| Full text | |
| Author(s): |
Ruiz Vargas, J. C.
;
Novaes, S. F.
;
Cobe, R.
;
Iope, R.
;
Stanzani, S.
;
Tomei, T. R.
;
IEEE
Total Authors: 7
|
| Document type: | Journal article |
| Source: | 2018 XLIV LATIN AMERICAN COMPUTER CONFERENCE (CLEI 2018); v. N/A, p. 5-pg., 2018-01-01. |
| Abstract | |
Deep neural networks provide the canvas to create models of millions of parameters to fit distributions involving an equally large number of random variables. The contribution of this study is twofold. First, we introduce a diffraction dataset containing computer-based simulations of a Young's interference experiment. Then, we demonstrate the adeptness of variational autoencoders to learn diffraction patterns and extract a latent feature that correlates with the physical wavelength. (AU) | |
| FAPESP's process: | 13/01907-0 - São Paulo Research and Analysis Center |
| Grantee: | Sergio Ferraz Novaes |
| Support Opportunities: | Research Projects - Thematic Grants |
| FAPESP's process: | 16/15897-4 - Search for new physics at the CMS experiment of the Large Hadron Collider |
| Grantee: | Thiago Rafael Fernandez Perez Tomei |
| Support Opportunities: | Regular Research Grants |