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.
2 code implementations • 13 Aug 2019 • M. Vincenzi, M. Sullivan, R. E. Firth, C. P. Gutiérrez, C. Frohmaier, M. Smith, C. Angus, R. C. Nichol
The design and analysis of time-domain sky surveys requires the ability to simulate accurately realistic populations of core collapse supernova (SN) events.
High Energy Astrophysical Phenomena