Mapping large-scale-structure evolution over cosmic times
Bibliographic record
Abstract
Abstract This paper outlines the science case for line-intensity mapping with a space-borne instrument targeting the sub-millimeter (microwaves) to the far-infrared (FIR) wavelength range. Our goal is to observe and characterize the large-scale structure in the Universe from present times to the high redshift Epoch of Reionization. This is essential to constrain the cosmology of our Universe and form a better understanding of various mechanisms that drive galaxy formation and evolution. The proposed frequency range would make it possible to probe important metal cooling lines such as [CII] up to very high redshift as well as a large number of rotational lines of the CO molecule. These can be used to trace molecular gas and dust evolution and constrain the buildup in both the cosmic star formation rate density and the cosmic infrared background (CIB). Moreover, surveys at the highest frequencies will detect FIR lines which are used as diagnostics of galaxies and AGN. Tomography of these lines over a wide redshift range will enable invaluable measurements of the cosmic expansion history at epochs inaccessible to other methods, competitive constraints on the parameters of the standard model of cosmology, and numerous tests of dark matter, dark energy, modified gravity and inflation. To reach these goals, large-scale structure must be mapped over a wide range in frequency to trace its time evolution and the surveyed area needs to be very large to beat cosmic variance. Only a space-borne mission can properly meet these requirements.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".