Constraints on high-energy neutrino emission from SN 2008D
Bibliographic record
Abstract
SN 2008D, a core collapse supernova at a distance of 27 Mpc, was serendipitously discovered by the Swift satellite through an associated X-ray flash. Core collapse supernovae have been observed in association with long gamma-ray bursts and X-ray flashes and a physical connection is widely assumed. This connection could imply that some core collapse supernovae possess mildly relativistic jets in which high-energy neutrinos are produced through proton-proton collisions. The predicted neutrino spectra would be detectable by Cherenkov neutrino detectors like IceCube. A search for a neutrino signal in temporal and spatial correlation with the observed X-ray flash of SN 2008D was conducted using data taken in 2007–2008 with 22 strings of the IceCube detector. Events were selected based on a boosted decision tree classifier trained with simulated signal and experimental background data. The classifier was optimized to the position and a “soft jet” neutrino spectrum assumed for SN 2008D. Using three search windows placed around the X-ray peak, emission time scales from 100–10 000 s were probed. No events passing the cuts were observed in agreement with the signal expectation of 0.13 events. Upper limits on the muon neutrino flux from core collapse supernovae were derived for different emission time scales and the principal model parameters were constrained. While no meaningful limits can be given in the case of an isotropic neutrino emission, the parameter space for a jetted emission can be constrained. Future analyses with the full 86 string IceCube detector could detect up to ~100 events for a core-collapse supernova at 10 Mpc according to the soft jet model.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".