Speaker
Description
MicroBooNE is a short baseline neutrino oscillation experiment that employs a Liquid Argon Time Projection Chamber (LArTPC) together with an array of Photomultiplier Tubes (PMTs), which detect scintillation light. This light detection is necessary for providing a means to reject cosmic ray background and trigger on beam-related interactions. Thus, accurate modeling of the expected optical detector signal is critical. Previous light models used on MicroBooNE have been simulation-based, which limits accuracy related to certain regions of the detector as well as different data conditions during runs. We present the status of a data-driven light model that uses a neural network to map the light yield in the MicroBooNE detector, allowing for specific conditioning based on MicroBooNE data.
| Type of contribution | Talk: 15 minutes. |
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