使用贝叶斯推断分析KATRIN数据
Analysis of KATRIN data using Bayesian inference
摘要：
KATRIN（KArlsruhe TRITUM NEUTRino）实验将分析氚β光谱，以确定中微子的质量，灵敏度为0.2 eV（90% C.L.）。这种测量中微子质量绝对值的方法只依赖于能量守恒原理，与宇宙学和无中微子双β衰变相比，在某种意义上可以称为模型无关。然而，模型无关仅指标准模型的最小扩展的情况。因此，我们还应该分析非标准耦合的数据，例如右手中微子或无菌中微子。作为一种替代的频率最小化方法在分析早期的实验在美因茨和特罗伊茨克，我们一直在研究马尔可夫链蒙特卡罗（MCMC）方法，这是非常适合探测多参数空间。我们发现，在COSMOMC包中实现KATRIN卡方函数（一种使用贝叶斯参数推断的MCMC代码）非常好地解决了手头的任务。
英文摘要：
The KATRIN (KArlsruhe TRItium Neutrino) experiment will be analyzing the tritium beta-spectrum to determine the mass of the neutrino with a sensitivity of 0.2 eV (90% C.L.). This approach to a measurement of the absolute value of the neutrino mass relies only on the principle of energy conservation and can in some sense be called model-independent as compared to cosmology and neutrino-less double beta decay. However by model independent we only mean in case of the minimal extension of the standard model. One should therefore also analyse the data for non-standard couplings to e.g. righthanded or sterile neutrinos. As an alternative to the frequentist minimization methods used in the analysis of the earlier experiments in Mainz and Troitsk we have been investigating Markov Chain Monte Carlo (MCMC) methods which are very well suited for probing multi-parameter spaces. We found that implementing the KATRIN chi squared function in the COSMOMC package - an MCMC code using Bayesian parameter inference - solved the task at hand very nicely.
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