ZTF SN Ia DR2: Overview
Bibliographic record
Abstract
We present the first homogeneous release of several thousand spectroscopically classified type Ia supernovae (SNe Ia) with spectroscopic redshifts. This release, named “DR2”, contains 3628 nearby ( z < 0.3) SNe Ia discovered, followed, and classified by the Zwicky Transient Facility survey between March 2018 and December 2020. Of these, 3000 have good-to-excellent sampling and 2667 pass standard cosmology light curve quality cuts. This release is thus the largest SN Ia release to date, increasing by an order of magnitude the number of well-characterized low-redshift objects. With DR2, we also provide a volume-limited ( z < 0.06) sample of nearly a thousand SNe Ia. With such a large, homogeneous, and well-controlled dataset, we are studying key current questions on SN cosmology, such as the linearity SNe Ia standardization, the SN and host dependencies, the diversity of the SN Ia population, and the accuracy of current light curve modeling. These, and more, are studied in detail in a series of articles associated with this release. Alongside the SN Ia parameters, we publish our forced-photometry gri -band light curves, 5138 spectra, local and global host properties, observing logs, and a Python tool to facilitate the use and access of these data. The photometric accuracy of DR2 is not yet suited for cosmological parameter inference, which will follow as the “DR2.5” release. We nonetheless demonstrate that our Hubble diagram of several thousands of SNe Ia has a typical 0.15 mag scatter.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.048 | 0.072 |
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".