Photometric selection of Type Ia supernovae in the Supernova Legacy Survey
Bibliographic record
Abstract
We present a sample of 485 photometrically identified Type Ia supernova candidates mined from the first three years of data of the CFHT SuperNova Legacy Survey (SNLS). The images were submitted to a deferred processing independent of the SNLS real-time detection pipeline. Light curves of all transient events were reconstructed in the gM, rM, iM and zM filters and submitted to automated sequential cuts in order to identify possible supernovae. Pure noise and long-term variable events were rejected by light curve shape criteria. Type Ia supernova identification relied on event characteristics fitted to their light curves assuming the events to be normal SNe Ia. The light curve fitter SALT2 was used for this purpose, assigning host galaxy photometric redshifts to the tested events. The selected sample of 485 candidates is one magnitude deeper than that allowed by the SNLS spectroscopic identification. The contamination by supernovae of other types is estimated to be 4%. Testing Hubble diagram residuals with this enlarged sample allows us to measure the Malmquist bias due to spectroscopic selections directly. The result is fully consistent with the precise Monte Carlo based estimate used to correct SN Ia distance moduli in the SNLS 3-year cosmological analyses. This paper demonstrates the feasibility of a photometric selection of high redshift supernovae with known host galaxy redshifts, opening interesting prospects for cosmological analyses from future large photometric SN Ia surveys.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".