New CC0π GENIE model tune for MicroBooNE
New CC0π GENIE model tune for MicroBooNE
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适用于 MicroBooNE 的新 CC0Ï GENIE 模型调整
DOI：
10.1103/physrevd.105.072001
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发表时间：
2022
影响因子：
5
通讯作者：
Barr, G.
中科院分区：
文献类型：
--
作者：
Abratenko, P.;An, R.;Anthony, J.;Arellano, L.;Asaadi, J.;Ashkenazi, A.;Balasubramanian, S.;Baller, B.;Barnes, C.;Barr, G.
获得具有相关不确定性的高质量相互作用模型对于研究振荡，核散射过程或两者的中微子实验至关重要。作为MicroBooNE实验的下一代中微子截面测量及其对MiniBooNE低能过剩的旗舰研究的主要输入，我们通过两个主要贡献过程-带电电流准弹性和多核子相互作用模型-在GENIE中微子事件发生器3.0.6版中提出了一种新的带电电流无线相互作用截面。这些模型中的参数被调整为T2K实验获得的μ子中微子截面数据，该实验提供了一组独立的中微子相互作用，中微子通量与MicroBooNE的中微子束的能量范围相似。虽然适合于介子中微子数据，但由于GENIE中使用了相同的底层模型，因此信息可以传递到电子中微子模拟中。为此开发了许多新的拟合参数，并从现有集和新集中选择最优参数。我们选择拟合之前没有受到理论或数据约束的四个参数。因此，这将被称为理论驱动的曲调。结果是对t2k数据的改进匹配，基于拟合具有更多动机良好的不确定性。
Obtaining a high-quality interaction model with associated uncertainties is essential for neutrino experiments studying oscillations, nuclear scattering processes, or both. As a primary input to the MicroBooNE experiment’s next generation of neutrino cross section measurements and its flagship investigation of the MiniBooNE low-energy excess, we present a new tune of the charged-current pionless () interaction cross section via the two major contributing processes—charged-current quasielastic and multinucleon interaction models—within version 3.0.6 of the GENIE neutrino event generator. Parameters in these models are tuned to muon neutrinocross section data obtained by the T2K experiment, which provides an independent set of neutrino interactions with a neutrino flux in a similar energy range to MicroBooNE’s neutrino beam. Although the fit is to muon neutrino data, the information carries over to electron neutrino simulation because the same underlying models are used in GENIE. A number of novel fit parameters were developed for this work, and the optimal parameters were chosen from existing and new sets. We choose to fit four parameters that have not previously been constrained by theory or data. Thus, this will be called a theory-driven tune. The result is an improved match to the T2Kdata with more well-motivated uncertainties based on the fit.