A galaxy-driven model of type Ia supernova luminosity variations

Wiseman, P.; Vincenzi, M.; Sullivan, M.; Kelsey, L.; Popovic, Brodie; Rose, Benjamin; Brout, D.; Davis, Tamara M.; Frohmaier, C.; Galbany, L.; Lidman, C.; Möller, A.; Scolnic, Dan; Smith, M.; Aguena, M.; Allam, S.; Andrade-Oliveira, F.; Annis, J.; Bertin, E.; Bocquet, S.; Burke, D. L.; Rosell, A. Carnero; Kind, M. Carrasco; Carretero, J.; Castander, F. J.; Costanzi, M.; Pereira, M. E. S.; Desai, S.; Diehl, H. T.; Doel, P.; Everett, S.; Ferrero, I.; Friedel, D. N.; Frieman, Joshua A.; García-Bellido, J.; Gatti, M.; Gaztañaga, E.; Gruen, D.; Gschwend, J.; Gutiérrez, G.; Hinton, S. R.; Hollowood, D. L.; Honscheid, K.; James, D. J.; March, M.; Menanteau, F.; Miquel, R.; Morgan, R.; Palmese, A.; Paz-Chinchón, F.; Pieres, A.; Malagón, A. A. Plazas; Romer, A. K.; Sánchez, E.; Scarpine, V.; Sevilla-Noarbe, I.; Soares-Santos, M.; Suchyta, E.; Tarlè, G.; To, C.; Varga, T. N.

doi:10.1093/mnras/stac1984

Cited by 19 publications

(6 citation statements)

References 66 publications

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“…On the other hand, Johansson et al (2021) found a mass step that decreased in the NIR (particularly the JH bands), and that disappeared when R V was included as a free parameter in the SN Ia distance fitting, although it is possible that fitting for R V could have added noise to the distances. Additional evidence for scatter in redder SNe Ia was seen by Rose et al (2022), and evidence for correlations between host-galaxy R V variation and Hubble residuals was seen recently by Meldorf et al (2023), Kelsey et al (2023), and Wiseman et al (2022), though in these studies either a small mass step or a host-galaxy colordependent step remained after modeling the host mass-host R V relation.…”

Section: Introductionmentioning

confidence: 61%

A Spectroscopic Model of the Type Ia Supernova–Host-galaxy Mass Correlation from SALT3

Jones

Kenworthy

Dai

et al. 2023

ApJ

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The unknown cause of the correlation between Type Ia supernova (SN Ia) Hubble residuals and their host-galaxy masses (the “mass step”) may bias cosmological parameter measurements. To better understand the mass step, we develop a SALT3 light-curve model for SN cosmology that uses the host-galaxy masses of 296 low-redshift SNe Ia to derive a spectral energy distribution–host-galaxy mass relationship. The resulting model has larger Ca ii H and K, Ca ii near-infrared triplet, and Si ii equivalent widths for SNe in low-mass host galaxies at 2.2–2.7σ significance; this indicates higher explosion energies per unit mass in low-mass-hosted SNe. The model has phase-dependent changes in SN Ia colors as a function of host mass, indicating intrinsic differences in mean broadband light curves. Although the model provides a better fit to the SN data overall, it does not substantially reduce data–model residuals for a typical light curve in our sample nor does it significantly reduce Hubble residual dispersion. This is because we find that previous SALT models parameterized most host-galaxy dependencies with their first principal component, although they failed to model some significant spectral variations. Our new model is luminosity and cosmology independent, and applying it to data reduces the mass step by 0.021 ± 0.002 mag (uncertainty accounts for correlated data sets); these results indicate that ∼35% of the mass step can be attributed to luminosity-independent effects. This SALT model version could be trained using alternative host-galaxy properties and at different redshifts, and therefore will be a tool for understanding redshift-dependent correlations between SNe Ia and their host properties as well as their impact on cosmological parameter measurements.

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Section: Introductionmentioning

confidence: 61%

A Spectroscopic Model of the Type Ia Supernova–Host-galaxy Mass Correlation from SALT3

Jones

Kenworthy

Dai

et al. 2023

ApJ

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“…The common host properties include both the global host galaxy properties, e.g., host galaxy stellar mass, and the siblings' mean local host galaxy properties, e.g., mean SFR, mean metallicity, mean stellar age, etc. The reported correlations of SN Ia Hubble residuals with global and/or local host galaxy properties (Sullivan et al 2003(Sullivan et al , 2010Kelly et al 2010;D'Andrea et al 2011;Childress et al 2013;Rigault et al 2013Rigault et al , 2020Pan et al 2014;Uddin et al 2017;Jones et al 2018;Roman et al 2018;Rose et al 2020;Smith et al 2020;Uddin et al 2020;Johansson et al 2021;Kelsey et al 2021;Ponder et al 2021;Popovic et al 2021;Thorp et al 2021;Briday et al 2022;Meldorf et al 2023;Wiseman et al 2022) indicates σ Common > 0, or equivalently, σ Rel < σ 0 .…”

Section: σ Rel Prior Knowledgementioning

confidence: 99%

Relative Intrinsic Scatter in Hierarchical Type Ia Supernova Sibling Analyses: Application to SNe 2021hpr, 1997bq, and 2008fv in NGC 3147

