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Record W2805527259 · doi:10.1051/0004-6361/201730404

Strong dependence of Type Ia supernova standardization on the local specific star formation rate

2020· article· en· W2805527259 on OpenAlexfundno aff

Bibliographic record

VenueAstronomy and Astrophysics · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGamma-ray bursts and supernovae
Canadian institutionsnot available
FundersLawrence Berkeley National LaboratoryJet Propulsion LaboratoryOffice of ScienceMax-Planck-Institut für AstronomieCentre National de la Recherche ScientifiqueEuropean Southern ObservatoryMax-Planck-GesellschaftBrookhaven National LaboratoryTsinghua UniversityChinese Academy of SciencesAgence Nationale de la RechercheHigh Energy PhysicsDeutsche ForschungsgemeinschaftEuropean CommissionWeizmann Institute of ScienceUniversity of OxfordYork UniversityNational Energy Research Scientific Computing CenterInstitut des Origines de LyonInstitut National de Physique Nucléaire et de Physique des ParticulesUniversität BaselFermilabNational Science FoundationCase Western Reserve UniversityCarnegie Mellon UniversityUniversity of PittsburghUniversity of ArizonaLos Alamos National LaboratoryCollege of Engineering, Michigan State UniversityUniversity of WashingtonAlfred P. Sloan FoundationPrinceton UniversityJohns Hopkins UniversityHarvard UniversityOhio State UniversityNew Mexico State UniversityUniversity of PortsmouthYale UniversityDrexel UniversityVanderbilt UniversityCalifornia Institute of TechnologyU.S. Naval ObservatoryAdvanced Scientific Computing ResearchNational Natural Science Foundation of ChinaU.S. Department of EnergyGordon and Betty Moore FoundationNational Aeronautics and Space Administration
KeywordsSupernovaStar formationNormalization (sociology)Extinction (optical mineralogy)Stellar massBrightnessGalaxyStars

Abstract

fetched live from OpenAlex

As part of an on-going effort to identify, understand and correct for astrophysics biases in the standardization of Type Ia supernovae (SN Ia) for cosmology, we have statistically classified a large sample of nearby SNe Ia into those that are located in predominantly younger or older environments. This classification is based on the specific star formation rate measured within a projected distance of 1 kpc from each SN location (LsSFR). This is an important refinement compared to using the local star formation rate directly, as it provides a normalization for relative numbers of available SN progenitors and is more robust against extinction by dust. We find that the SNe Ia in predominantly younger environments are Δ Y = 0.163 ± 0.029 mag (5.7 σ ) fainter than those in predominantly older environments after conventional light-curve standardization. This is the strongest standardized SN Ia brightness systematic connected to the host-galaxy environment measured to date. The well-established step in standardized brightnesses between SNe Ia in hosts with lower or higher total stellar masses is smaller, at Δ M = 0.119 ± 0.032 mag (4.5 σ ), for the same set of SNe Ia. When fit simultaneously, the environment-age offset remains very significant, with Δ Y = 0.129 ± 0.032 mag (4.0 σ ), while the global stellar mass step is reduced to Δ M = 0.064 ± 0.029 mag (2.2 σ ). Thus, approximately 70% of the variance from the stellar mass step is due to an underlying dependence on environment-based progenitor age. Also, we verify that using the local star formation rate alone is not as powerful as LsSFR at sorting SNe Ia into brighter and fainter subsets. Standardization that only uses the SNe Ia in younger environments reduces the total dispersion from 0.142 ± 0.008 mag to 0.120 ± 0.010 mag. We show that as environment-ages evolve with redshift, a strong bias, especially on the measurement of the derivative of the dark energy equation of state, can develop. Fortunately, data that measure and correct for this effect using our local specific star formation rate indicator, are likely to be available for many next-generation SN Ia cosmology experiments.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.777
Threshold uncertainty score0.617

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.219
Teacher spread0.201 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations152
Published2020
Admission routes1
Has abstractyes

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