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.
MILOU : a Monte-Carlo for Deeply Virtual Compton Scattering
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In this note, we present a new generator for Deeply Virtual Compton Scattering processes. This generator is based on formalism of Generalized Partons Distributions evolved at Next Leading Order (NLO). In the following we give a brief description of this formalism and we explain the main features of the generator for the elastic reactions, as well as for proton dissociation.
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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.