A generative model of galactic dust emission using variational autoencoders. Issue 2 (12th April 2021)

• Record Type: Journal Article
• Authors: Thorne, Ben; Knox, Lloyd; Prabhu, Karthik
• Is Part Of: Monthly notices of the Royal Astronomical Society. Volume 504:Issue 2(2021)
• Page Start: 2603
• Page End: 2613
• Publication Date: 2021-04-12
• Subjects: methods: statistical -- ISM: general -- cosmic background radiation; Astronomy -- Periodicals; Periodicals; 520.5
• DOI: 10.1093/mnras/stab1011
• Languages: English
• ISSNs: 0035-8711

Abstract

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.