Investigating the diversity of Type Ia supernova spectra with the open-source relational data base kaepora

Siebert, M. R.; Foley, R. J.; Jones, D. O.; Angulo, Raúl E.; Davis, Kyle W.; Duarte, Anaí; Strasburger, E.; Conlon, Mallory; Kazmi, N.; Nishimoto, R.; Schubert, Marcel; Sun, L.; Tippens, Rebecca

doi:10.1093/mnras/stz1209

Cited by 45 publications

(47 citation statements)

References 105 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In this work, we aim to investigate whether any optical spectral properties of SNe Ia correlate with HRs. This work makes use of kaepora, a public, open-source relational database of SN Ia spectra that was recently presented by Siebert et al (2019) (hereafter S19). This tool provides a large sample of homogenized SN Ia spectra and their associated metadata.…”

Section: Introductionmentioning

confidence: 99%

A possible distance bias for type Ia supernovae with different ejecta velocities

Siebert

Foley

Jones

et al. 2020

Monthly Notices of the Royal Astronomical Society

Self Cite

View full text Add to dashboard Cite

After correcting for their light-curve shape and color, Type Ia supernovae (SNe Ia) are precise cosmological distance indicators. However, there remains a non-zero intrinsic scatter in the differences between measured distance and that inferred from a cosmological model (i.e., Hubble residuals or HRs), indicating that SN Ia distances can potentially be further improved. We use the open-source relational database kaepora to generate composite spectra with desired average properties of phase, light-curve shape, and HR. At many phases, the composite spectra from two subsamples with positive and negative average HRs are significantly different. In particular, in all spectra from 9 days before to 15 days after peak brightness, we find that SNe with negative HRs have, on average, higher ejecta velocities (as seen in nearly every optical spectral feature) than SNe with positive HRs. At +4 days relative to B-band maximum, using a sample of 62 SNe Ia, we measure a 0.091 ± 0.035 mag (2.7σ) HR step between SNe with Si ii λ6355 line velocities (v Si ii ) higher/lower than −11,000 km s −1 (the median velocity). After light-curve shape and color correction, SNe with higher velocities tend to have underestimated distance moduli relative to a cosmological model. The intrinsic scatter in our sample reduces from 0.094 mag to 0.082 mag after making this correction. Using the Si ii λ6355 velocity evolution of 115 SNe Ia, we estimate that a velocity difference >500 km s −1 exists at each epoch between the positive-HR and negative-HR samples with 99.4% confidence. Finally at epochs later than +37 days, we observe that negative-HR composite spectra tend to have weaker spectral features in comparison to positive-HR composite spectra.

show abstract

Section: Introductionmentioning

confidence: 99%

A possible distance bias for type Ia supernovae with different ejecta velocities

Siebert

Foley

Jones

et al. 2020

Monthly Notices of the Royal Astronomical Society

Self Cite

View full text Add to dashboard Cite

show abstract

“…byosed perturbers can also be created from observations. Here we use composite spectra generated from the kaepora 2 database, an open-source relational database of SN Ia observations (Siebert et al 2019). We use the Gini-weighting method to create the composite spectra, outlined in Siebert et al (2019), which provides a representative spectrum that maximizes S/N ratio while mitigating the impact of high S/N outliers.…”

Section: Creating Byosed Perturbers From Observablesmentioning

confidence: 99%

“…Here we use composite spectra generated from the kaepora 2 database, an open-source relational database of SN Ia observations (Siebert et al 2019). We use the Gini-weighting method to create the composite spectra, outlined in Siebert et al (2019), which provides a representative spectrum that maximizes S/N ratio while mitigating the impact of high S/N outliers. We control for average properties of phase and ∆m 15 (B) to produce sequences of composite spectra with desired properties (e.g.…”

Section: Creating Byosed Perturbers From Observablesmentioning

confidence: 99%

“…As a final example, we include host galaxy mass correlations as a byosed perturber in our simulations, in addition to the stretch and color perturbers outlined in Section 3.2. Host galaxy masses are simulated according to the observed population from Siebert et al (2019). In this section we create two sets of simulations: First we include host mass as a static byosed perturber with no redshift evolution.…”

Section: Fiducial+m Stellar and Fiducial+m Stellar (Z) Simulationsmentioning

confidence: 99%

See 1 more Smart Citation

Understanding Type Ia Supernova Distance Biases by Simulating Spectral Variations

