Speaker
Description
In the Short-Baseline Neutrino (SBN) Program’s two-detector oscillation searches, detector-related systematic uncertainties are expected to be the dominant source of uncertainty due to cancellations in flux and cross section uncertainties between the near and far detectors. Traditional approaches for evaluating detector systematics rely on varying a fixed set of known sources of uncertainties in low-level detector parameters, and propagating those variations to high-level physics variables of interest. However, this approach requires accurately estimating the realistic scale of these variations, cannot capture any unknown sources of systematic uncertainties, and is typically computationally expensive. This poster demonstrates a data-driven approach called WireMod, first developed for the MicroBooNE experiment, for capturing and quantifying unmodelled discrepancies between data and simulated wire waveforms in liquid argon time-projection chamber (LArTPC) detectors like those in the SBN Program and the Deep Underground Neutrino Experiment (DUNE). We show how the WireMod approach can parametrize waveform differences along multiple spatial and angular dimensions using data from the Short-Baseline Near Detector (SBND), and is computationally tractable for producing high-statistics MC variation samples.