Tentative flow chart of CMS Multi-Muon analysis 1 – DATASETS 2 - RESOLUTIONS 3 – FAKE RATES 4 – NUCLEAR INT MODEL 5 – IP TEMPLATES MODEL 6 – SAMPLE COMPOSITION FITS 7 – EXTENSION OF IP TO “SIGNAL REGION” 8 – SEARCH FOR ADDITIONAL MUONS 9 – NEW PHYSICS MODELS

1 - DATASETS DIMUON TRIGGERED DATA: “DIMUON” –must try to avoid HLT enforcement of pixel-seeded tracks for muon candidates –Reconstruct TIB/TID-seeded tracks in input to GM definition; –Require last station to triggered muons –Apply Pt cut enforcement on muons –Apply |  | cut (ex. |  |<2.4) on both legs –Apply quality cuts on event: Good run  z  <xx cm  2 < yy... SINGLE MUON DATA: “INCMU” –Reconstruct TIB/TID-seeded tracks –Reconstruct GM candidates similarly as above QCD JET TRIGGERED DATA (or Min Bias): “QCD” –Reconstruct TIB/TID-seeded tracks –Reconstruct GM candidates similarly as above MONTE CARLO SAMPLES: –QCD, with trigger simulation –heavy flavors, with DIMUON and INCMU trigger filters TASKS: 1A) Understand how to reconstruct the data with special tracking, and verify that V particles are found with large efficiency 1B) Decide “standard” muon cuts 1C) Get ready to produce sizable samples of MC according to our needs, by putting together cfg files and cards suitable to the task, and trigger simulation

2 - RESOLUTIONS Search for J/psi and Y states in DIMUON and INCMU data Extract Pt resolution from scale fits (MuScleFit) of all resonances Extract IP resolution from sidebands-subtraction method on Y states Verify MC simulation TASKS: 2A) Construct filter for resonances 2B) Construct macro which extracts IP resolution and compares to MC 2C) Scale fits to low-mass resonances

3 – FAKE PROBABILITIES Study two-prong hadronic decays in QCD data: K  ,   p ,   KK, D  K  –Match legs to muon candidates –Extract P fake (  ), P fake (K), P fake (p) as a function of track Pt and rapidity –Check flatness of P fake vs IP, R dec –Verify whether rates are consistent with QCD MC simulation TASKS: 3A) prepare macros that extract fake rates from all resonances 3B) show that D can be found 3C) Put together tool to verify fake rates with Monte Carlo simulation

4 – NUCLEAR INT MODEL Find 2-pronged vertices in QCD data Attach additional tracks with simple chisquared method Match multiplicity and Rdec distributions with MC expectations – obtain scale factor Extract prediction for single- prong component from MC as ratio WRT reconstructed 2- prongs Determine hadronic composition of charged tracks from MC Can then extrapolate on DIMUON data using obseved 2- prongs there TASKS: 4A) Put together tool to add tracks to 2-pronged vertices found by V0Producer 4B) Verify feasibility of method 4C) Verify uncertainties due to knowledge of hadron composition

5 – IP TEMPLATES MODEL –b template: search for D  K  signal close to muon in INCMU sample, extract IP of muon from b with sidebands-subtraction method Check with MC simulation Can derive expected b fraction in DIMUON data by counting D signal as a x-check –c template: Can try to search for D  K  signal opposite to muon in INCMU sample, deriving IP distribution of charm-enriched data; required b- component subtraction may make this difficult in practice Or can get from MC simulation Other ideas needed –Punch-through & DIF: Get IP distributions of muons from application of P fake ( ,K,p) to expected mixture of hadrons in QCD MC simulation; check result on QCD data; use same method on DIMUON data  may obtain both shape AND normalization (within largish error) which can be useful in 2D fit to IP distributions –Nuclear interactions component: verify & (if needed) rescale amount of N.I./evt with different multiplicities as estimated from MC, using vertices found in QCD data & MC Apply P fake to N.I. tracks & extract IP distribution and expected normalization –Prompt component: Get from Y resonances TASKS: 5A) Find D signal in B sim 5B) Understand how to extract c template 5C) Apply fake param to QCD simulation and verify that IP distribution agrees

6 - SAMPLE COMPOSITION FITS Once all templates (with estimates for their normalization in case of PT and NI) are ready, one can do a 2-D fit to DIMUON data and extract the various components, in a controlled region: –IP<0.5cm –May want to require innermost pixel layer has been hit by muon tracks –Can check results for b-fraction using D signal –Should be able to verify fake and NI component by relaxing constraints in global fit TASKS: 6A) Put together fitter 6B) Develop filter for track pairs not hitting inner pixels

7 - EXTRAPOLATION Once sample is understood (might require a lot of work!), can extrapolate results to larger IP region and/or no hit in innermost pixel layer –Verify shape and normalization of events with large IP –Can study quality of muons in this “signal region” –Characterization of sample in terms of kinematics TASKS: 7A) Understand how to best define signal box 7B) Perform pseudoexperiment to verify sensitivity to unknown component

8 – SEARCH FOR ADDITIONAL MUONS Go back to low-IP sample and verify prediction for number of additional muons Predict number and IP distribution of additional muons in sample with large IP of triggering muons –For prompt muons, use rate of additional muons in Y events –For b- and c- component, use real muon estimate of MC and fake rate prediction applied to all tracks –For PT and NI, use method already outlined above TASKS: 8A) Determine sensitivity with pseudoexperiment

9 – NEW PHYSICS MODELS Generate MC sample modeling suitable new- physics hypothesis –Reconstruct and filter with DIMUON trigger simulation and preselection cuts –Verify sensitivity of signal boxes to NP model –Verify sensitivity of counting method to NP model TASKS: 9A) Generate sample 9B) Study how search strategy can be improved / tailored to considered new physics signal