Parameters for the 58 {tau}HI(v) sightlines

However, accurate measurements of HI temperature and density are limited by the availability of background continuum sources for measuring HI absorption. Here we test the use of deep learning for extracting HI properties over large areas without optical depth information.

We train a 1D convolutional neural network using synthetic observations of 3D numerical simulations of the ISM to predict the fraction (f_CNM_) of cold neutral medium (CNM) and the correction to the optically thin HI column density for optical depth (R_H_I__) from 21cm emission alone. We restrict our analysis to high Galactic latitudes (|b|>30{deg}), where the complexity of spectral line profiles is minimized. We verify that the network accurately predicts f_CNM_ and R_H_I__ by comparing the results with direct constraints from 21cm absorption.