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The ability to generate physically plausible ensembles of variable sources iscritical to the optimization of time-domain survey cadences and the training ofclassification models on datasets with few to no labels. Traditional dataaugmentation techniques expand training sets by reenvisioning observedexemplars, seeking to simulate observations of specific training sources underdifferent (exogenous) conditions. Unlike fully theory-driven models, theseapproaches do not typically allow principled interpolation nor extrapolation.Moreover, the principal drawback of theory-driven models lies in theprohibitive computational cost of simulating source observables from {\it abinitio} parameters. In this work, we propose a computationally tractablemachine learning approach to generate realistic light curves of periodicvariables capable of integrating physical parameters and variability classes asinputs. Our deep generative model, inspired by the Transparent Latent SpaceGenerative Adversarial Networks (TL-GANs), uses a Variational Autoencoder (VAE)architecture with Temporal Convolutional Network (TCN) layers, trained usingthe \hbox{OGLE-III} optical light curves and physical characteristics (e.g.,effective temperature and absolute magnitude) from Gaia DR2. A test using thetemperature-shape relationship of RR\,Lyrae demonstrates the efficacy of ourgenerative "Physics-Enhanced Latent Space VAE" (PELS-VAE) model. Such deepgenerative models, serving as non-linear non-parametric emulators, present anovel tool for astronomers to create synthetic time series over arbitrarycadences.

Deep Generative Modeling of Periodic Variable Stars Using Physical Parameters