8 citations · 18 across the 5 of their papers we have counts for
5 papers
Debiasing with Diffusion: Probabilistic reconstruction of Dark Matter fields from galaxies with CAMELS
Victoria Ono, Core Francisco Park, Nayantara Mudur +3
Galaxies are biased tracers of the underlying cosmic web, which is dominated by dark matter components that cannot be directly observed. Galaxy formation simulations can be used to…
Hyperspectral shadow removal with Iterative Logistic Regression and latent Parametric Linear Combination of Gaussians
Core Francisco Park, Maya Nasr, Manuel Pérez-Carrasco +3
Shadow detection and removal is a challenging problem in the analysis of hyperspectral images. Yet, this step is crucial for analyzing data for remote sensing applications like met…
Probabilistic reconstruction of Dark Matter fields from biased tracers using diffusion models
Core Francisco Park, Victoria Ono, Nayantara Mudur +2
Galaxies are biased tracers of the underlying cosmic web, which is dominated by dark matter components that cannot be directly observed. The relationship between dark matter densit…
Stellar Reddening Based Extinction Maps for Cosmological Applications
Nayantara Mudur, Core Francisco Park, Douglas P Finkbeiner
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 corre…
Revisiting Latent-Space Interpolation via a Quantitative Evaluation Framework
Lu Mi, Tianxing He, Core Francisco Park +3
Latent-space interpolation is commonly used to demonstrate the generalization ability of deep latent variable models. Various algorithms have been proposed to calculate the best tr…