21cmVAE: A VAE-based Emulator of the 21-cm Global Signal
Bibliographic record
Abstract
Considerable observational efforts are being dedicated to measuring the sky-averaged (global) 21-cm signal of neutral hydrogen from Cosmic Dawn and the Epoch of Reionization. Deriving observational constraints on the astrophysics of this era requires modelling tools that can quickly and accurately generate theoretical signals across the wide astrophysical parameter space. For this purpose artificial neural networks were used to create the only two existing global signal emulators 21cmGEM and globalemu. In this paper we introduce 21cmVAE, a global signal emulator based on advanced machine learning methods such as variational autoencoder (VAE) and trained with the same dataset of ~ 30,000 global signals as the other two emulators. The VAE allows us to explore a low-dimensional representation of the dataset and establish the most important astrophysical processes that drive the global 21-cm signal at different epochs. 21cmVAE has a relative rms error of only 0.41\% -- equivalently 0.66 mK -- on average, which is a significant improvement compared to the existing emulators, and a run time of 0.04 seconds per parameter set. The emulator, the code, and the processed datasets are publicly available at this https URL and through this http URL.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".