<tt>E-INSPIRE</tt> – I. Bridging the gap with the local Universe: stellar population of a statistical sample of ultra-compact massive galaxies at <i>z</i> &lt; 0.3
Bibliographic record
Abstract
ABSTRACT This paper presents the first effort to Extend the Investigation of Stellar Populations In RElics (E-INSPIRE). We present a catalogue of 430 spectroscopically confirmed ultra-compact massive galaxies (UCMGs) from the Sloan Digital Sky Survey at redshifts $0.01< z< 0.3$. This increases the original INSPIRE sample eightfold, bridging the gap with the local Universe. For each object, we compute integrated stellar velocity dispersion, age, metallicity, and [Mg/Fe] through spectroscopic stellar population analysis. We infer star formation histories (SFHs), metallicity evolution histories (MEHs) and compute the Degree of Relicness (DoR) of each object. The UCMGs, covering a wide range of DoR from 0.05 to 0.88, can be divided into three groups, according to how extreme their SFH was. The first group consists of 81 extreme relics ($\text{DoR}\gtrsim 0.6$) that have formed the totality of their stellar mass by $z\sim 2$ and have super-solar metallicities at all cosmic epochs. The second group ($0.3\lesssim \text{DoR}\lesssim 0.6$) contains 293 objects also characterized by peaked SFHs but with a small percentage of later-formed stars and with a variety of MEHs. The third group ($\text{DoR}\lesssim 0.3$), has 56 objects that cannot be considered relics since they have extended SFHs and formed a non-negligible fraction (${>}25$ per cent) of their stellar mass at $z< 2$. We conclude that the most efficient method of finding relics is to select UCMGs with a combination of large velocity dispersion values (as already found by INSPIRE), super-solar metallicities and high [Mg/Fe].
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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 | 0.001 | 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 | 0.001 |
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".