Speaker
Description
The 2x2 Demonstrator is a prototype of ND-LAr, the liquid argon time-projection chamber of the Deep Underground Neutrino Experiment's Near Detector complex. Both the 2x2 Demonstrator and ND-LAr are modular detectors with pixelated charge readouts and inactive regions wherein there is no sensitivity to energy depositions in the liquid argon. These inactive regions are located between the active detector modules, creating the challenge of inferring what the missing charge signals should look like in these regions. This study explores the use of a dual-decoder generative sparse 3D convolutional neural network to infer missing charged-particle track and shower structure and the energy lost in the inactive regions of the 2x2 Demonstrator and ND-LAr. Results indicate that this approach shows promise in predicting missing energy depositions and topology in dead regions with good accuracy.
