1 code implementation • 15 Jul 2022 • George Stein, Uros Seljak, Vanessa Bohm, G. Aldering, P. Antilogus, C. Aragon, S. Bailey, C. Baltay, S. Bongard, K. Boone, C. Buton, Y. Copin, S. Dixon, D. Fouchez, E. Gangler, R. Gupta, B. Hayden, W. Hillebrandt, M. Karmen, A. G. Kim, M. Kowalski, D. Kusters, P. F. Leget, F. Mondon, J. Nordin, R. Pain, E. Pecontal, R. Pereira, S. Perlmutter, K. A. Ponder, D. Rabinowitz, M. Rigault, D. Rubin, K. Runge, C. Saunders, G. Smadja, N. Suzuki, C. Tao, R. C. Thomas, M. Vincenzi
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.