Exploring Type Ia Supernova Systematics: the Host Galaxy Bias and Intrinsic Variability
Bibliographic record
Abstract
Type Ia supernovae are bright transient events with similar peak brightness. Once calibrated and standardized, type Ia supernova samples become powerful cosmological probes, especially for measuring dark energy and the universe’s accelerating expansion. Rising tensions between independent measurements in an era of precision cosmology underscores the importance of accounting for systematic errors in analyses. This is particularly true for type Ia supernova cosmology, where observations are fit to empirical models in place of elusive theoretical alternatives. Additional concerning systematics include those arising from redshift dependence of the underlying supernova population. This dissertation explores two topics in type Ia supernova systematics. The established type Ia supernova host galaxy bias, where intrinsically brighter type Ia supernovae prefer less massive, younger hosts, alongside its potential dependence on observation methods and fitting techniques, is studied in chapter two. Various host galaxy stellar mass and specific star formation rates samples are estimated from photometry or spectroscopy using different galaxy property fitting software, from which different estimates of the host bias are calculated and then compared. No evidence is found that the choice in method or technique influences the host bias, let alone being the source of it. The dissertation’s third chapter introduces a new physics-agnostic empirical model which provides more detailed exploration of phase-independent flux variation than that afforded by ubiquitous comparable models, such as SALT2. It is demonstrated that there is sufficient signal-to-noise in available data sets to constrain models beyond the commonly used two parameter empirical model. The results also indicate that intrinsic flux variation can be misidentified as dust-like, highlighting the difficulty estimated dust properties of type Ia supernovae. For the second project, more work is needed to better separate dust-like flux variation from intrinsic variability, and to analyze the model’s standardization performance for cosmology applications. Both topics studied advance our understanding of supernova cosmology systematics while stressing nuance in exploring sources of and solutions to these errors.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".