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.
Code for prompt numerical computation of the leading order GPD evolution
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This paper describes the design and work of a set of computer routines capable for numerical computation of generalized parton distributions (GPDs) evolution at the leading order. The main intention of this work is to present a fast-working computer code making possible fitting of GPDs parameters to the data on hard electron-nucleon scattering.
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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.