LATIS: The Lyα Tomography IMACS Survey
Bibliographic record
Abstract
Abstract We introduce LATIS, the Lyα Tomography IMACS Survey, a spectroscopic survey at Magellan designed to map the z = 2.2–2.8 intergalactic medium (IGM) in three dimensions by observing the Lyα forest in the spectra of galaxies and QSOs. Within an area of 1.7 deg2, we will observe approximately half of ≳L* galaxies at z = 2.2–3.2 for typically 12 hr, providing a dense network of sightlines piercing the IGM with an average transverse separation of 2.5 h −1 comoving Mpc (1 physical Mpc). At these scales, the opacity of the IGM is expected to be closely related to the dark matter density, and LATIS will therefore map the density field in the z ∼ 2.5 universe at ∼Mpc resolution over the largest volume to date. Ultimately, LATIS will produce approximately 3800 spectra of z = 2.2–3.2 galaxies that probe the IGM within a volume of 4 × 106 h −3 Mpc3, large enough to contain a representative sample of structures from protoclusters to large voids. Observations are already complete over one-third of the survey area. In this paper, we describe the survey design and execution. We present the largest IGM tomographic maps at comparable resolution yet made. We show that the recovered matter overdensities are broadly consistent with cosmological expectations based on realistic mock surveys, that they correspond to galaxy overdensities, and that we can recover structures identified using other tracers. LATIS is conducted in Canada–France–Hawaii Telescope Legacy Survey fields, including COSMOS. Coupling the LATIS tomographic maps with the rich data sets collected in these fields will enable novel studies of environment-dependent galaxy evolution and the galaxy–IGM connection at cosmic noon.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".