Extracting the Cold Neutral Medium from Hi Emission with Deep Learning: Implications for Galactic Foregrounds at High Latitude

Resolving the phase structure of neutral hydrogen (H i) is crucial for understanding the life cycle of the interstellar medium (ISM). However, accurate measurements of H i temperature and density are limited by the availability of background continuum sources for measuring H i absorption. Here we test the use of deep learning for extracting H i 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 (fCNM) of cold neutral medium (CNM) and the correction to the optically thin H i column density for optical depth (RHI) from 21 cm emission alone. We restrict our analysis to high Galactic latitudes (| b| > 30°), where the complexity of spectral line profiles is minimized. We verify that the network accurately predicts fCNM and RHI by comparing the results with direct constraints from 21 cm absorption. By applying the network to the GALFA-H i survey, we generate large-area maps of fCNM and RHI. Although the overall contribution to the total H i column of CNM-rich structures is small (∼5%), we find that these structures are ubiquitous. Our results are consistent with the picture that small-scale structures observed in 21 cm emission aligned with the magnetic field are dominated by CNM. Finally, we demonstrate that the observed correlation between H i column density and dust reddening (E(B-V)) declines with increasing RHI, indicating that future efforts to quantify foreground Galactic E(B-V) using H i, even at high latitudes, should increase fidelity by accounting for H i phase structure.