Second Data Release of the COSMOS Lyα Mapping and Tomography Observations: The First 3D Maps of the Detailed Cosmic Web at 2.05 < z < 2.55
Bibliographic record
Abstract
Abstract We present the second data release of the COSMOS Lyα Mapping And Tomography Observations Survey conducted with the Low Resolution Imaging Spectrometer on the Keck I telescope. This project used Lyα forest absorption in the spectra of faint star-forming galaxies and quasars at z ∼ 2–3 to trace neutral hydrogen in the intergalactic medium. In particular, we use 320 objects over a footprint of ∼0.2 deg2 to reconstruct the absorption field at 2.05 < z < 2.55 at ∼2 h −1 Mpc resolution. We apply a Wiener filtering technique to the observed data to reconstruct three-dimensional (3D) maps of the field over a volume of 4.1 × 105 h −3 Mpc3. In addition to the filtered flux maps, for the first time we infer the underlying dark matter field through a forward-modeling framework from a joint likelihood of galaxy and Lyα forest data, finding clear examples of the detailed cosmic web consisting of cosmic voids, sheets, filaments, and nodes. In addition to traditional figures, we present a number of interactive 3D models to allow exploration of the data and qualitative comparisons to known galaxy surveys. We find that our inferred overdensities are consistent with those found from galaxy fields. We will make all our reduced spectra, extracted Lyα forest pixel data, and reconstructed tomographic maps publicly available upon publication.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| 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) | 0.020 | 0.014 |
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".