Photometric redshifts and clustering of emission line galaxies selected jointly by DES and eBOSS
Bibliographic record
Abstract
We present the results of the first observations of the emission line galaxies (ELG) of the extended Baryon Oscillation Spectroscopic Survey. From the total 9000 targets, 4600 have been selected from the Dark Energy Survey (DES). In this subsample, the total success rate for redshifts between 0.6 and 1.2 is 71 and 68 per cent for a bright and a faint samples, respectively, including redshifts measured from a single strong emission line. The mean redshift is 0.80 for the bright and 0.87 for the faint sample, while the percentage of unknown redshifts is 15 and 13 per cent, respectively. In both cases, the star contamination is lower than 2 per cent. We evaluate how well the ELG redshifts are measured using the target selection photometry and validating with the spectroscopic redshifts measured by eBOSS. We explore different techniques to reduce the photometric redshift outliers fraction with a comparison between the template fitting, the neural networks and the random forest methods. Finally, we study the clustering properties of the DES SVA1 ELG samples. We select only the most secure spectroscopic redshift in the redshift range 0.6 < z < 1.2, leading to a mean redshift for the bright and faint sample of 0.85 and 0.90, respectively. We measure the projected angular correlation function and obtain a galaxy bias averaging on scales from 1 to 10 Mpc h−1 of 1.58 ± 0.10 for the bright sample and 1.65 ± 0.12 for the faint sample. These values are representative of a galaxy population with MB − log(h) < −20.5, in agreement with what we measure by fitting galaxy templates to the photometric data.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".