Detecting H i Self-Absorption using Neural Networks

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ID: 329858
2026
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Abstract
Abstract Cold atomic hydrogen plays a crucial role in the life cycle of interstellar gas. It serves as the intermediary phase in the condensation and cooling processes that bridge the warm diffuse gas in and around galaxies, and the cold molecular gas that drives star formation. H i self-absorption in the 21-cm emission line is the most direct observational tracer of cold H i gas that does not require a continuum background source, yet its systematic extraction remains a long-standing challenge. Existing methods of self-absorption identification rely on subjective by-eye inspection or modelling of the underlying emission, making repeatable, thorough, and large-scale applications difficult. We present a lightweight convolutional neural network designed to detect self-absorption features and infer their velocities via a post-hoc process, without assumptions about the underlying emission or the use of ancillary data. Trained on synthetic emission spectra, the neural network achieves 96.5 per cent accuracy, 96.0 per cent precision, and 97.0 per cent recall on held-out synthetic data. Applied to observed 21-cm emission data from the Riegel–Crutcher cloud and giant molecular filament regions towards the Galactic Plane, the network recovers the spatial distributions and velocities of known self-absorption structures when compared to previous analyses and ancillary 13CO emission data. Crucially, the neural network is computationally efficient, processing detections for ~15, 000 spectra per second on a single consumer laptop GPU, enabling real-time cold H i detection at the data rates anticipated by next-generation facilities such as the Square Kilometre Array.
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Authors E. Müller, N. M. McClure‐Griffiths, Hiep Nguyen, Matthew J. Alger, F. Buckland-Willis, J. R. Dawson, Min-Young Lee, A. Marchal
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Journal monthly notices of the royal astronomical society
Year 2026
DOI
10.1093/mnras/stag1853
URL
https://doi.org/10.1093/mnras/stag1853
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