SUSY Roberto Rossin (UCSB)

1. SUSY Roberto Rossin (UCSB) USCMS meeting 8-9 May 2009

2. mSUGRA vs GMSB vs … • Not really the topic of this talk. • Benchmark points have been used extensively to study the physics reach of CMS for various SUSY hypotheses • Good, but not the only possible, nor optimal, approach to design a search • Presented here: few searches based on simple signatures defined by their physics objects (Reference Analyses) • As model independent as possible • As data driven as possible • Lot of work has been done and is ongoing, only a small part of it will be touched in this presentation

3. Outline • Searches and methods for data driven backgrounds and • efficiencies estimations • most of this talk Simplified models based on topologies Detector commissioning from SUSY perspective

4. g + jets +MET (1) • GMSB if you like, but it is actually a simple final state. • 2 photons • Jets • MET • Focus on the first and last bullet • Measure photon efficiencies on data: Z->ee • e/g definition as similar as possible • Estimate the MET contributions from: • QCD • EWK • CR and beam halo

5. g + jets +MET (2) • Efficiencies via Z->ee (tag & probe) • iso variables chosen to be similar for iso electrons and g • tag: pixel match and tight requirement on h cluster width • probe: measure iso and pixel matching efficiencies • Isolation efficiency • check (and correct) for biases (MC vs T&P) • signal will have higher jet activity • MC will be validated on data and account for this

6. g + jets +MET (3) • Pixel matching (in)efficiency • data again: ee vs eg vs gg • EWK backgrounds • Wg->eng is a background if the ele has no pixel match • Select e+g and weight by the pixel match inefficiency factor fe->g/(1- fe->g) • The estimation is subtracted to the background, leaving only the QCD background contribution

7. g + jets +MET (4) • QCD backgrounds • assuming no signal at low MET to normalize • two control samples considered for the modeling of the MET spectrum • fake-fake (both EM objects fail at least one iso) • Z->ee • control samples spectra are different from the gg one • reweight to account for different hadronic activity • closure tests successful • no impact on signal • MET prediction

8. (di)jet +MET with alphaT (1) SUSY: squark-squark production with Mgluino > Msquark Squark decaying to quark+LSP • Final state: di-jet+MET • 2 high pT jets • MHT = - (pTj1+ pTj2) • not aligned w/ jets • lepton veto • third jet veto • Main backgrounds: • QCD di-jet • Z->vv +jets • W+jets, Z->ll and top when leptons are lost

9. (di)jet +MET with alphaT (2) QCD background: Randall & Tucker-Smith suggest to use a kinematics variable • for QCD di-jets: aT=0.5 (or smaller if mis-measured ET) • exploits that for QCD jets need to be back-to-back and of equal magnitude • for real MET aT can be greater

10. (di)jet +MET with alphaT (3) • Data driven method to estimate the backgrounds: • Z->nn + jets • W -> nl, Z->ll, top • QCD (again) • ABCD method • 3 out of 4 regions must be signal free • need 2 uncorrelated variables for all backgrounds • aT and h of the leading jet

11. (di)jet +MET with alphaT (4) Data driven method to estimate the backgrounds: results w/o signal (closure test) w/ signal (LM1) • Extra checks: • Check the background flatness in h on data by relaxing the HT and as a consequence diluting the (potential) signal • Alternative data driven Z->nn+jets estimation from W->nl+jets Update: aT definition extended to multi-jet events. Ongoing.

12. MET modeling in V + jets (1) If V=Z/g the main source of MET are the jets (If V=W the lepton can parameterize the n ) Idea: model the jet system using QCD events V • Algorithm: • Construct MET templates from the QCD sample based on 2 variables: Nj and HT=SjETj • For each V+jets event pick MET template according to Nj and HT

13. MET modeling in V(=Z) + jets (2) Z+jets: easy. Blue: measured Red: predicted Nj=2 Nj=3 Nj=4 Measured/Predicted

14. MET modeling in V(=W) + jets (3) • In lepton+jets+MET the dominant background is SM W(ln)+jets and semileptonic ttbar+jets • To predict MET need to know 2 ingredients: • MET from resolution effects • neutrino spectrum -> use the charged lepton spectrum Nj=4 Nj=2 Nj=3 Blue: measured Red: predicted Prediction is 20% higher for Nj=2 due to W+jets contribution with W polarized in the transverse plane after event selection. Can be corrected

15. Blue: measured Red: predicted Purple: prediction in presence of LM1 Nj=3 MET modeling in V(=W) + jets (4) What if BSM is there? Q: Can it bias the prediction? A: No Regimes where the method can become biased: • jet inefficiency • bias when very high pT jet is lost. • non Gaussian jet resolution • bias when large fluctuation. • hot cells • tail overestimation. • energy scale offset • bias, coherently adding/removing energy. Solution: calibration <10% or search in the direction ortogonal wrt the V Solution: apply quality selection criteria to remove badly mis-reconstructed events.

