Jets in Hydrogen-poor Superluminous Supernovae: Constraints from a Comprehensive Analysis of Radio Observations
Bibliographic record
Abstract
Abstract The energy source powering the extreme optical luminosity of hydrogen-stripped superluminous supernovae (SLSNe-I) is not known, but recent studies have highlighted the case for a central engine. Radio and/or X-ray observations are best placed to track the fastest ejecta and probe the presence of outflows from a central engine. We compile all the published radio observations of SLSNe-I to date and present three new observations of two new SLSNe-I. None were detected. Through modeling the radio emission, we constrain the subparsec environments and possible outflows in SLSNe-I. In this sample, we rule out on-axis collimated relativistic jets of the kind detected in gamma-ray bursts (GRBs). We constrain off-axis jets with opening angles of 5° (30°) to energies of E k < 4 × 10 50 erg ( E k < 10 50 erg ) in environments shaped by progenitors with mass-loss rates of M ˙ < 10 − 4 M ⊙ yr − 1 ( M ˙ < 10 − 5 M ⊙ yr − 1 ) for all off-axis angles, assuming fiducial values ϵ e = 0.1 and ϵ B = 0.01 . The deepest limits rule out emission of the kind seen in faint uncollimated GRBs (with the exception of GRB 060218) and from relativistic SNe. Finally, for the closest SLSN-I, SN 2017egm, we constrain the energy of an uncollimated nonrelativistic outflow like those observed in normal SNe to E k ≲ 10
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.001 | 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".