cymyc -- Calabi-Yau Metrics, Yukawas, and Curvature
Per Berglund, Giorgi Butbaia, Tristan Hübsch, Vishnu Jejjala, Challenger Mishra, Damián Mayorga Peña, Justin Tan
TL;DR
This work introduces cymyc, a high-performance Python framework for numerically exploring Calabi–Yau geometry and moduli spaces by constructing globally defined tensor-field approximations via a geometric ansatz and neural-network discretisation. It develops dd^c and global-section-based approaches to approximate Ricci-flat metrics, together with harmonic-form techniques anchored in Kodaira–Spencer theory, and validates them on complete-intersection CYs such as mirrors of P^5[3,3], P^7[2,2,2,2], and the Tian–Yau quotient. The library computes moduli-space metrics (Weil–Petersson) and canonically normalised Yukawa couplings, demonstrating close agreement with special-geometry results and revealing moduli-dependent curvature behaviour, including heavy-tailed distributions and singular-locus phenomena. By enabling efficient Monte Carlo evaluations of geometric and topological observables, cymyc provides a practical bridge from compactification geometry to low-energy phenomenology, and opens avenues for systematic exploration of the string landscape and mathematical conjectures in CY geometry.
Abstract
We introduce \texttt{cymyc}, a high-performance Python library for numerical investigation of the geometry of a large class of string compactification manifolds and their associated moduli spaces. We develop a well-defined geometric ansatz to numerically model tensor fields of arbitrary degree on a large class of Calabi-Yau manifolds. \texttt{cymyc} includes a machine learning component which incorporates this ansatz to model tensor fields of interest on these spaces by finding an approximate solution to the system of partial differential equations they should satisfy.
