Types of Transients in the Centers of Post-Starburst and Quiescent Balmer-Strong Galaxies
Bibliographic record
Abstract
Tidal Disruption Events (TDEs) have been found to show a preference for post-starburst (PS) and quiescent Balmer-strong (QBS) galaxies. This preference can be used to help find TDEs in transient surveys. But what other transients might "contaminate" such a search, and by how much? We examine all reported transients coincident with the centers of galaxies in the French & Zabludoff (2018) catalog of spectroscopically confirmed PS and QBS galaxies and photometrically identified PS and QBS galaxy candidates. We find that TDEs and Type Ia supernovae (SNe) are the only types of transients classified in the centers of these galaxies (aside from one active galactic nucleus flare), with Type Ia SNe being 8.3+-0.2 times more prevalent than TDEs (1-sigma confidence bounds). This factor is ~2.7 times lower than in a control sample of quiescent galaxies. Narrowing the sample to spectroscopically confirmed QBS galaxies does not change these statistics much. In spectroscopically confirmed PS galaxies, however, TDEs are the ones that outnumber Type Ia SNe 2+-0.6 to 1. Unfortunately, there are few such galaxies in the catalog. By classifying transients from the entire catalog, three times more TDEs are expected to be found, but with a ~16-times larger Type Ia SN contamination. We use the public ZTF photometric archive to search for possibly missed TDEs in the French & Zabludoff (2018) galaxies. We find three unclassified clear transients -- none of which are likely missed TDEs based on their light-curve colors.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".