Bibliographic record
Abstract
In this thesis we make progress toward establishing the observational method of 21 cm intensity mapping as a sensitive and efficient method for mapping the large-scale structure of the Universe. In Part I we undertake theoretical studies to better understand the potential of intensity mapping. This includes forecasting the ability of intensity mapping experiments to constrain alternative explanations to dark energy for the Universe's accelerated\nexpansion. We also considered how 21 cm observations of the neutral gas in the early Universe (after recombination but before reionization) could be used to detect primordial gravity waves, thus providing a window into cosmological inflation. Finally we showed that scientifically interesting measurements could in principle be performed using intensity mapping in the near term, using existing telescopes in pilot surveys or prototypes for larger dedicated surveys.\n\nPart II describes observational efforts to perform some of the first measurements using 21 cm intensity mapping. We develop a general data analysis pipeline for analyzing intensity mapping data from single dish radio\ntelescopes. We then apply the pipeline to observations using the Green Bank Telescope. By cross-correlating the intensity mapping survey with a traditional galaxy redshift survey we put a lower bound on the amplitude of the 21 cm signal. The auto-correlation provides an upper bound on the signal amplitude and we thus constrain the signal from both above and below. This pilot survey represents a pioneering effort in establishing 21 cm intensity mapping as a probe of the Universe.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".