Speaker
Description
The MicroBooNE experiment at Fermilab is a short baseline neutrino experiment. The experiment was created with the goal of examining the low-energy excess seen in MiniBooNE. The MicroBooNE detector is a LArTPC which relies on three wire readout planes to collect data. In order to examine the low energy excess, it is necessary to reject background with high efficiency while retaining high purity and efficiency of signal events. One major problem is about ten percent of the readout wires are either missing (dead) or noisy. Traditional cosmic ray taggers often fail due to these dead channels, especially in regions with clusters of dead wires. These algorithms involve clustering groups of charge together which is often broken up by the dead wires. This poster presents the results for implementing a generative neural network that reconstructs the ADC values in the dead channels in the context of improving the results of the cosmic ray tagging algorithms.
