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arXiv 1012.1204hep-phnucl-th
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无中微子双电子俘获的共振增强

Resonance enhancement of neutrinoless double electron capture

M. I. Krivoruchenko, Fedor Simkovic, Dieter Frekers, Amand Faessler

摘要:

本文重新讨论了无中微子双电子俘获过程($0ν$ ECEC)中两个参与原子质量接近简并的情况。理论框架是具有不同轻子总数(和宇称)的两个原子的振荡形式,其中一个可以处于激发态,从而实现质量简并。在这种情况下,假设是轻的马约拉纳中微子,两个原子相对于弱相互作用将处于混合构型。这种原子对之间的跃迁将发生共振增强,这可以通过子原子和原子核的激发态的随后电磁去激发来检测。原子质量,以及核和原子激发的可用数据被用来选择最有可能的候选人,这样的共振$0ν$ ECEC过渡。假设马约拉纳中微子的有效质量为1 eV,在幺正极限下,一些中微子的半衰期被预测为低至$10^{22}$年。有人认为,为了获得更准确的预测的$0ν$ ECEC半衰期,精确的质量测量所涉及的原子是必要的,这可以很容易地完成今天的高精度潘宁陷阱。进一步的进展还需要更好地理解末态核的高激发态(即激发能、角动量和宇称)以及核矩阵元的计算。

英文摘要:

The process of neutrinoless double electron capture ($0ν$ECEC) is revisited for those cases where the two participating atoms are nearly degenerate in mass. The theoretical framework is the formalism of an oscillation of two atoms with different total lepton number (and parity), one of which can be in an excited state so that mass degeneracy is realized. In such a case and assuming light Majorana neutrinos, the two atoms will be in a mixed configuration with respect to the weak interaction. A resonant enhancement of transitions between such pairs of atoms will occur, which could be detected by the subsequent electromagnetic de-excitation of the excited state of the daughter atom and nucleus. Available data of atomic masses, as well as nuclear and atomic excitations are used to select the most likely candidates for such resonant $0ν$ECEC transitions. Assuming an effective mass for the Majorana neutrino of 1 eV, some half-lives are predicted to be as low as $10^{22}$ years in the unitary limit. It is argued that, in order to obtain more accurate predictions for the $0ν$ECEC half-lives, precision mass measurements of the atoms involved are necessary, which can readily be accomplished by today's high precision Penning traps. Further advancements also require a better understanding of high-lying excited states of the final nuclei (i.e. excitation energy, angular momentum and parity) and the calculation of the nuclear matrix elements.

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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