Outputs (2)

Deep recurrent neural networks for supernovae classification (2017)
Journal Article
Charnock, T., & Moss, A. (2017). Deep recurrent neural networks for supernovae classification. Astrophysical Journal, 837(2), https://doi.org/10.3847/2041-8213/aa603d

We apply deep recurrent neural networks, which are capable of learning complex sequential information, to classify supernovae (code available at https://github.com/adammoss/supernovae). The observational time and filter fluxes are used as inputs to t... Read More about Deep recurrent neural networks for supernovae classification.

Planck intermediate results. L. Evidence of spatial variation of the polarized thermal dust spectral energy distribution and implications for CMB B-mode analysis (2017)
Journal Article
Planck Collaboration, Aghanim, N., Ashdown, M., Aumont, J., Baccigalupi, C., Ballardini, M., …Zonca, A. (2017). Planck intermediate results. L. Evidence of spatial variation of the polarized thermal dust spectral energy distribution and implications for CMB B-mode analysis. Astronomy and Astrophysics, 599, Article A51. https://doi.org/10.1051/0004-6361/201629164

The characterization of the Galactic foregrounds has been shown to be the main obstacle in thechallenging quest to detect primordial B-modes in the polarized microwave sky. We make use of the Planck-HFI 2015 data release at high frequencies to place... Read More about Planck intermediate results. L. Evidence of spatial variation of the polarized thermal dust spectral energy distribution and implications for CMB B-mode analysis.