Difference - Probability-To-Exceed (PTE) and $\chi^2$ distribution

I would like to understand the difference between the $$\chi^{2}$$ distribution and the Probability-To-Exceed ?

I have to compare 2 data sets A and B and in the article I am reading, they talk about this PTE :

I only know the $$\chi^{2}$$ distribution with $$k=2$$ degrees of freedom :

$$f(\Delta\chi^{2})=\dfrac{1}{2}\,e^{-\dfrac{\Delta\chi^{2}}{2}}\quad(1)$$

and the relation with confidence level :

$$1-CL={\large\int}_{\Delta\chi^{2}_{CL}}^{+\infty}\,\dfrac{1}{2}\,e^{-\dfrac{\Delta\chi^{2}}{2}}\,d\,\chi^{2}=e^{-\dfrac{\Delta\chi_{CL}^{2}}{2}}\quad(2)$$

I don't know how to do the link with the text above.

In the article, they make appear the integral of gaussian whereas in $$(2)$$, I can only make appear a simple integration of exponential (I mean, there is no "$$\text{erf}$$" function appearing unlike into the article).

If someone could tell me the difference between $$\chi^{2}$$ distribution and $$P_{\chi^2}$$ (PTE) ?

UPDATE 1: the context is about astrophysics where I have to compare the consistency of 2 data sets (cosmological parameters) . The method is described below :

Could anyone tell me what's the definition of this Probability-To-Exceed and how to determine it ?

Is it a cumulative function ? How to get the integral of a gaussian in this case (since erf appears) ?

Any help is welcome, regards

• I find this quotation so unintelligible that I suppose a good answer might require referring to the original context. Please, then, tell us the source. – whuber Jan 3 at 21:35
• @whuber The context is about an astrophysical context where I have to compare the consistency of 2 data sets. You can see more in my UPDATE 1 that describes the method – youpilat13 Jan 3 at 23:18
• In the citation, "the expected variance of random variable". The variance is expectation of something, so we have no expected variance. $C_A$ and $C_B$ are matrices, but $C_A + C_B$ is the variance. I think you may need to find other books/papers. – user158565 Jan 4 at 2:29
• @user158565 I think that $C_{tot}$ is the sum of the two covariance matrices $C_{A}$ and $C_{B}$, you don't need to focus of "the expected variance". But what it matters is to know the general method to test independant or non-correlated shared parameters between the 2 data sets with independant expriments $A$ and $B$ : I would like to grasp the subtilities of the method, especially by the formula of $\chi^{2} = (p_{A}-p_{B})^T\,C_{tot}^{-1}\,(p_{A}-p_{B})$ : how can I prove this relation ? – youpilat13 Jan 5 at 23:22
• Moreover, what does mean "the treshold for evidence of tension" for $3\sigma$ and "definitive evidence of tension" for $5\sigma$ ? It would mean that if difference between 2 data sets is greater than $5\sigma$, so it would break all previous estimations, wouldn't it ? But how to compute the "difference between 2 data sets" ? regards – youpilat13 Jan 5 at 23:26