The Twins Embedding of Type Ia Supernovae. I. The Diversity of Spectra at Maximum Light

Boone, K.; Aldering, G.; Antilogus, P.; Aragon, C.; Bailey, S.; Baltay, C.; Bongard, S.; Buton, C.; Copin, Y.; Dixon, S.; Fouchez, D.; Gangler, É.; Gupta, R.; Hayden, Brian; Hillebrandt, W.; Kim, A. G.; Kowalski, M.; Küsters, D.; Léget, P. F.; Mondon, Florian; Nordin, J.; Pain, R.; Pécontal, É.; Pereira, R.; Perlmutter, S.; Ponder, K. A.; Rabinowitz, D.; Rigault, M.; Rubin, D.; Runge, K.; Saunders, C.; Smadja, G.; Suzuki, N.; Tao, C.; Taubenberger, S.; Thomas, R. C.; Vincenzi, M.

doi:10.3847/1538-4357/abec3c

Cited by 13 publications

(13 citation statements)

References 68 publications

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“…Any optical depth variations along the line of sight should be slowly varying with respect to the ∼50 day observation window of an SN (Huang et al 2017), and therefore should be dependent only on the wavelength of observation. This color law is generally an input to the model (Guy et al 2007;Saunders et al 2018;Mandel et al 2022;Boone et al 2021a), and any timedependent color variation should be captured by the variations in the global model.…”

Section: Empirical Modeling Of Sne Iamentioning

confidence: 99%

“…Following our past procedure for similar analyses (Fakhouri et al 2015;Saunders et al 2018;Aldering et al 2020;Léget et al 2020;Boone et al 2021aBoone et al , 2021b, the wavelength and phases have been transformed to the rest frame, and the fluxes have been transformed to a reference redshift of z = 0.05 using the appropriate factors of z and 1 + z using redshifts from Childress et al (2013) and Rigault et al (2020). Because SN Ia spectral features are broad, the spectra are rebinned to a common rest-frame wavelength binning of 1000 km s −1 between 3300 and 8600 Å, resulting in N λ = 288 rest frame wavelength bins.…”

Section: Data Set and Reference Baselinesmentioning

confidence: 99%

“…Throughout this work we compare our spectral reconstructions to the SALT2 model (Guy et al 2007), and compare our cosmological distance measurements to both the SALT2 model and the Twins Embedding (Boone et al 2021a(Boone et al , 2021b. where p is the rest-frame time since the date of maximum luminosity in the B band and λ is the rest-frame wavelength.…”

Section: Reference Baselinesmentioning

confidence: 99%

“…The Twins Embedding (Boone et al 2021a(Boone et al , 2021b does not model temporal evolution and instead aims to explain the spectral variability of SNe Ia at maximum light. There are four separate components to the model: 1.…”

Section: Twins Embeddingmentioning

confidence: 99%

“…With the advent of full SN Ia spectral time series (Aldering et al 2020) or their virtually constructed analogs (Siebert et al 2019;Stahl et al 2020b), it has become possible to study the full spectro-temporal behavior of SNe Ia. Examples include the first construction of a spectral metric space (Sasdelli et al 2015), Gaussian Process (GP) twinning (Fakhouri et al 2015), expectation maximization factor analysis (Saunders et al 2018), spectral feature factor analysis (Léget et al 2020), an SN Ia autoencoder (Sasdelli et al 2016) hierarchical Bayesian spectro-temporal modeling (Mandel et al 2022), deep learning (Stahl et al 2020a), and the nonlinear Twins Embedding space of Boone et al (2021aBoone et al ( , 2021b.…”

Section: Introductionmentioning

confidence: 99%

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A Probabilistic Autoencoder for Type Ia Supernova Spectral Time Series

Stein

Seljak

Böhm

et al. 2022

ApJ

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We construct a physically parameterized probabilistic autoencoder (PAE) to learn the intrinsic diversity of Type Ia supernovae (SNe Ia) from a sparse set of spectral time series. The PAE is a two-stage generative model, composed of an autoencoder that is interpreted probabilistically after training using a normalizing flow. We demonstrate that the PAE learns a low-dimensional latent space that captures the nonlinear range of features that exists within the population and can accurately model the spectral evolution of SNe Ia across the full range of wavelength and observation times directly from the data. By introducing a correlation penalty term and multistage training setup alongside our physically parameterized network, we show that intrinsic and extrinsic modes of variability can be separated during training, removing the need for the additional models to perform magnitude standardization. We then use our PAE in a number of downstream tasks on SNe Ia for increasingly precise cosmological analyses, including the automatic detection of SN outliers, the generation of samples consistent with the data distribution, and solving the inverse problem in the presence of noisy and incomplete data to constrain cosmological distance measurements. We find that the optimal number of intrinsic model parameters appears to be three, in line with previous studies, and show that we can standardize our test sample of SNe Ia with an rms of 0.091 ± 0.010 mag, which corresponds to 0.074 ± 0.010 mag if peculiar velocity contributions are removed. Trained models and codes are released at https://github.com/georgestein/suPAErnova.

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