ZTF SN Ia DR2: Environmental dependencies of stretch and luminosity for a volume-limited sample of 1000 type Ia supernovae
Bibliographic record
Abstract
Context. Type Ia supernova (SN Ia) cosmology studies will soon be dominated by systematic, uncertainties, rather than statistical ones. Thus, it is crucial to understand the unknown phenomena potentially affecting their luminosity that may remain, such as astrophysical biases. For their accurate application in such studies, SN Ia magnitudes need to be standardised; namely, they must be corrected for their correlation with the light-curve width and colour. Aims. Here, we investigate how the standardisation procedure used to reduce the scatter of SN Ia luminosities is affected by their environment. Our aim is to reduce scatter and improve the standardisation process. Methods. We first studied the SN Ia stretch distribution, as well as its dependence on environment, as characterised by local and global (g − z) colour and stellar mass. We then looked at the standardisation parameter, α, which accounts for the correlation between residuals and stretch, along with its environment dependency and linearity. Finally, we computed the magnitude offsets between SNe in different astrophysical environments after the colour and stretch standardisations (i.e. steps). This analysis has been made possible thanks to the unprecedented statistics of the volume-limited Zwicky Transient Facility (ZTF) SN Ia DR2 sample. Results. The stretch distribution exhibits a bimodal behaviour, as previously found in the literature. However, we find the distribution to be dependent on environment. Specifically, the mean stretch modes decrease with host stellar mass, at a 9.2σ significance. We demonstrate, at the 13.4σ level, that the stretch-magnitude relation is non-linear, challenging the usual linear stretch-residuals relation currently used in cosmological analyses. In fitting for a broken-α model, we did indeed find two different slopes between stretch regimes (x1 ≶ x10 with x10 = −0.48 ± 0.08): αlow = 0.271 ± 0.011 and αhigh = 0.083 ± 0.009, comprising a difference of Δα = −0.188 ± 0.014. As the relative proportion of SNe Ia in the high-stretch and low-stretch modes evolves with redshift and environment, this implies that a single-fitted α also evolves with the redshift and environment. Concerning the environmental magnitude offset γ, we find it to be greater than 0.12 mag, regardless of the considered environmental tracer used (local or global colour and stellar mass), all measured at the ≥5σ level. When accounting for the non-linearity of the stretch, these steps increase to ∼0.17 mag, measured with a precision of 0.01 mag. Such strong results highlight the importance of using a large volume-limited dataset to probe the underlying SN Ia-host correlations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".