Simulating a full-sky high resolution Galactic synchrotron spectral index map using neural networks
M. O. Irfan

TL;DR
This paper develops a neural network-based model to generate high-resolution Galactic synchrotron spectral index maps, aiding in foreground removal for 21 cm cosmology experiments by providing realistic simulations of Galactic emission.
Contribution
The work introduces a convolutional neural network approach to produce high-resolution spectral index maps from lower-resolution data, improving the realism of Galactic foreground models for cosmological studies.
Findings
Neural network maps match observational data at 14.4 arcmin resolution.
Foreground removal effectiveness varies with spectral index model resolution.
No resolution finer than 5 degrees is necessary for effective foreground subtraction.
Abstract
We present a model for the full-sky diffuse Galactic synchrotron spectral index with an appropriate level of spatial structure for a resolution of 56 arcmin (to match the resolution of the Haslam 408 MHz data). Observational data at 408 MHz and 23 GHz have been used to provide spectral indices at a resolution of 5 degrees. In this work we make use of convolutional neural networks to provide a realistic proxy for the higher resolution information, in place of the genuine structure. Our deep learning algorithm has been trained using 14.4 arcmin observational data from the 1.4 GHz Parkes radio continuum survey. We compare synchrotron emission maps constructed by extrapolating the Haslam data using various spectral index maps, of different angular resolution, with the Global Sky Model. We add these foreground maps to a total emission model for a 21 cm intensity mapping experiment, then…
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Taxonomy
TopicsParticle Accelerators and Free-Electron Lasers · Radio Astronomy Observations and Technology
