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arXiv 1004.0358hep-exphysics.ins-det
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中微子工厂用真实μ子重建的MIND探测器的性能

Performance of the MIND detector at a Neutrino Factory using realistic muon reconstruction

A. Cervera, A. Laing, J. Martin-Albo, F. J. P. Soler

摘要:

利用μ子衰变产生由nubar_e和nubar_mu组成的强流束的中微子工厂对两个目前尚未测量的中微子混合参数θ_13和delta_CP具有最高的灵敏度。使用“错号μ子”信号测量50千吨磁化铁中微子探测器(MIND)中的nu_e至nu_mu(nubar_e至nubar_mu)振荡,对delta_CP的灵敏度可以保持在低至theta_13的小值。然而,在以前的研究中使用的检测器效率计算假设完美的模式识别。在本文中,MIND重新评估考虑到,第一次,一个现实的模式识别的μ子候选人。分析的重新优化利用了多种方法的组合,包括与MINOS中使用的多变量分析类似的多变量分析,以保持高效率,同时抑制背景,确保信号选择效率和背景水平与先前分析中的结果相当或更好。

英文摘要:

A Neutrino Factory producing an intense beam composed of nu_e(nubar_e) and nubar_mu(nu_mu) from muon decays has been shown to have the greatest sensitivity to the two currently unmeasured neutrino mixing parameters, theta_13 and delta_CP . Using the `wrong-sign muon' signal to measure nu_e to nu_mu(nubar_e to nubar_mu) oscillations in a 50 ktonne Magnetised Iron Neutrino Detector (MIND) sensitivity to delta_CP could be maintained down to small values of theta_13. However, the detector efficiencies used in previous studies were calculated assuming perfect pattern recognition. In this paper, MIND is re-assessed taking into account, for the first time, a realistic pattern recognition for the muon candidate. Reoptimisation of the analysis utilises a combination of methods, including a multivariate analysis similar to the one used in MINOS, to maintain high efficiency while suppressing backgrounds, ensuring that the signal selection efficiency and the background levels are comparable or better than the ones in previous analyses.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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