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arXiv 1201.3631hep-phastro-ph.COphysics.data-an
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未来暗物质直接探测实验的基本统计限制

Fundamental statistical limitations of future dark matter direct detection experiments

Charlotte Strege, Roberto Trotta, Gianfranco Bertone, Annika H. G. Peter, Pat Scott

摘要:

我们讨论了未来吨级暗物质直接探测实验不可减少的统计限制。我们特别关注置信区间的覆盖范围,它量化了用于重建暗物质参数的统计方法的可靠性,以及重建参数的偏差。我们在即将到来的吨级实验范围内研究了36个基准暗物质模型。我们发现,近似的置信区间从配置文件的似然分析完全覆盖或覆盖的WIMP参数的真实值,因此是保守的。我们评估数据中不可避免的统计波动可能导致暗物质参数的有偏重建或重建参数值的大不确定性的概率。我们表明,这种概率可以是令人惊讶的大,即使是基准模型导致一个大的事件率为一百计数。我们发现,从两个不同的目标相结合的数据集导致改善的覆盖性能,以及大幅减少统计偏差和不确定性的暗物质参数。

英文摘要:

We discuss irreducible statistical limitations of future ton-scale dark matter direct detection experiments. We focus in particular on the coverage of confidence intervals, which quantifies the reliability of the statistical method used to reconstruct the dark matter parameters, and the bias of the reconstructed parameters. We study 36 benchmark dark matter models within the reach of upcoming ton-scale experiments. We find that approximate confidence intervals from a profile-likelihood analysis exactly cover or over-cover the true values of the WIMP parameters, and are hence conservative. We evaluate the probability that unavoidable statistical fluctuations in the data might lead to a biased reconstruction of the dark matter parameters, or large uncertainties on the reconstructed parameter values. We show that this probability can be surprisingly large, even for benchmark models leading to a large event rate of order a hundred counts. We find that combining data sets from two different targets leads to improved coverage properties, as well as a substantial reduction of statistical bias and uncertainty on the dark matter parameters.

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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