Photometric selection of high-redshift type Ia supernovae
Bibliographic record
Abstract
We present a method for selecting high-redshift type Ia supernovae (SNe Ia) located via rolling SN searches. The technique, using both color and magnitude information of events from only 2-3 epochs of multi-band real-time photometry, is able to discriminate between SNe Ia and core collapse SNe. Furthermore, for the SNe Ia, the method accurately predicts the redshift, phase and light-curve parameterization of these events based only on pre-maximum-light data. We demonstrate the effectiveness of the technique on a simulated survey of SNe Ia and core-collapse SNe, where the selection method effectively rejects most core-collapse SNe while retaining SNe Ia. We also apply the selection code to real-time data acquired as part of the Canada-France-Hawaii Telescope Supernova Legacy Survey (SNLS). During the period May 2004 to January 2005 in the SNLS, 440 SN candidates were discovered of which 70 were confirmed spectroscopically as SNe Ia and 15 as core-collapse events. For this test dataset, the selection technique correctly identifies 100% of the identified SNe II as non-SNe Ia with only a 1-2% false rejection rate. The predicted parameterization of the SNe Ia has a precision of |delta_z|/(1+z_spec)<0.09 in redshift, and +/- 2-3 rest-frame days in phase, providing invaluable information for planning spectroscopic follow-up observations. We also investigate any bias introduced by this selection method on the ability of surveys such as SNLS to measure cosmological parameters (e.g., w and omega matter), and find any effect to be negligible.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".