Extending Galactic foreground emission with neural networks

TL;DR

This paper presents a novel Cycle-GAN based method to simulate CO emissions in galactic foregrounds, improving the modeling of high-latitude regions using neural networks trained on Planck and HI data.

Contribution

It introduces a Cycle-GAN approach for accurate CO emission simulation, enhancing current models especially in poorly observed high-Galactic latitude areas.

Findings

The algorithm reproduces angular correlations of CO emissions.

Generated emissions match the statistical properties of real CO data.

The method improves modeling of high-latitude galactic regions.

Abstract

We introduce an innovative approach employing Cycle Generative Adversarial Networks (Cycle-GANs) to accurately simulate Carbon Monoxide (CO) emissions by learning features identified in thermal dust emission maps from the Planck satellite alongside HI data from HI4PI survey. Our training dataset is complemented by the targets represented by the two rotational transition lines of CO (J:1-0, J:2-1) provided by the Planck satellite. We ensure the robustness of our dataset by focusing on regions with a signal-to-noise ratio (SNR) exceeding 8. The outcomes, assessed utilizing angular power spectra and Minkowski functionals, confirm that our algorithm proficiently achieves the set goals, indicating that the amplitudes of the generated emission accurately reproduce the angular correlations and share the statistical properties of the employed CO targets. We thus aim at improving the current models of CO emission specifically in the high-Galactic latitude areas that have been hardly observed by the most recent surveys, and, in doing so, to address and overcome the limitations affecting current models regions. This research lays the groundwork for creating transformative synthetic simulations, leveraging convolutional neural networks tied to data procured from latest observations.