HINOTORI I: The Nature of Rejuvenation Galaxies
Bibliographic record
Abstract
We present the HINOTORI (star formation History INvestigatiOn TO find RejuvenatIon) project to reveal the nature of rejuvenation galaxies (RGs), which are galaxies that restarted their star formation after being quiescent. As the first step of HINOTORI, we construct the largest RG sample with 1071 sources. We select these RGs from 8857 MaNGA (Mapping Nearby Galaxies at APO) survey galaxies by reconstructing their star formation histories with Prospector spectral energy distribution fitting code. Both optical spectral data and UV to IR photometric data are used for the fitting. Using mock data, we confirm that our method can detect weak rejuvenation events that form only about 0.1% of the total stellar mass with high completeness. The RGs account for ~10% of the whole sample, and rejuvenation events contribute on average only about 0.1% of the total stellar mass in those galaxies but 17% of the cosmic-star formation rate density today. Our RGs have a similar mass distribution to quiescent galaxies (QGs). However, the morphology of the RGs is more disk-like than QGs, suggesting that rejuvenation may occur selectively in disk-like QGs. Our results also suggest the possibility of multiple-time rejuvenation events in a single galaxy. Further spatially resolved analyses of integral field unit data and radio observations and comparisons to simulations are needed to identify the mechanism and the role of rejuvenation in galaxy evolution.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".