Detailed spectrophotometric analysis of the superluminous and fast evolving SN 2019neq
Bibliographic record
Abstract
ABSTRACT SN 2019neq was a very fast evolving superluminous supernova. At a redshift z = 0.1059, its peak absolute magnitude was −21.5 ± 0.2 mag in g band. In this work, we present data and analysis from an extensive spectrophotometric follow-up campaign using multiple observational facilities. Thanks to a nebular spectrum of SN 2019neq, we investigated some of the properties of the host galaxy at the location of SN 2019neq and found that its metallicity and specific star formation rate are in a good agreement with those usually measured for SLSNe-I hosts. We then discuss the plausibility of the magnetar and the circumstellar interaction scenarios to explain the observed light curves, and interpret a nebular spectrum of SN 2019neq using published sumo radiative-transfer models. The results of our analysis suggest that the spin-down radiation of a millisecond magnetar with a magnetic field $B\simeq 6\times 10^{14}\, \mathrm{G}$ could boost the luminosity of SN 2019neq.
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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 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".