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arXiv 1101.3296hep-phphysics.data-an
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多维SUSY扫描中轮廓可能性评估的挑战

Challenges of Profile Likelihood Evaluation in Multi-Dimensional SUSY Scans

F. Feroz, K. Cranmer, M. Hobson, R. Ruiz de Austri, R. Trotta

摘要:

超对称理论基本参数的统计推断是一项具有挑战性的积极努力。为此,已经采用了几种复杂的算法。虽然马尔可夫链蒙特卡罗(MCMC)和嵌套抽样技术是面向贝叶斯推断,他们也被用来估计频率置信区间的基础上的轮廓似然比。我们调查的性能和适当的配置的MultiNest,嵌套抽样为基础的算法,当用于配置文件的可能性为基础的分析玩具模型和约束MSSM的参数空间。我们发现,虽然标准配置是适当的贝叶斯后验的准确重建,配置文件的可能性是很差的近似。我们确定了一个更合适的配置文件似然分析,这给了一个很好的探索的配置文件的可能性(虽然在一个更大的计算成本),包括全球极大似然值的识别MultiNest配置。我们的结论是,通过适当的配置,MultiNest是一个合适的工具,配置文件的可能性研究,表明以前的说法,相反是没有充分的根据。

英文摘要:

Statistical inference of the fundamental parameters of supersymmetric theories is a challenging and active endeavor. Several sophisticated algorithms have been employed to this end. While Markov-Chain Monte Carlo (MCMC) and nested sampling techniques are geared towards Bayesian inference, they have also been used to estimate frequentist confidence intervals based on the profile likelihood ratio. We investigate the performance and appropriate configuration of MultiNest, a nested sampling based algorithm, when used for profile likelihood-based analyses both on toy models and on the parameter space of the Constrained MSSM. We find that while the standard configuration is appropriate for an accurate reconstruction of the Bayesian posterior, the profile likelihood is poorly approximated. We identify a more appropriate MultiNest configuration for profile likelihood analyses, which gives an excellent exploration of the profile likelihood (albeit at a larger computational cost), including the identification of the global maximum likelihood value. We conclude that with the appropriate configuration MultiNest is a suitable tool for profile likelihood studies, indicating previous claims to the contrary are not well founded.

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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