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Improved Photometric Classification of Supernovae using Deep Learning
Adam Moss
We present improved photometric supernovae classification using deep recurrent neural networks. The main improvements over previous work are (i) the introduction of a time gate in…
Deep Recurrent Neural Networks for Supernovae Classification
Tom Charnock, Adam Moss
We apply deep recurrent neural networks, which are capable of learning complex sequential information, to classify supernovae\footnote{Code available at \href{https://github.com/ad…
Planck 2015 results. V. LFI calibration
Planck Collaboration, P. A. R. Ade, N. Aghanim +206
We present a description of the pipeline used to calibrate the Planck Low Frequency Instrument (LFI) timelines into thermodynamic temperatures for the Planck 2015 data release, cov…
Planck 2015 results. VII. HFI TOI and beam processing
Planck Collaboration, R. Adam, P. A. R. Ade +225
The Planck High Frequency Instrument (HFI) has observed the full sky at six frequencies (100, 143, 217, 353, 545, and 857 GHz) in intensity and at four frequencies in linear polari…
Planck 2015 results. II. Low Frequency Instrument data processing
Planck Collaboration, P. A. R. Ade, N. Aghanim +213
We present an updated description of the Planck Low Frequency Instrument (LFI) data processing pipeline, associated with the 2015 data release. We point out the places where our re…
Planck 2013 results. V. LFI calibration
Planck Collaboration, N. Aghanim, C. Armitage-Caplan +217
We discuss the methods employed to photometrically calibrate the data acquired by the Low Frequency Instrument on Planck. Our calibration is based on a combination of the Orbital D…