The Type I superluminous supernova catalogue – II. Spectroscopic evolution in the photospheric phase, velocity measurements, and constraints on diversity
Bibliographic record
Abstract
ABSTRACT Hydrogen-poor superluminous supernovae (SLSNe) are among the most energetic explosions in the universe, reaching luminosities up to 100 times greater than those of normal supernovae. This paper presents the largest compilation of SLSN photospheric spectra to date, encompassing data from the advanced Public ESO Spectroscopic Survey of Transient Objects (ePESSTO+), the Finding Luminous and Exotic Extragalactic Transients (FLEET) search, and all published spectra up to December 2022. The data set includes a total of 974 spectra of 234 SLSNe. By constructing average phase binned spectra, we find SLSNe initially exhibit high temperatures (10 000–11 000 K), with blue continua and weak lines. A rapid transformation follows, as temperatures drop to 5000–6000 K by 40 d post-peak, leading to stronger P-Cygni features. Variance within the data set is slightly reduced when defining the phase of spectra relative to explosion, rather than peak, and normalising to the population’s median e-folding decline time. Principal Component Analysis (PCA) supports this, requiring fewer components to explain the same level of variation when binning data by scaled days from explosion, suggesting a more homogeneous grouping. Using PCA and K-means clustering, we identify outlying objects with unusual spectroscopic evolution and evidence for energy input from interaction, but find no support for groupings of two or more statistically significant subpopulations. We find Fe ii $\lambda$5169 line velocities closely track the radius implied from blackbody fits, indicating formation near the photosphere. We also confirm a correlation between velocity and velocity gradient, which can be explained if all SLSNe are in homologous expansion but with different scale velocities. This behaviour aligns with expectations for an internal powering mechanism.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".