Re-interpreting ATLAS SUSY search results

>>> t.Draw("MissingET.MET")
>>> t.Draw("Jet_size")

Run larger dataset

Change the Cards/run_card.dat file and increase the number of events to 10 thousand (10000 = nevents), and then repeat the generation (MadEvent + Pythia + PGS). The results should be saved in run_02

3) SUSY ANALYSIS

Implementing the analysis code

Now that we have events generated we need to implement the analysis code. The analysis is described in the paper.

This can be quite some work. For this tutorial you can use the following example code which implements an approximation of SR D. The python code can be found here ATLAS_SUSY_0lepJetsMET_search2011data.py.txt.

• downloaded it and place it in your MSSM_GOGO1 directory,
• remove the .txt suffix from the name,
• the script assumes a local ntuple file with name pgs_events.root. Either put a symbolic link to your pgs root file (e.g. ln -s Events/run_02/tag_1_pgs_events.root pgs_events.root) or change the script to point to your file directly,
• run the script like this python -i ATLAS_SUSY_0lepJetsMET_search2011data.py

It should open your PGS ntuple file, loop over the events and apply the event selection. In the end you should see one plot of the final effective mass (meff) distribution and event selection efficiencies printed out on the screen:

N_LArVeto: 7095  eff.: 0.80079006772
N_MET130: 5344  eff.: 0.60316027088
N_JetQuality: 5032  eff.: 0.567945823928
N_Jet1Pt: 4730  eff.: 0.533860045147
N_Jet4: 3188  eff.: 0.359819413093
N_dPhi: 2668  eff.: 0.301128668172
N_METoverMeff: 1926  eff.: 0.217381489842
N_Meff: 345  eff.: 0.0389390519187
Info in <TCanvas::MakeDefCanvas>:  created default TCanvas with name c1

Validation of analysis code

We can compare the full event selection efficiency of the above example, i.e. signal region D, with public ATLAS information. This link shows for each grid point (of the given model) the ATLAS acceptance times efficiency values. Find our SUSY model, and compare the value for SR D (the agreement should be at the level of 10%). Note that as of Aug-22 2012 the AxEps numbers above on HEPdata are off by a factor 1000. This will be corrected.

In principle, one can also use the ntuple before PGS (i.e. from madgraph + Pythia). This gives the pure acceptance. For recent ATLAS SUSY searches acceptance and efficiency are each made public. Thus, one can validate the truth-level (or to be more precise hadron-level) quantities, and then the detector simulation separately.

4) INTERPRETATION

Cross sections

Find the next-to-leading-log cross section for our process (gluino pairs, with gluino mass of 562.5 GeV) on this webpage.

Test for exclusion

Calculate the expected number of signal events in the signal region (i.e. cross section times acceptance times efficiency times integrated luminosity). This should come out to be about

predicted events in SR D: 44.5

The paper states the excluded values of are 22 fb, 25 fb, 429 fb, 27 fb and 17 fb, respectively, at the 95% confidence level.

Calculate yourself, and check whether it is above or below the limit. Is the point excluded?

Compare your finding with the official exclusion plot for the given simplified model.

SUMMARY

In this tutorial you have:

• generated events for one SUSY model,
• run a (too) simple analysis code for one signal selection of the ATLAS SUSY search in the 0-lepton channel,
• validated the analysis code to first order,
• and checked whether the given model is already excluded.

OUTLOOK

Generate and run other BSM model grids to test how much model space is already excluded.

-- TillEifert - 17-Aug-2012