Sharpening up Galactic all-sky maps with complementary data - A machine learning approach

Abstract

Galactic all-sky maps at very disparate frequencies, like in the radio and $\gamma$-ray regime, show similar morphological structures. This mutual information reflects the imprint of the various physical components of the interstellar medium. We want to use multifrequency all-sky observations to test resolution improvement and restoration of unobserved areas for maps in certain frequency ranges. For this we aim to reconstruct or predict from sets of other maps all-sky maps that, in their original form, lack a high resolution compared to other available all-sky surveys or are incomplete in their spatial coverage. Additionally, we want to investigate the commonalities and differences that the ISM components exhibit over the electromagnetic spectrum. We build an $n$-dimensional representation of the joint pixel-brightness distribution of $n$ maps using a Gaussian mixture model and see how predictive it is: How well can one map be reproduced based on subsets of other maps? Tests with mock data show that reconstructing the map of a certain frequency from other frequency regimes works astonishingly well, predicting reliably small-scale details well below the spatial resolution of the initially learned map. Applied to the observed multifrequency data sets of the Milky Way this technique is able to improve the resolution of, e.g., the low-resolution Fermi LAT maps as well as to recover the sky from artifact-contaminated data like the ROSAT 0.855 keV map. The predicted maps generally show less imaging artifacts compared to the original ones. A comparison of predicted and original maps highlights surprising structures, imaging artifacts (fortunately not reproduced in the prediction), and features genuine to the respective frequency range that are not present at other frequency bands. We discuss limitations of this machine learning approach and ideas how to overcome them.

References in corpus (27)

• HEALPix -- a Framework for High Resolution Discretization, and Fast Analysis of Data Distributed on the Sphere
• HI4PI: A full-sky HI survey based on EBHIS and GASS
• Cosmic-ray propagation and interactions in the Galaxy
• The New Galaxy: Signatures of its Formation
• Giant Gamma-ray Bubbles from Fermi-LAT: AGN Activity or Bipolar Galactic Wind?
• A Joint Analysis of BICEP2/Keck Array and Planck Data
• Extrapolation of Galactic Dust Emission at 100 Microns to CMBR Frequencies Using FIRAS
• A Full-Sky H-alpha Template for Microwave Foreground Prediction
• The Fermi Large Area Telescope On Orbit: Event Classification, Instrument Response Functions, and Calibration
• A model of diffuse Galactic Radio Emission from 10 MHz to 100 GHz
• IRIS: A new generation of IRAS maps
• Revised Catalog of GALEX Ultraviolet Sources. I. The All-sky Survey: GUVcat_AIS
• Planck 2015 results. VIII. High Frequency Instrument data processing: Calibration and maps
• Planck intermediate results. XVII. Emission of dust in the diffuse interstellar medium from the far-infrared to microwave frequencies
• Information field theory for cosmological perturbation reconstruction and non-linear signal analysis
• The $AKARI$ Far-Infrared All-Sky Survey Maps
• NIFTY - Numerical Information Field Theory - a versatile Python library for signal inference
• A Full-sky, High-resolution Atlas of Galactic 12 micron Dust Emission with WISE
• The Denoised, Deconvolved, and Decomposed Fermi $\gamma$-ray sky - An application of the D${}^3$PO algorithm
• Supernova Remnants Interacting with Molecular Clouds: X-ray and Gamma-ray Signatures
• Calibration of the AKARI Far-infrared All Sky Survey Maps
• A radio continuum survey of the southern sky at 1420 MHz. Observations and data reduction
• D${}^3$PO - Denoising, Deconvolving, and Decomposing Photon Observations
• All-sky census of Galactic high-latitude molecular intermediate-velocity clouds
• Correlation between galactic HI and the Cosmic Microwave Background
• Novel Techniques for Decomposing Diffuse Backgrounds
• SOMBI: Bayesian identification of parameter relations in unstructured cosmological data

Cited by in corpus (3)

• Surveying the reach and maturity of machine learning and artificial intelligence in astronomy
• Bayesian decomposition of the Galactic multi-frequency sky using probabilistic autoencoders
• Machine learning application to Fermi-LAT data: sharpening all-sky map and emphasizing variable sources