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.
1 code implementation • 11 Apr 2018 • E. E. O. Ishida, R. Beck, S. Gonzalez-Gaitan, R. S. de Souza, A. Krone-Martins, J. W. Barrett, N. Kennamer, R. Vilalta, J. M. Burgess, B. Quint, A. Z. Vitorelli, A. Mahabal, E. Gangler
We report a framework for spectroscopic follow-up design for optimizing supernova photometric classification.
Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics