yippy : Standardized Yield Input Package Processing and Fourier-Domain PSF Interpolation for HWO Coronagraphs

Abstract Coronagraphs are a fundamental component of the Habitable Worlds Observatory (HWO), and nearly every aspect of mission design depends on accurate coronagraph performance data. Coronagraph designers generate yield input packages (YIPs), a standardized FITS-based format consumed by HWO yield calculators like ayo and exosims. However, no standard tool exists to process and use them: each research group independently recreates the same steps to extract throughput curves, compute contrast, and interpolate PSFs, and small method differences produce significant errors. We present yippy (Yield Input Package Python), an open-source library that standardizes YIP usage. yippy reproduces all coronagraph performance metrics used by ayo and exosims, so HWO researchers can use coronagraph data consistently with the community’s yield and exposure time calculators. Beyond scalar metrics, yippy implements Fourier-based PSF interpolation that generates spatially continuous off-axis images from the discrete PSF set in a YIP at yield-pipeline speeds, providing a reusable interface for coronagraph image simulation from yield input data. A jax-compatible interface supports batch processing on accelerators: on an H100 GPU yippy calculates nearly 15,000 PSFs per second, while the underlying physical optics propagation sustains roughly 4–5 PSFs per second on an Apple M2 Pro CPU. We validate the interpolation accuracy across three coronagraph architectures, demonstrating median relative RMS errors of ∼10−4 at typical grid spacings, and cross-validate performance metrics against both ayo (to 3% error) and exosims (to 6% error), two yield calculators whose ETCs have known disagreements.