LOITS is a differentiable sampling method, demonstrated in a GAN closure test, that maps sampled events back to the parameters of a target density for event-level inference.
, K}.(8) 6 0.2 0.4 0.6 0.8 x 0.2 0.4 0.6 0.8 y LOITS(4×4) 0.2 0.4 0.6 0.8 x 0.2 0.4 0.6 0.8 y LOITS(4×4) + MH 0.2 0.4 0.6 0.8 x 0.2 0.4 0.6 0.8 y LOITS(50×50) FIG
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Toward an event-level analysis of hadron structure using differential programming
LOITS is a differentiable sampling method, demonstrated in a GAN closure test, that maps sampled events back to the parameters of a target density for event-level inference.