Deep Generative Modeling of Periodic Variable Stars Using Physical Parameters

Jorge Martínez-Palomera, Joshua Bloom, Ellianna Abrahams

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.