Exploring the role of globular cluster specific frequency on the nova rates in three virgo elliptical galaxies
Bibliographic record
Abstract
It has been proposed that a galaxy's nova rate might be enhanced by the production of nova progenitor binaries in the dense cores of its globular clusters (GCs). To explore this idea, relative nova rates in three Virgo elliptical galaxies, M87, M49, and M84, which have significantly different GC specific frequencies (SN) of 14, 3.6, and 1.6, respectively, were measured over the course of 4 epochs spanning a period of 14 months. To simplify the analysis, observations of the nearly equidistant galaxies were made on the same nights, with the same integration times, and through the same filter (Hα), so that the relative numbers of novae discovered would reflect the relative nova rates. At the conclusion of our survey we found a total of 27 novae associated with M87, 37 with M49, and 19 with M84. After correcting for survey completeness, we found annual nova rates of , , and , for M87, M49, and M84, respectively, corresponding to K-band luminosity-specific nova rates of 3.8 ± 1.0, 3.4 ± 0.6, and 3.0 ± 0.6 novae per year per . The overall results of our study suggest that a galaxy's nova rate simply scales with its luminosity, and is insensitive to its GC specific frequency. Two novae, one in M87 and one in M84, were found to be spatially coincident with known GCs. After correcting for the mass fraction in GCs, we estimate that novae are likely enhanced relative to the field by at least an order of magnitude in the GC systems of luminous Virgo ellipticals.
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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) | 0.000 | 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".