Pierel,

Jones,

Dai

et al. 2020

Preprint

View full text Add to dashboard Cite

In the next decade, transient searches from the Vera C. Rubin Observatory and the Nancy Grace Roman Space Telescope will increase the sample of known Type Ia Supernovae (SN Ia) from ∼10 3 to 10 5 . With this reduction of statistical uncertainties on cosmological measurements, new methods are needed to reduce systematic uncertainties. Characterizing the underlying spectroscopic evolution of SN Ia remains a major systematic uncertainty in current cosmological analyses, motivating a new simulation tool for the next era of SN Ia cosmology: Build Your Own Spectral Energy Distribution (byosed). byosed is used within the SNANA framework to simulate light curves by applying spectral variations to model SEDs, enabling flexible testing of possible systematic shifts in SN Ia distance measurements. We test the framework by comparing a nominal Roman SN Ia survey simulation using a baseline SED model to simulations using SEDs perturbed with byosed, and investigate the impact of neglecting specific SED features in the analysis. These features include semi-empirical models of two possible, predicted relationships: between SN ejecta velocity and light curve observables, and a redshift-dependent relationship between SN Hubble residuals and host galaxy mass. We analyze each byosed simulation using the SALT2 & BBC framework, and estimate changes in the measured value of the dark energy equation-of-state parameter, w. We find a difference of ∆w = −0.023 for SN velocity and ∆w = 0.021 for redshift-evolving host mass when compared to simulations without these features. By using byosed for SN Ia cosmology simulations, future analyses (e.g., Rubin and Roman SN Ia samples) will have greater flexibility to constrain or reduce such SN Ia modeling uncertainties.

show abstract

“…At the same time, the spectroscopic sample of SNe Ia has grown considerably (e.g., Silverman et al 2012a;Blondin et al 2012;Folatelli et al 2013;Stahl et al 2020a). Consequently, there has been forward progress in identifying spectroscopic parameters to potentially improve the precision of SN Ia distance measurements (e.g., Bailey et al 2009;Wang et al 2009;Blondin et al 2011;Silverman et al 2012b;Fakhouri et al 2015;Zheng et al 2018;Siebert et al 2019;Léget et al 2020). Relatedly, recent work has demonstrated that Δ𝑚 15 , a measure of light-curve shape -and hence, of peak luminosity via the SN Ia WLR -can be recovered from a single optical spectrum with a high degree of precision through the use of convolutional neural networks (using, e.g., the deepSIP 1 package; Stahl et al 2020b, S20 hereafter).…”

Section: Introductionmentioning

confidence: 99%

The snapshot distance method: estimating the distance to a Type Ia supernova from minimal observations

Stahl,

de Jaeger,

Zheng

et al. 2021

Preprint

View full text Add to dashboard Cite

We present the snapshot distance method (SDM), a modern incarnation of a proposed technique for estimating the distance to a Type Ia supernova (SN Ia) from minimal observations. Our method, which has become possible owing to recent work in the application of deep learning to SN Ia spectra (we use the deepSIP package), allows us to estimate the distance to an SN Ia from a single optical spectrum and epoch of 2+ passband photometry -one night's worth of observations (though contemporaneity is not a requirement). Using a compilation of well-observed SNe Ia, we generate snapshot distances across a wide range of spectral and photometric phases, light-curve shapes, photometric passband combinations, and spectrum signal-to-noise ratios. By comparing these estimates to the corresponding distances derived from fitting all available photometry for each object, we demonstrate that our method is robust to the relative temporal sampling of the provided spectroscopic and photometric information, and to a broad range of light-curve shapes that lie within the domain of standard width-luminosity relations. Indeed, the median residual (and asymmetric scatter) between SDM distances derived from two-passband photometry and conventional light-curve-derived distances that utilise all available photometry is 0.013 +0.154 −0.143 mag. Moreover, we find that the time of maximum brightness and light-curve shape (both of which are spectroscopically derived in our method) are only minimally responsible for the observed scatter. In a companion paper, we apply the SDM to a large number of sparsely observed SNe Ia as part of a cosmological study.

show abstract

Investigating the diversity of Type Ia supernova spectra with the open-source relational data base kaepora

Cited by 45 publications

References 105 publications

A possible distance bias for type Ia supernovae with different ejecta velocities

A possible distance bias for type Ia supernovae with different ejecta velocities

Understanding Type Ia Supernova Distance Biases by Simulating Spectral Variations

The snapshot distance method: estimating the distance to a Type Ia supernova from minimal observations

Contact Info

Product

Resources

About