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Title: A generative model of galactic dust emission using variational autoencoders
ABSTRACT Emission from the interstellar medium can be a significant contaminant of measurements of the intensity and polarization of the cosmic microwave background (CMB). For planning CMB observations, and for optimizing foreground-cleaning algorithms, a description of the statistical properties of such emission can be helpful. Here, we examine a machine learning approach to inferring the statistical properties of dust from observational data. In particular, we apply a type of neural network called a variational autoencoder (VAE) to maps of the intensity of emission from interstellar dust as inferred from Planck sky maps and demonstrate its ability to (i) simulate new samples with similar summary statistics as the training set, (ii) provide fits to emission maps withheld from the training set, and (iii) produce constrained realizations. We find VAEs are easier to train than another popular architecture: that of generative adversarial networks, and are better suited for use in Bayesian inference.
Authors:
; ;
Award ID(s):
1852617
Publication Date:
NSF-PAR ID:
10232682
Journal Name:
Monthly Notices of the Royal Astronomical Society
Volume:
504
Issue:
2
Page Range or eLocation-ID:
2603 to 2613
ISSN:
0035-8711
Sponsoring Org:
National Science Foundation
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