The DEEP2 Galaxy Redshift Survey: environments of post-starburst galaxies at<i>z</i>∼ 0.1 and ∼0.8
Bibliographic record
Abstract
Post-starburst (also known as K+A) galaxies exhibit spectroscopic signatures indicating that their star formation was recently quenched; they are candidates for galaxies in transition from a star-forming phase to a passively evolving phase. We have spectroscopically identified large samples of post-starburst galaxies both in the Sloan Digital Sky Survey (SDSS) at z∼ 0.1 and in the DEEP2 Galaxy Redshift Survey at z∼ 0.8, using a uniform and robust selection method based on a cut in Hβ line emission rather than the more problematic [O ii]λ3727. Based on measurements of the overdensity of galaxies around each object, we find that post-starburst galaxies brighter than 0.4L*B at low redshift have a similar, statistically indistinguishable environment distribution as blue galaxies, preferring underdense environments, but dramatically different from that of red galaxies. However, at higher-z, the environment distribution of post-starburst galaxies is more similar to red galaxies than to blue galaxies. We conclude that the quenching of star formation and the build-up of the red sequence through the K+A phase is happening in relatively overdense environments at z∼ 1 but in relatively underdense environments at z∼ 0. Although the relative environments where quenching occurs are decreasing with time, the corresponding absolute environment may have stayed the same along with the quenching mechanisms, because the mean absolute environments of all galaxies has to grow with time. In addition, we do not find any significant dependence on luminosity in the environment distribution of K+As. The existence of a large K+A population in the field at both redshifts indicates that cluster-specific mechanisms cannot be the dominant route by which these galaxies are formed. Our work also demonstrates that studying post-starburst–environment relations by measuring the K+A fraction in different environments, as is the common practice, is highly non-robust; modest changes in the comparison population used to define the fraction can drastically alter conclusions. Statistical comparisons of the overall environment distributions of different populations are much better behaved.
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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".