21cmVAE: A Very Accurate Emulator of the 21 cm Global Signal
Bibliographic record
Abstract
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 modeling 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 neural-network-based global signal emulator, trained on the same data set of ∼30,000 global signals as the other two emulators, but with a more direct prediction algorithm that prioritizes accuracy and simplicity. Using neural networks, we compute derivatives of the signals with respect to the astrophysical parameters 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 0.34%—equivalently 0.54 mK—on average, which is a significant improvement compared to the existing emulators, and a run time of 0.04 s per parameter set. The emulator, the code, and the processed data sets are publicly available at https://github.com/christianhbye/21cmVAE and through https://zenodo.org/record/5904939 .
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".