Supernova Rates, Rise-Ttmes and their Relations to Progenitors
Bibliographic record
Abstract
Supernovae are fundamental in astronomy: they inject high mass elements into the interstellar medium enriching the chemistry of galaxies, they feed processes of star formation and active galactic nuclei, and they have been a key for the developments in cosmology of the past decades.\n\nThis dissertation presents a set of subluminous type Ia supernovae (SNe Ia) at z>0.1 from the Supernova Legacy Survey (SNLS). These faint and short-lived transients are found in massive and passive host galaxies. We measure a volumetric rate as a function of redshift that is different from the normal SNIa population. The observations point towards a long delay time since the birth of the progenitors systems and argue for progenitor stars of initial low mass.\n\nWe calculate a stretch-corrected rise-time since explosion to maximum brightness for different sets of SNe~Ia. We find that a fiducial 17 day quadratic rise is sufficient to explain all SNe Ia, including subluminous ones, arguing for their homogeneity throughout the entire light-curve. Subluminous SNe Ia are powered by as little as 0.05 solar masses of radioactive nickel synthesized in the explosion. Theoretical models need to explain these challenging weak explosions within the framework of SNe Ia. \n\nFinally, we develop one of the first robust automated techniques to identify plateau supernovae (SNe IIP) in large photometric transient surveys. This simple method was tested with a variety of real and simulated SN samples and proved to be effective across different redshifts. Such a photometric typing will be of great power for coming surveys and will allow numerous scientific studies of SNe IIP.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".