Difference: Brenoorzari (31 vs. 32)

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17/09/2019

Today I've studied the difference between the Delphes CMS cards with and without PU. Apparently, the particles (including the ones coming from PU) have no correction until the end of the calorimeters. After that, the first correction (PU track correction) is applied, which will interfere in the jets (that have another correction: jet PU correction) and in the isolation of electrons, muons and photons. This means, that almost all of the Delphes outputs will have corrections in the end. An interesting feature is that there isn't a module for the FatJets. I guess I'll introduce it by hand and check what is the difference (in practice) to the card without PU. There is a flowchart that shows the steps that Delphes makes while working. It's inside my main folder, and it is named delphes_PU_finished.png.

I've also looked inside the Pythia files to write very clearly what are the processes that it's seeing while simulating the signal processes. In MadGraph syntax, the following 6 different processes were found:

  • q ~q > H* > hs n1 n1, hs > b ~b;
  • q g > H* q, H* > hs n1 n1, hs > b ~b;
  • q ~q > Z'* > hs n1 n1, hs > b ~b;
  • q g > Z'* q, Z'* > hs n1 n1, hs > b ~b;
  • q ~q > hs* hs, hs* > n1 n1, hs > b ~b;
  • q g > hs* hs q, hs* > n1 n1, hs > b ~b;

Particles tmarked with * are the ones that have its mass very close to the m_{Z'} set in MadGraph param_card. It's interesting that only those particles are decaying to the 2 DM, even when we have 2 intermediate dark Higgses.

Today I've started looking the TLimit class to see how to put uncertainties in the BG. Apparently, it's just use the syntax TConfidenceLevel * TLimit::ComputeLimit (Double_t s, Double_t b, Int_t d, TVectorD * se, TVectorD * be, TObjArray * l, Int_t nmc = 50000, bool stat = false, TRandom * generator = 0), where s, b and d are signal, BG and data, respectively, se and be look like the errors per bin of the signal and BG histograms (in my case that will be only a number), I don't know what is l, nmc is the number of montecarlo simulations to do, stat is dependent on the shape of the number of events (if I want to count one tail of the distribution, or none of them) and the generator is the random generator. I've also discovered how to find the results of TLimit with +/- 1 and +/- 2 sigmas (the default is 0, the average). I'll try everything out.

I'm having some problems to implement the BG errors in TLimit. The classes TVectorD and TObjArray are a bit weird, and I'll talk to Thiago tomorrow to see how to do it. In the mean time, I'm thinking if it's suitable to only vary the number of events in the BG for some different values like 10%, 20%, and some others, and see how the CLs variable will change, and how this affects the cross sections.

I can do the same thing with the -2, -1, 0, 1 and 2 sigma expected CLs, converting everything to number of events of signal and than to cross sections (would this be a brazilian plot?).

 

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