, in both intensity and polarisation. Several options are…
Global ambient air pollution, a transboundary challenge, is typically addressed through interventions relying on data from spatially sparse and heterogeneously placed monitoring stations. These stations often encounter temporal data gaps…
The upcoming generation of cosmic microwave background (CMB) experiments face a major challenge in detecting the weak cosmic B-mode signature predicted as a product of primordial gravitational waves. To achieve the required sensitivity…
Dust formation in brown dwarf atmospheres is studied by utilising a model for driven turbulence in the mesoscopic scale regime. We apply a pseudo-spectral method where waves are created and superimposed within a limited wavenumber interval.…
Generative modeling is typically framed as learning mapping rules, but from an observer's perspective without access to these rules, the task becomes disentangling the geometric support from the probability distribution. We propose that…
Using statistical physics methods, we study generative diffusion models in the regime where the dimension of space and the number of data are large, and the score function has been trained optimally. Our analysis reveals three distinct…
We demonstrate the use of deep network to learn the distribution of data from state-of-the-art hydrodynamic simulations of the CAMELS project. To this end, we train a generative adversarial network to generate images composed of three…
While working within the spatial domain can pose problems associated with ill-conditioned scores caused by power-law decay, recent advances in diffusion-based generative models have shown that transitioning to the wavelet domain offers a…
Polarized dust emission is a major astrophysical foreground contaminant of the cosmic microwave background polarization (CMB), which must be accurately measured to look for the faint primordial polarization B-modes of inflationary origin.…
[Abridged] We present updated estimates of Galactic foreground emission using seven years of WMAP data. Using the power spectrum of differences between multi-frequency template-cleaned maps, we find no evidence for foreground contamination…
Channel models that represent various operating conditions a communication system might experience are important for design and standardization of any communication system. While statistical channel models have long dominated this space,…
Along with the prosperity of generative artificial intelligence (AI), its potential for solving conventional challenges in wireless communications has also surfaced. Inspired by this trend, we investigate the application of the advanced…
Spatial fields in the Earth and environmental sciences are often available at multiple scales or resolutions. While coarse-scale data (e.g., from global circulation models) are often abundant, they lack the local detail provided by…