Construction of a far ultraviolet all sky map from an incomplete survey: Application of a deep learning algorithm

TL;DR

This paper presents a method to create a comprehensive far ultraviolet all sky map by combining actual observations with deep learning predictions for unobserved regions, validated against GALEX data.

Contribution

It introduces a deep neural network approach to predict FUV intensities in unobserved sky regions, filling gaps in the all sky map with validated accuracy.

Findings

Predicted FUV intensities agree well with GALEX observations.

The neural network effectively estimates FUV in unobserved regions.

The constructed map enables applications like dust scattering simulations.

Abstract

We constructed a far ultraviolet (FUV) all sky map based on observations from the Far Ultraviolet Imaging Spectrograph (FIMS) aboard the Korean microsatellite STSAT-1. For the ~20% of the sky not covered by FIMS observations, predictions from a deep artificial neural network were used. Seven datasets were chosen for input parameters, including five all sky maps of H-alpha, E(B-V), N(HI), and two X-ray bands, with Galactic longitudes and latitudes. 70% of the pixels of the observed FIMS dataset were randomly selected for training as target parameters and the remaining 30% were used for validation. A simple four-layer neural network architecture, which consisted of three convolution layers and a dense layer at the end, was adopted, with an individual activation function for each convolution layer; each convolution layer was followed by a dropout layer. The predicted FUV intensities exhibited good agreement with Galaxy Evolution Explorer (GALEX) observations made in a similar FUV wavelength band for high Galactic latitudes. As a sample application of the constructed map, a dust scattering simulation was conducted with model optical parameters and a Galactic dust model for a region that included observed and predicted pixels. Overall, FUV intensities in the observed and predicted regions were reproduced well.