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• W3025730045 abstract "Abstract 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 data sets 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 principled interpolation nor extrapolation. Moreover, the principal drawback of theory-driven models lies in the prohibitive computational cost of simulating source observables from ab initio parameters. In this work, we propose a computationally tractable machine learning approach to generate realistic light curves of periodic variables capable of integrating physical parameters and variability classes as inputs. Our deep generative model, inspired by the transparent latent space generative adversarial networks, uses a variational autoencoder (VAE) architecture with temporal convolutional network layers, trained using the OGLE-III optical light curves and physical characteristics (e.g., effective temperature and absolute magnitude) from Gaia DR2. A test using the temperature–shape relationship of RR Lyrae demonstrates the efficacy of our generative “physics-enhanced latent space VAE” (PELS-VAE) model. Such deep generative models, serving as nonlinear nonparametric emulators, present a novel tool for astronomers to create synthetic time series over arbitrary cadences." @default.
• W3025730045 created "2020-05-21" @default.
• W3025730045 creator A5012189438 @default.
• W3025730045 creator A5053766501 @default.
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• W3025730045 date "2022-11-30" @default.
• W3025730045 modified "2023-09-30" @default.
• W3025730045 title "Deep Generative Modeling of Periodic Variable Stars Using Physical Parameters" @default.
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• W3025730045 doi "https://doi.org/10.3847/1538-3881/ac9b3f" @default.
• W3025730045 hasPublicationYear "2022" @default.
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