USING THE 1.6 μm BUMP TO STUDY REST-FRAME NEAR-INFRARED-SELECTED GALAXIES AT REDSHIFT 2
Bibliographic record
Abstract
We explore the feasibility and limitations of using the 1.6 μm bump as a photometric redshift indicator and selection technique, and use it to study the rest-frame H -band galaxy luminosity and stellar mass functions (SMFs) at redshift z ∼ 2. We use publicly available Spitzer /IRAC images in the GOODS fields and find that color selection in the IRAC bandpasses alone is comparable in completeness and contamination to BzK selection. We find that the shape of the 1.6 μm bump is robust, and photometric redshifts are not greatly affected by choice of model parameters. Comparison with spectroscopic redshifts shows photometric redshifts to be reliable. We create a rest-frame NIR-selected catalog of galaxies at z ∼ 2 and construct a galaxy SMF. Comparisons with other SMFs at approximately the same redshift but determined using shorter wavelengths show good agreement. This agreement suggests that selection at bluer wavelengths does not miss a significant amount of stellar mass in passive galaxies. Comparison with SMFs at other redshifts shows evidence for the downsizing scenario of galaxy evolution. We conclude by pointing out the potential for using the 1.6 μm bump technique to select high-redshift galaxies with the JWST , whose λ>0.6 μm coverage will not be well suited to selecting galaxies using techniques that require imaging at shorter wavelengths.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".