A source-time split with bias correction lets ML models infer lattice correlators at nearby mass parameters with per-configuration uncertainty estimates that match truth-level fit results.
Calculation of fermion loops for $\eta^\prime$ and nucleon scalar and electromagnetic form factors
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abstract
The exact evaluation of the disconnected diagram contributions to the flavor-singlet pseudoscalar meson mass, the nucleon sigma term and the nucleon electromagnetic form factors, is carried out utilizing GPGPU technology with the NVIDIA CUDA platform. The disconnected loops are also computed using stochastic methods with several noise reduction techniques. Various dilution schemes as well as the truncated solver method are studied. We make a comparison of these stochastic techniques to the exact results and show that the number of noise vectors depends on the operator insertion in the fermionic loop.
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Using AI for Efficient Statistical Inference of Lattice Correlators Across Mass Parameters
A source-time split with bias correction lets ML models infer lattice correlators at nearby mass parameters with per-configuration uncertainty estimates that match truth-level fit results.