We present a first search for dark-trident scattering in a neutrino beam using a dataset corresponding toprotons on target taken with the MicroBooNE detector at Fermilab. Proton interactions in the neutrino target at the main injector produceandmesons, which could decay into dark-matter (DM) particles mediated via a dark photon. A convolutional neural network is trained to identify interactions of the DM particles in the liquid-argon time projection chamber (LArTPC) exploiting its imagelike reconstruction capability. In the absence of a DM signal, we provide limits at the 90% confidence level on the squared kinematic mixing parameteras a function of the dark-photon mass in the range. The limits cover previously unconstrained parameter space for the production of fermion or scalar DM particlesfor two benchmark models with mass ratiosand 2 and for dark fine-structure constants.
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