Query : 2021A&A...650A.100M

2021A&A...650A.100M - Astronomy and Astrophysics, volume 650A, 100-100 (2021/6-1)

Bayesian decomposition of the Galactic multi-frequency sky using probabilistic autoencoders.


Abstract (from CDS):

Context. All-sky observations show both Galactic and non-Galactic diffuse emission, for example from interstellar matter or the cosmic microwave background (CMB). The decomposition of the emission into different underlying radiative components is an important signal reconstruction problem.
Aims. We aim to reconstruct radiative all-sky components using spectral data, without incorporating knowledge about physical or spatial correlations.
Methods. We built a self-instructing algorithm based on variational autoencoders following three steps: (1)We stated a forward model describing how the data set was generated from a smaller set of features, (2) we used Bayes' theorem to derive a posterior probability distribution, and (3) we used variational inference and statistical independence of the features to approximate the posterior. From this, we derived a loss function and optimized it with neural networks. The resulting algorithm contains a quadratic error norm with a self-adaptive variance estimate to minimize the number of hyperparameters. We trained our algorithm on independent pixel vectors, each vector representing the spectral information of the same pixel in 35 Galactic all-sky maps ranging from the radio to the γ-ray regime.
Results. The algorithm calculates a compressed representation of the input data. We find the feature maps derived in the algorithm's latent space show spatial structures that can be associated with all-sky representations of known astrophysical components. Our resulting feature maps encode (1) the dense interstellar medium (ISM), (2) the hot and dilute regions of the ISM, and (3) the CMB, without being informed about these components a priori.
Conclusions. We conclude that Bayesian signal reconstruction with independent Gaussian latent space statistics is sufficient to reconstruct the dense and the dilute ISM, as well as the CMB, from spectral correlations only. The computational approximation of the posterior can be performed efficiently using variational inference and neural networks, making them a suitable approach to probabilistic data analysis.

Abstract Copyright: © S. Milosevic et al. 2021

Journal keyword(s): methods: data analysis - methods: statistical - techniques: image processing - Galaxy: general - ISM: structure

VizieR on-line data: <Available at CDS (J/A+A/650/A100): list.dat fits/*>

Simbad objects: 9

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Number of rows : 9
N Identifier Otype ICRS (J2000)
ICRS (J2000)
Mag U Mag B Mag V Mag R Mag I Sp type #ref
1850 - 2024
1 NAME Magellanic Clouds GrG 03 00 -71.0           ~ 6980 1
2 NAME Per Region reg 03 37 00.0 +31 15 00           ~ 270 0
3 NAME Ori Region reg 05 35 17.30 -05 23 28.0           ~ 573 0
4 NAME North Polar Spur ISM 12 00 +00.0           ~ 366 1
5 NAME Ophiuchus Molecular Cloud SFR 16 28 06 -24 32.5           ~ 3595 1
6 NAME Fermi Bubbles bub 17 45 -29.0           ~ 430 0
7 NAME Galactic Center reg 17 45 39.60213 -29 00 22.0000           ~ 14209 0
8 NAME Cyg Complex reg 20 32 +45.0           ~ 597 0
9 NAME Vela Field reg ~ ~           ~ 182 0

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