ZTF SN Ia DR2: Simulations and volume-limited sample
Bibliographic record
Abstract
Type Ia supernovae (SNe Ia) constitute a historical probe for deriving cosmological parameters through the fit of the Hubble-Lemaître diagram, that is, the SN Ia distance modulus versus their redshift. In the era of precision cosmology, realistic simulation of SNe Ia for any survey entering an Hubble-Lemaître diagram is a key tool for addressing observational systematics, such as the Malmquist bias. As the distance modulus of SNe Ia is derived from the fit of their light curves, a robust simulation framework is required. In this paper, we present the performances of the simulation framework skysurvey with the aim to reproduce the Zwicky Transient Facility (ZTF) SN Ia DR2, which covers the first phase of the ZTF and ran from March 2018 to December 2020. The ZTF SN Ia DR2 sample corresponds to almost 3000 classified SNe Ia of cosmological quality. We simulated individual light curves of the ZTF SN Ia DR2 sample to confirm the validity of the framework while taking the observing conditions and instrument performances into account. After the ZTF SN Ia DR2 selection criteria were applied, we found that the simulated fluxes and associated uncertainties agre well with the measured uncertainties when the sky-noise deduced from the observed science magnitude limits is corrected for by a factor 1.23 for the g band, 1.17 for the r band, and 1.20 for the i band. In addition, we accounted for an error floor of 2.5%, 3.5%, and 6% of the flux level in the g, r, and i bands, respectively. Furthermore a redshift dependence of the SALT2 light-curve parameters (stretch and colour) was conducted to deduce the redshift limit that defines a volume-limited sample, that is, an unbiased SNe Ia sample. We found that the ZTF SN Ia DR2 volume-limited sample is characterized by z ≤ 0.06. This volume-limited sample of about 1000 SNe Ia is unique, and an astrophysical analysis can be carried out based on it, or the standardisation procedure can be tested with unprecedented precision (these analyses are presented in companion papers).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".