HerMES: A STATISTICAL MEASUREMENT OF THE REDSHIFT DISTRIBUTION OF HERSCHEL-SPIRE SOURCES USING THE CROSS-CORRELATION TECHNIQUE
Bibliographic record
Abstract
The wide-area imaging surveys with the Herschel Space Observatory at submillimeter (sub-mm) wavelengths have now resulted in catalogs of the order of one-hundred-thousand dusty, starburst galaxies. These galaxies capture an important phase of galaxy formation and evolution, but, unfortunately, the redshift distribution of these galaxies, N ( z ), is still mostly uncertain due to limitations associated with counterpart identification at optical wavelengths and spectroscopic follow-up. We make a statistical estimate of N ( z ) using a clustering analysis of sub-mm galaxies detected at each of 250, 350 and 500 μm from the Herschel Multi-tiered Extragalactic Survey centered on the Boötes field. We cross-correlate Herschel galaxies against galaxy samples at optical and near-IR wavelengths from the Sloan Digital Sky Survey, the NOAO Deep Wide Field Survey, and the Spitzer Deep Wide Field Survey. We create optical and near-IR galaxy samples based on their photometric or spectroscopic redshift distributions and test the accuracy of those redshift distributions with similar galaxy samples defined with catalogs from the Cosmological Evolution Survey (COSMOS), which has superior spectroscopic coverage. We model the clustering auto- and cross-correlations of Herschel and optical/IR galaxy samples to estimate N ( z ) and clustering bias factors. The S 350 > 20 mJy galaxies have a bias factor varying with redshift as b ( z ) = 1.0 +1.0 − 0.5 (1 + z ) 1.2 +0.3 − 0.7 . This bias and the redshift dependence is broadly in agreement with galaxies that occupy dark matter halos of mass in the range of 10 12 to 10 13 M ☉ . We find that galaxy selections in all three Spectral and Photometric Imaging Receiver (SPIRE) bands share a similar average redshift, with 〈 z 〉 = 1.8 ± 0.2 for 250 μm selected samples, and 〈 z 〉 = 1.9 ± 0.2 for both 350 and 500 μm samples, while their distributions behave differently. For 250 μm selected galaxies we find the a larger number of sources with z ⩽ 1 when compared with the subsequent two SPIRE bands, with 350 and 500 μm selected SPIRE samples having peaks in N ( z ) at progressively higher redshifts. We compare our clustering-based N ( z ) results to sub-mm galaxy model predictions in the literature, and with an estimate of N ( z ) using a stacking analysis of COSMOS 24 μm detections.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".