Type Ic supernovae from the (intermediate) Palomar Transient Factory
Bibliographic record
Abstract
Context. Type Ic supernovae represent the explosions of the most stripped massive stars, but their progenitors and explosion mechanisms remain unclear. Larger samples of observed supernovae can help characterize the population of these transients. Aims. We present an analysis of 44 spectroscopically normal Type Ic supernovae, with focus on the light curves. The photometric data were obtained over 7 years with the Palomar Transient Factory and its continuation, the intermediate Palomar Transient Factory. This is the first homogeneous and large sample of SNe Ic from an untargeted survey, and we aim to estimate explosion parameters for the sample. Methods. We present K-corrected Bgriz light curves of these SNe, obtained through photometry on template-subtracted images. We performed an analysis on the shape of the r -band light curves and confirmed the correlation between the rise parameter Δ m −10 and the decline parameter Δ m 15 . Peak r -band absolute magnitudes have an average of −17.71 ± 0.85 mag. To derive the explosion epochs, we fit the r -band lightcurves to a template derived from a well-sampled light curve. We computed the bolometric light curves using r and g band data, g − r colors and bolometric corrections. Bolometric light curves and Fe II λ 5169 velocities at peak were used to fit to the Arnett semianalytic model in order to estimate the ejecta mass M ej , the explosion energy E K and the mass of radioactive nickel M ( 56 Ni) for each SN. Results. Including 41 SNe, we find average values of ⟨ M ej ⟩ = 4.50 ± 0.79 M ⊙ , ⟨ E K ⟩ = 1.79 ± 0.29 × 10 51 erg, and ⟨ M 56 Ni ⟩ = 0.19 ± 0.03 M ⊙ . The explosion-parameter distributions are comparable to those available in the literature, but our large sample also includes some transients with narrow and very broad light curves leading to more extreme ejecta masses values.
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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 | 0.001 |
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".