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- W2897779246 abstract "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 the recurrent cell that uses the observational time as an input; (ii) greatly increased data augmentation including time translation, addition of Gaussian noise and early truncation of the lightcurve. For post Supernovae Photometric Classification Challenge (SPCC) data, using a training fraction of $5.2%$ (1103 supernovae) of a representational dataset, we obtain a type Ia vs. non type Ia classification accuracy of $93.2 pm 0.1%$, a Receiver Operating Characteristic curve AUC of $0.980 pm 0.002$ and a SPCC figure-of-merit of $F_1=0.57 pm 0.01$. Using a representational dataset of $50%$ ($10660$ supernovae), we obtain a classification accuracy of $96.6 pm 0.1%$, an AUC of $0.995 pm 0.001$ and $F_1=0.76 pm 0.01$. We found the non-representational training set of the SPCC resulted in a large degradation in performance due to a lack of faint supernovae, but this can be migrated by the introduction of only a small number ($sim 100$) of faint training samples. We also outline ways in which this could be achieved using unsupervised domain adaptation." @default.
- W2897779246 created "2018-10-26" @default.
- W2897779246 creator A5001572213 @default.
- W2897779246 date "2018-10-15" @default.
- W2897779246 modified "2023-09-27" @default.
- W2897779246 title "Improved Photometric Classification of Supernovae using Deep Learning" @default.
- W2897779246 hasPublicationYear "2018" @default.
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