Understanding Chi-Squared Plots & CP Violation Ranges

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Understanding chi-squared plots requires assumptions about the behavior of chi-squared distributions, particularly to apply Wilks' theorem, which is crucial for interpreting CP violation ranges in relation to 3 and 5 sigma levels. The discussion highlights that using the chi-squared distribution directly may not be valid without these assumptions, and Monte Carlo simulations could be an alternative approach. The sensitivity plots referenced indicate expected results from experiments, but actual limits will depend on real data fluctuations. The debate surrounding this topic was significant in the neutrino community several years ago, leading to several relevant publications. Overall, the interpretation of these plots is complex and requires careful consideration of statistical principles.
kimmm
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Would you please help me to understand the chi-squared plot the third plot in the bellow list.
How can I relate the cp violation ranges to the 3 and 5 sigma?
(http://inspirehep.net/record/1473193/plots)

 
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A priori, you cannot, you need to make some assumptions on how the ##\chi^2## behaves. Essentially you need the conditions of Wilks' theorem to apply so that the ##\Delta \chi^2## is actually ##\chi^2## distributed. The aternative is deducing the distribution from a Monte Carlo simulation but that is not what is done here.

This question (whether you can use the ##\chi^2## distribution or not) was rather heatedly debated in the neutrino community 4-5 years ago. I wrote several papers on the subject, the most relevant to your question being:
 
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Also, it should be mentioned that these are sensitivity plots. They tell you the typical result you would expect from the experiment. The actual limits will later be set by the actual data, which may have up- or down-fluctuations.
 
Orodruin said:
Thank you so much for the references.
 
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