• Corpus ID: 218674231

# Deep Generative Modeling of Periodic Variable Stars Using Physical Parameters

@article{MartinezPalomera2020DeepGM,
title={Deep Generative Modeling of Periodic Variable Stars Using Physical Parameters},
author={Jorge Mart'inez-Palomera and Joshua S. Bloom and Ellianna S. Abrahams},
journal={arXiv: Instrumentation and Methods for Astrophysics},
year={2020}
}
• Published 15 May 2020
• Computer Science
• arXiv: Instrumentation and Methods for Astrophysics
The ability to generate physically plausible ensembles of variable sources is critical to the optimization of time-domain survey cadences and the training of classification models on datasets with few to no labels. Traditional data augmentation techniques expand training sets by reenvisioning observed exemplars, seeking to simulate observations of specific training sources under different (exogenous) conditions. Unlike fully theory-driven models, these approaches do not typically allow…
2 Citations

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