HYDROGEN-POOR SUPERLUMINOUS SUPERNOVAE AND LONG-DURATION GAMMA-RAY BURSTS HAVE SIMILAR HOST GALAXIES
Bibliographic record
Abstract
We present optical spectroscopy and optical/near-IR photometry of 31 host galaxies of hydrogen-poor superluminous supernovae (SLSNe), including 15 events from the Pan-STARRS1 Medium Deep Survey. Our sample spans the redshift range 0.1 ≲ z ≲ 1.6, and is the first comprehensive host galaxy study of this specific subclass of cosmic explosions. Combining the multi-band photometry and emission-line measurements, we determine the luminosities, stellar masses, star formation rates, and metallicities. We find that, as a whole, the hosts of SLSNe are a low-luminosity (〈 M B 〉 ≈ −17.3 mag), low stellar mass (〈 M * 〉 ≈ 2 × 10 8 M ☉ ) population, with a high median specific star formation rate (〈sSFR〉 ≈ 2 Gyr −1 ). The median metallicity of our spectroscopic sample is low, 12 + log (O/H) ≈ 8.35 ≈ 0.45 Z ☉ , although at least one host galaxy has solar metallicity. The host galaxies of H-poor SLSNe are statistically distinct from the hosts of GOODS core-collapse SNe (which cover a similar redshift range), but resemble the host galaxies of long-duration gamma-ray bursts (LGRBs) in terms of stellar mass, SFR, sSFR, and metallicity. This result indicates that the environmental causes leading to massive stars forming either SLSNe or LGRBs are similar, and in particular that SLSNe are more effectively formed in low metallicity environments. We speculate that the key ingredient is large core angular momentum, leading to a rapidly spinning magnetar in SLSNe and an accreting black hole in LGRBs.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".