Speaker
Description
We present a first search for dark-trident scattering in a neutrino beam using a data set taken with the MicroBooNE detector at Fermilab. Proton interactions in the neutrino target at the Main Injector produce neutral mesons, which could decay into dark-matter (DM) particles mediated via a dark photon A′. A convolutional neural network (CNN) is trained to identify interactions of the DM particles in the liquid-argon time projection chamber (LArTPC) exploiting its image-like reconstruction capability. The CNN architecture is based on a model for dense images with adaptations for LArTPCs. The output layer has two neurons that correspond to the probability for signal or background.
In the absence of a DM signal, we provide limits at the 90% confidence level on the coupling parameters of the model as a function of the dark-photon mass using the CNN outputs, excluding previously unexplored regions of parameter space.
| Type of contribution | Talk: 15 minutes. |
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