Detecting HI Self-Absorption using Neural Networks

arXiv:2609.38042 · astro-ph.GA, astro-ph.IM · Submitted 2026-09-29 · Read on arXiv

astro-ph.GA, astro-ph.IM

Submitted: 2026-09-29

Updated: 2026-09-29

Terminology

• Accuracy — 12× in this paper · explained in 1 episode
• Calibration — 9× in this paper · explained in 7 episodes
• Trace — 5× in this paper · explained in 2 episodes
• Benchmark — 3× in this paper · explained in 2 episodes
• Consistency — 3× in this paper · explained in 3 episodes
• Adam — 2× in this paper · explained in 2 episodes
• Interpretability — 2× in this paper · explained in 15 episodes
• AUC — 1× in this paper · explained in 2 episodes
• Australian Telescope Compact Array — 1× in this paper
• Clustering — 1× in this paper · explained in 2 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Domain Shift — 1× in this paper · explained in 4 episodes
• Higher-order derivatives — 1× in this paper · explained in 2 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Metallicity — 1× in this paper · explained in 3 episodes
• Synthetic Data Generation — 1× in this paper · explained in 5 episodes

Sources

• On Calibration of Modern Neural Networks
• Deep Residual Learning for Image Recognition
• Identification of molecular line emission using Convolutional Neural Networks
• Adam: A Method for Stochastic Optimization
• Neutral gas phase distribution from HI morphology: phase separation with scattering spectra and variational autoencoders
• Measuring Calibration in Deep Learning
• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
• Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
• SmoothGrad: removing noise by adding noise

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