A Probabilistic Autoencoder for Type Ia Supernovae Spectral Time Series

Autor: Stein, George, Seljak, Uros, Buton, C., Copin, Y., Dixon, S., Fouchez, D., Gangler, E., Gupta, R., Hayden, B., Hillebrandt, W., Karmen, M., Kim, A. G., Bohm, Vanessa, Kowalski, M., Kuesters, Daniel, Leget, P. F., Mondon, F., Nordin, J., Pain, R., Pecontal, E., Pereira, R., Perlmutter, S., Ponder, K. A., Aldering, G., Rabinowitz, D., Rigault, M., Rubin, D., Runge, K., Saunders, C., Smadja, G., Suzuki, N., Tao, C., Taubenberger, S., Thomas, R. C., Antilogus, P., Vincenzi, M., Nearby Supernova Factory Collaboration, Aragon, C., Bailey, S., Baltay, C., Bongard, S., Boone, K.
Přispěvatelé: Laboratoire de Physique Nucléaire et de Hautes Énergies (LPNHE (UMR_7585)), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut de Physique des 2 Infinis de Lyon (IP2I Lyon), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), Centre de Physique des Particules de Marseille (CPPM), Aix Marseille Université (AMU)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Physique de Clermont (LPC), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS)-Université Clermont Auvergne (UCA), Centre de Recherche Astrophysique de Lyon (CRAL), École normale supérieure de Lyon (ENS de Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), Institut Lagrange de Paris, Nearby Supernova Factory
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Astrophys.J.
Astrophys.J., 2022, 935 (1), pp.5. ⟨10.3847/1538-4357/ac7c08⟩
The astrophysical journal / 2 935(1), 5 (2022). doi:10.3847/1538-4357/ac7c08
ISSN: 1538-4357
Popis: The astrophysical journal 935(1), 5 (2022). doi:10.3847/1538-4357/ac7c08
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.
Published by Univ., Chicago, Ill. [u.a.]
Databáze: OpenAIRE
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