On the cross-correlation of sub-mm sources and optically selected galaxies
Bibliographic record
Abstract
Bright submillimetre (sub-mm) galaxies are expected to arise in massive highly biased haloes, and hence exhibit strong clustering. We argue that a valuable tool for measuring these clustering properties is the cross-correlation of sub-mm galaxies with faint optically selected sources. We analyse populations of sub-mm detected and optical galaxies in the Great Observatories Origins Deep Survey-North (GOODS-N) survey area. Using optical/infrared photometric-redshift information, we search for correlations induced by two separate effects: (i) cosmic magnification of background sub-mm sources by foreground dark matter haloes traced by optical galaxies at lower redshifts and (ii) galaxy clustering due to sub-mm and optical sources tracing the same population of haloes where their redshift distributions overlap. Regarding cosmic magnification, we find no detectable correlation. Our null result is consistent with a theoretical model for the cosmic magnification, and we show that a dramatic increase in the number of sub-mm sources will be required to measure the effect reliably. Regarding clustering, we find evidence at the 3.5σ level for a cross-correlation between sub-mm and optical galaxies analysed in identical photometric redshift slices. The data hint that the sub-mm sources have an enhanced bias parameter compared to the optically selected population (with a significance of 2σ). The next generation of deep sub-mm surveys can potentially perform an accurate measurement of each of these cross-correlations, adding a new set of diagnostics for understanding the development of massive structure in 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".