Upper limit on BF using pyhf

[Giulia Frau]

Hi all, I am trying to use pyhf to set an upper limit on the branching fraction of a very rare decay. The idea is to use the function `pyhf.infer.intervals.upperlimit` which takes as input some data and a model. Given that we are performing a blinded analysis, our observed data corresponds to the background, defined in the model together with the signal. Again, as the analysis is blinded I don’t have a value for the signal. What I have is alpha or the so-called single event sensitivity which is related to the number of signal events by the formula: BF = Nsig*alpha. I read the documentation about the modifiers, which seems to be the way to add a normalization factor to the signal events, but it’s not clear to me how to pass these alphas so that they can act as I want. Moreover, the limit is to be evaluated in 16x8 bins for each of the three data-taking years. Do you happen to have any examples about my case? Thanks for your help. 

Giulia

[Alexander Held]

Hi everyone,

This question is being followed up on in the pyhf discussions forum on GitHub, you can find the thread here: https://github.com/scikit-hep/pyhf/discussions/1831.

Best,

Alex

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From: scikit-h...@googlegroups.com [scikit-h...@googlegroups.com] on behalf of Giulia Frau [giulia...@gmail.com]
Sent: Tuesday, March 29, 2022 15:23
To: Scikit-HEP forum
Subject: Upper limit on BF using pyhf

Hi all, I am trying to use pyhf to set an upper limit on the branching fraction of a very rare decay. The idea is to use the function `pyhf.infer.intervals.upperlimit` which takes as input some data and a model. Given that we are performing a blinded analysis, our observed data corresponds to the background, defined in the model together with the signal. Again, as the analysis is blinded I don’t have a value for the signal. What I have is alpha or the so-called single event sensitivity which is related to the number of signal events by the formula: BF = Nsig*alpha. I read the documentation about the modifiers, which seems to be the way to add a normalization factor to the signal events, but it’s not clear to me how to pass these alphas so that they can act as I want. Moreover, the limit is to be evaluated in 16x8 bins for each of the three data-taking years. Do you happen to have any examples about my case? Thanks for your help. 

Giulia

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