ROOT Version: 6.16
_Platform:_MacOs Mojave
Compiler: gcc
Hello Everyone. This might not be the best place to post this question but I canβt find a way out of this problem.
1- bosons have an angular distribution in ΞΈ and π
πΉ(π,π)=34π[0.5(1βπΌ)+(0.5)(3πΌβ1)πππ (π)2βπ½π ππ(π)2πππ (2π)β2βΎβπΎπ ππ(2π)πππ (π)]
With πΌ=0.65,π½=0.06,πΎ=β0.18
I sample some Monte Carlo events from this distribution (50000). Now 0- bosons are uniformly distributed π,π,
that means that the pdf is a constant:
π(π,π)=12π2πππβ[0,π],πβ[0,2π]
Iβm asked to test the hypothesis that the Montecarlo data comes from 1- or 0- distribution. This sounds really trivial of course. I opted for a simple likelihood ratio. Note now that πΉ(π,π) reduces to uniform when πΌ=πΌ0=1./3,π½=π½0=0,πΎ=πΎ0=0:
π=βπ(π,π|πΌ0,π½0,πΎ0) / βπΉ(π,π|πΌ,π½,πΎ)
So basically what I do now is simple taking every π and π in my 50000 events and plug it into each pdf and get the value or the likelihood for that instance to be sampled given the uniform or alternative hypothesis.
Since both values are, by definition of pdf minor than 1, then this multiplication computation exceeds the numerical precision of a long double in c++. What I did then is turn into a logarithm:
πππ(π)=βπππ(π(ππ,ππ|πΌ0,π½0,πΎ0))ββπππ(πΉ(ππ,ππ|πΌ,π½,πΎ))
This value turns out to be:
πππ(π)=β40051βπ=πxp(β40051)
with the method I used before.
I expected a really small value for the statistics since a sample from one of the hypothesis Iβm testing on but can it be this small?
Furthermore how do I compute the rejection region according to the Neyman Pearson Lemma?
Is this the best way do do a statistical hypothesis test in 2 dimensions?
Iβll post some snapshot of the distributions and their fits. Thank you everyone who can help, Iβm kind of desperate ahah.
result_ML_unbinned_2.pdf (104.0 KB)
result_ML_unbinned.pdf (76.3 KB)