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A Probabilistic Autoencoder for Type Ia Supernovae Spectral Time Series
George Stein, Uros Seljak, Vanessa Bohm +37
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. T…
The Twins Embedding of Type Ia Supernovae I: The Diversity of Spectra at Maximum Light
K. Boone, G. Aldering, P. Antilogus +34
We study the spectral diversity of Type Ia supernovae (SNe Ia) at maximum light using high signal-to-noise spectrophotometry of 173 SNe Ia from the Nearby Supernova Factory. We dec…
The Twins Embedding of Type Ia Supernovae II: Improving Cosmological Distance Estimates
K. Boone, G. Aldering, P. Antilogus +34
We show how spectra of Type Ia supernovae (SNe Ia) at maximum light can be used to improve cosmological distance estimates. In a companion article, we used manifold learning to bui…
The SNEMO and SUGAR Companion Datasets
G. Aldering, P. Antilogus, C. Aragon +41
The Nearby Supernova Factory has made spectrophotometric observations of Type Ia supernovae since . This work presents an interim version of the data produced, including $210…
Peculiar velocity cosmology with type Ia supernovae
R. Graziani, M. Rigault, N. Regnault +20
Type Ia Supernovae have yet again the opportunity to revolutionize the field of cosmology as the new generation of surveys are acquiring thousands of nearby SNeIa opening a new era…
SUGAR: An improved empirical model of Type Ia Supernovae based on spectral features
P. -F. Léget, E. Gangler, F. Mondon +40
Type Ia Supernovae (SNe Ia) are widely used to measure the expansion of the Universe. Improving distance measurements of SNe Ia is one technique to better constrain the acceleratio…