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ABSTRACT Exoplanet transits contain substantial information about the architecture of a system. By fitting transit light curves we can extract dynamical parameters and place constraints on the properties of the planets and their host star. Having a well-defined probabilistic model plays a crucial role in making robust measurements of these parameters, and the ability to differentiate the model provides access to more robust inference tools. Gradient-based inference methods can allow for more rapid and accurate sampling of high-dimensional parameter spaces. We present a fully differentiable photodynamical model for multiplanet transit light curves that display transit-timing variations. We model time-integrated exposures, compute the dynamics of a system over the full length of observations, and provide analytic expressions for derivatives of the flux with respect to the dynamical and photometric model parameters. The model has been implemented in the Julia language and is available open-source on GitHub. We demonstrate with a simulated data set that Bayesian inference with the NUTS HMC algorithm, which uses the model gradient, can outperform the affine-invariant (e.g. emcee) MCMC algorithm in CPU time per effective sample, and we find that the relative sampling efficiency improves with the number of model parameters.
Langford et al. (Mon,) studied this question.