Feed-forward neural networks trained on Fourier coefficients can predict modular weights for negative-weight powers of eta and E2, and for simple Jacobi theta products, within the training range.
Rademacher expansion of a Siegel modular form for ${\cal N}= 4$ counting
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abstract
The degeneracies of $1/4$ BPS states with unit torsion in heterotic string theory compactified on a six-torus are given in terms of the Fourier coefficients of the reciprocal of the Igusa cusp Siegel modular form $\Phi_{10}$ of weight $10$. We use the symplectic symmetries of the latter to construct a fine-grained Rademacher type expansion which expresses these BPS degeneracies as a regularized sum over residues of the poles of $1/\Phi_{10}$. The construction uses two distinct ${\rm SL}(2, \mathbb{Z})$ subgroups of ${\rm Sp}(2, \mathbb{Z})$ which encode multiplier systems, Kloosterman sums and Eichler integrals appearing therein. Additionally, it shows how the polar data are explicitly built from the Fourier coefficients of $1/\eta^{24}$ by means of a continued fraction structure.
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Machine learning automorphic forms for black holes
Feed-forward neural networks trained on Fourier coefficients can predict modular weights for negative-weight powers of eta and E2, and for simple Jacobi theta products, within the training range.