16. Simplified models for search design/optimization (1) ~ constant all lines on-shell Full model calculation Parameterization * spins couplings off-shell states … branching ratios

17. Simplified models for search design/optimization (2) Model Parameterization “blobs” represent dynamics that are parameterized by one rate and possibly an additional shape parameter on- and off-shell masses several couplings control both kinematics and rates Production contributions: Associated q-g: intermediate q and g Same sign q: g and 4 neutralinos Off-shell particle do not appear, their effects present in the rates ~ ~ ~ gauginos do not appear in the OSET ~

18. Simplified models for search design/optimization (3) Very simple Monte Carlo scripting language Quantum numbers / mass sup : charge=1 color=1 mass= 800 New particle sup > up LSP Decay mode g g > sup sup : matrix 1 Production mode • Other particles, production processes and decays can be added similarly • No cross section calculation, reweight applied at analysis level

19. Simplified models for search design/optimization (4) • Example. Simple SUSY-like system with: gluino, sup, LSP Final states: 2-6 jets, MET

20. Simplified models for search design/optimization (5) • Simple exercise: • Mass grid generated with variable Mgluino, Msup MLSP • Cross sections calculated w/ Prospino • Using QCD only as background • Using MET/HT as discriminator • Punzi significance as figure of merit • Optimizing the leading jet transverse momentum pTj • Grid example for fixed MLSP=100 • When there is direct decay to LSP (and since no systematics are considered) the optimization suggests to cut very hard

21. Simplified models for search design/optimization (6) • Significance grid for fixed MLSP=100 • suggests which physics parameters are in the reach of the search • Significance curves VS cut for several parameters • suggests whether the optimized cut is valid/stable as function of parameters

22. Simplified models for search design/optimization (7) • Advantages: • focus on the topologies you are interested on • deal with (few) physics parameters (masses) • very simple scripting language • ideal for (physics) parameter scanning, grid generating tools available • Caveats: • you get what you put in. No Lagrangian has been calculated for you • might oversimplify the model • no “signal” events will be present in control samples with different topologies • the speaker loves it and he is biased...

23. HCAL noise impact on MET(1) • Impact on MET spectrum (tail) from noisy HCAL channels can be estimated by looking at CRAFT data • Collect events with a MinBias HCAL trigger • Get noise MET spectrum (and cross section) • Mix it with MC QCD events • split noise and QCD in MET bins, then “sum” • Approximate approach • Mixing done at tower level, not at digi-level (DataMixingModule) • No timing: everything considered synchronous

24. HCAL noise impact on MET(2) CRAFT MET spectrum (metNoHF) MC QCD before mixing • Interested in MET>200GeV • mixing only bins that can contribute above threshold

25. HCAL noise impact on MET(3) Results (before any cleaning cuts) • Remarks • bin with lowest initial MET is the only relevant because of the steeply falling spectrum • MET and MHT behave very similarly wrt the noise

26. HCAL noise impact on MET(4) • Analysis preselections • Jet 1,2,3: pT > 50 GeV, |h| < 2.5 • Df ( Jet 1,2,3 ; MET ) > 0.3 • SUSY cleaning cuts • Jet 1,2,3: 0.05 < EMF < 0.95 • Df (MPT ; MET ) < 0.75 + • Looks good. Work is ongoing, still have to: • apply the real mixing at digi level • apply the HCAL clean-up routines

27. Conclusions • SUSY group is prepared for the data coming this year • developed/under development analysis techniques which rely on data driven approaches • independent/multiple background estimations will provide further confidence in case of signal observation • quasi model independent approaches are evaluated • the group is also working on the detector commissioning • recall: we are picky customers • What has not been presented here: A LOT • There are 8 Reference analysis groups working on different topologies to cover as much parameter/model space as possible • Apologies for all the work which has not been cited • The structure of SUSY in Reference Analyses allows to naturally build cooperation among groups facing similar issues (workshop next week)