Speaker
Description
Large water Cherenkov detector experiments aim to shed light on violations in the neutrino sector by extracting the oscillation parameters from observed neutrino events. However, this is made challenging by the complexity of the detector dataset, which consists of charge and timing measurements from tens of thousands of photomultiplier tubes. In this talk, I describe recent progress toward a fully Bayesian formulation of the oscillation analysis. First, we leverage advances in machine learning to compress the dataset by several orders of magnitude into a several-dimension summary statistic. Then, we employ simulation-based inference with normalising flows to obtain a representation of the likelihood for this dimensionally-reduced data. Finally, we carry out hierarchical Bayesian inference with these flows to demonstrate how such a detector will be able to measure the CP-violating phase in a fully Bayesian framework.
| Contribution types | Standard talk (20min + 5min Q/A) |
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