SN 2017gci: a nearby Type I Superluminous Supernova with a bumpy tail
Bibliographic record
Abstract
We present and discuss the optical spectrophotometric observations of the nearby (z = 0.087) Type I superluminous supernova (SLSN I) SN 2017gci, whose peak K-corrected absolute magnitude reaches M-g = -21.5 mag. Its photometric and spectroscopic evolution includes features of both slow- and of fast-evolving SLSN I, thus favoring a continuum distribution between the two SLSN-I subclasses. In particular, similarly to other SLSNe I, the multiband light curves (LCs) of SN 2017gci show two re-brightenings at about 103 and 142 d after the maximum light. Interestingly, this broadly agrees with a broad emission feature emerging around 6520 angstrom after similar to 51 d from the maximum light, which is followed by a sharp knee in the LC. If we interpret this feature as H alpha, this could support the fact that the bumps are the signature of late interactions of the ejecta with a (hydrogen-rich) circumstellar material. Then we fitted magnetar- and CSM-interaction-powered synthetic LCs on to the bolometric one of SN 2017gci. In the magnetar case, the fit suggests a polar magnetic field B-p similar or equal to 6 x 10(14) G, an initial period of the magnetar P-initial similar or equal to 2.8 ms, an ejecta mass M-ejecta similar or equal to 9M(circle dot) and an ejecta opacity kappa similar or equal to 0.08 cm(2) g(-1). A CSM-interaction scenario would imply a CSM mass similar or equal to 5 M-circle dot and an ejecta mass similar or equal to 12M(circle dot). Finally, the nebular spectrum of phase + 187 d was modeled, deriving a mass of similar or equal to 10 M-circle dot for the ejecta. Our models suggest that either a magnetar or CSM interaction might be the power sources for SN 2017gci and that its progenitor was a massive (40 M-circle dot) star.
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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 | 0.000 | 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".