A Differentiable Parametric Model of Binary-Black-Hole Initial Data: I. Conformally flat Bowen-York punctures

Abstract

Constraint-satisfying initial data for binary black holes require the repeated solution of a nonlinear elliptic boundary-value problem throughout the physical parameter space. We present a para\-metric, differentiable initial-data model that combines sparse-grid spectral interpolation with Newton refinement over a family of binary configurations. Interpolation is used only to provide warm starts for the underlying elliptic solver, rather than to replace it. A small number of Newton iterations refines each interpolated solution to a discrete, row-equilibrated constraint residual $\|R\|_\infty\le10^{-10}$, independent of the interpolation error, so that every returned initial-data set satisfies the discretized constraint to that tolerance. Our JAX implementation provides analytic derivatives with respect to the physical parameters, enabling gradient-based parameter targeting, optimization, and sensitivity analysis. We demonstrate the method for quasi-circular binary-black-hole initial data using conformally flat Bowen--York punctures by constructing four-dimensional aligned-spin and eight-dimensional general-spin models. The resulting initial data are validated against TwoPunctures, and the method is applied to certified targeting of the physical Arnowitt--Deser--Misner mass and angular momentum and to effective-potential eccentricity reduction using a dedicated separation--tangential-momentum model.