The Spatial Clustering of Ultraluminous Infrared Galaxies over 1.5 < <i>z</i> < 3
Bibliographic record
Abstract
We present measurements of the spatial clustering of galaxies with stellar masses ≳10 11 M ☉ , infrared luminosities ≳10 12 L ☉ , and star formation rates ≳200 M ☉ yr -1 in two redshift intervals: 1.5 < z < 2.0 and 2 < z < 3. Both samples cluster moderately strongly, with spatial correlation lengths of r 0 = 6.14 ± 0.84 h -1 Mpc for the 2 < z < 3 sample and r 0 = 5.36 ± 1.28 h -1 Mpc for the 1.5 < z < 2.0 sample. These clustering amplitudes are consistent with both populations residing in dark matter halos with masses of ~7 × 10 12 M ☉ , which is comparable to that seen for optical QSOs at the same epochs. We infer that a minimum dark matter halo mass is an important factor for all forms of luminous, obscured activity in galaxies at z > 1, both starbursts and active galactic nuclei. Adopting plausible models for the growth of dark matter halos with redshift, the halos hosting the 2 < z < 3 sample will likely host poor to rich clusters of galaxies at z = 0, whereas the halos hosting the 1.5 < z < 2.0 sample will likely host ≳ L * elliptical galaxies or poor clusters at z = 0. We conclude that ultraluminous infrared galaxies (ULIRGs) at z ~ 2.5 likely signpost stellar buildup in galaxies that will reside in clusters at z = 0 and that ULIRGs at z ~ 1.7 signpost stellar buildup in sources that will either become ≳ L * elliptical galaxies or reside in poor clusters at z = 0.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".