Ward,

Thorp,

Mandel

et al. 2023

ApJ

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We present Young Supernova Experiment grizy photometry of SN 2021hpr, the third Type Ia supernova sibling to explode in the Cepheid calibrator galaxy, NGC 3147. Siblings are useful for improving SN-host distance estimates and investigating their contributions toward the SN Ia intrinsic scatter (post-standardization residual scatter in distance estimates). We thus develop a principled Bayesian framework for analyzing SN Ia siblings. At its core is the cosmology-independent relative intrinsic scatter parameter, σ Rel: the dispersion of siblings distance estimates relative to one another within a galaxy. It quantifies the contribution toward the total intrinsic scatter, σ 0, from within-galaxy variations about the siblings’ common properties. It also affects the combined distance uncertainty. We present analytic formulae for computing a σ Rel posterior from individual siblings distances (estimated using any SN model). Applying a newly trained BayeSN model, we fit the light curves of each sibling in NGC 3147 individually, to yield consistent distance estimates. However, the wide σ Rel posterior means σ Rel ≈ σ 0 is not ruled out. We thus combine the distances by marginalizing over σ Rel with an informative prior: σ Rel ∼ U(0, σ 0). Simultaneously fitting the trio’s light curves improves constraints on distance and each sibling’s individual dust parameters, compared to individual fits. Higher correlation also tightens dust parameter constraints. Therefore, σ Rel marginalization yields robust estimates of siblings distances for cosmology, as well as dust parameters for sibling–host correlation studies. Incorporating NGC 3147's Cepheid distance yields H 0 = 78.4 ± 6.5 km s−1 Mpc−1. Our work motivates analyses of homogeneous siblings samples, to constrain σ Rel and its SN-model dependence.

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“…However, we conservatively include a systematic uncertainty in the Milky Way reddening law and analyze the data using a Cardelli et al (1989) color law. The Cardelli et al (1989) color law has the second lowest χ 2 when compared to the extinction derived from −2.5 0.0 2.5 Comparison between data and alternative simulations generated using the galaxy-driven model by Wiseman et al (2022). We compare distributions of (from top to bottom) SN x1, SN host stellar mass, SN host u − r restframe color, and correlations between x1 and host M⋆ and host u − r restframe color.…”

Section: Milky Way Extinction Correctionsmentioning

confidence: 99%

“…The DES-SN3YR sample included 207 SNe Ia from DES and was critical in the development and motivation of analyses leading up to the work presented here. This includes photometry and calibration (Burke et al 2018;Brout et al 2019b;Lasker et al 2019), survey and SN Ia population modeling (Kessler et al 2019a;Popovic et al 2021b), understanding and modeling of the 'mass/dust step' (Sullivan et al 2010a;Lampeitl et al 2010;Smith et al 2018;Scolnic et al 2020;Brout & Scolnic 2021;Popovic et al 2021a;Wiseman et al 2022;Duarte et al 2022;Dixon et al 2022;Chen et al 2022;Meldorf et al 2023), estimates and treatment of systematic uncertain-ties (Brout et al 2019a(Brout et al , 2020, and the automation of the analysis pipeline .…”

mentioning

confidence: 99%

“…• Improved modeling of SN Ia intrinsic scatter using host dust-based models (Popovic et al 2021a), focusing on modeling correlations between SN Ia properties and both host mass and host color (restframe u−r, following Kelsey et al 2023) and modeling the SN Ia progenitor age distribution (Wiseman et al 2022), thus significantly improving previous analyses that neglect modeling of host properties; (Scolnic & Kessler 2016).…”

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confidence: 99%

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The Dark Energy Survey supernova program: cosmological biases from supernova photometric classification

Vincenzi

Sullivan

Möller

et al. 2022

Monthly Notices of the Royal Astronomical Society

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Cosmological analyses of samples of photometrically-identified type Ia supernovae (SNe Ia) depend on understanding the effects of ‘contamination’ from core-collapse and peculiar SN Ia events. We employ a rigorous analysis using the photometric classifier SuperNNova on state-of-the-art simulations of SN samples to determine cosmological biases due to such ‘non-Ia’ contamination in the Dark Energy Survey (DES) 5-year SN sample. Depending on the non-Ia SN models used in the SuperNNova training and testing samples, contamination ranges from 0.8–3.5 per cent, with a classification efficiency of 97.7–99.5 per cent. Using the Bayesian Estimation Applied to Multiple Species (BEAMS) framework and its extension BBC (‘BEAMS with Bias Correction’), we produce a redshift-binned Hubble diagram marginalised over contamination and corrected for selection effects, and use it to constrain the dark energy equation-of-state, w. Assuming a flat universe with Gaussian ΩM prior of 0.311 ± 0.010, we show that biases on w are <0.008 when using SuperNNova, with systematic uncertainties associated with contamination around 10 per cent of the statistical uncertainty on w for the DES-SN sample. An alternative approach of discarding contaminants using outlier rejection techniques (e.g., Chauvenet’s criterion) in place of SuperNNova leads to biases on w that are larger but still modest (0.015–0.03). Finally, we measure biases due to contamination on w0 and wa (assuming a flat universe), and find these to be <0.009 in w0 and <0.108 in wa, 5 to 10 times smaller than the statistical uncertainties for the DES-SN sample.

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A galaxy-driven model of type Ia supernova luminosity variations

Cited by 19 publications

References 66 publications

A Spectroscopic Model of the Type Ia Supernova–Host-galaxy Mass Correlation from SALT3

A Spectroscopic Model of the Type Ia Supernova–Host-galaxy Mass Correlation from SALT3

Relative Intrinsic Scatter in Hierarchical Type Ia Supernova Sibling Analyses: Application to SNe 2021hpr, 1997bq, and 2008fv in NGC 3147

The Dark Energy Survey supernova program: cosmological biases from supernova photometric classification

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