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New MT method to remove SUSY contaminations

New MT method to remove SUSY contaminations. CSC Note 1&2 : 27 Aug 2007 Ginga Akimoto, Y. Kataoka , S. Asai. Table of Contents. Original MT methods (no SUSY) SUSY contamination to control sample new method to remove SUSY contamination conclusion and outlook.

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New MT method to remove SUSY contaminations

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  1. New MT method to remove SUSY contaminations CSC Note 1&2 : 27 Aug 2007 Ginga Akimoto, Y. Kataoka , S. Asai

  2. Table of Contents • Original MT methods (no SUSY) • SUSY contamination to control sample • new method to remove SUSY contamination • conclusion and outlook

  3. 1. Background Estimation ( the case : no SUSY ) • These figures show the mET distribution for MT>100GeV and MT<100GeV. • The shape of distribution of MT>100 is the same as Control Sample (MT<100). mET ( Control Sample : MT<100GeV ) mET ( Signal Region : MT>100GeV ) mET (MT>100) and scaled Control Sample detail has already shown in the previous many meetings. MT method works well if SUSY dose not exist. # of mET>300 estimate BG 17.4±4 real BG 15.8±4 10% accuracy

  4. [fig.2] mET ( Signal Region : MT>100 ) [fig.1] mET ( Control Sample : MT<100 ) 2. Background Estimation ( with SUSY ) • If SUSY exists, SUSY contamination contributes to control sample. • [fig.1] shows mET distribution (SU3) of Control Sample. Hatched area shows the background, bold blue line is SUSY, bold black line is Observed Signal (SUSY+BG). The shape become harder due to SUSY contamination. • [fig.2] shows mET distribution (SU3) of Signal Region (MT>100). Red points with error bar are estimated background. The background is overestimated.

  5. 2. Background Estimation II (with SUSY) • Not only the Shape but also normalization factor is also altered by SUSY contamination. • This figure shows mET distribution of Signal Region (MT>100) . Hatched histogram shows BG , blue shows SUSY and black line shows the sum of BG and SUSY. SUSY contamination can not be negligible even in low mET region and make overestimate of BG as shown in the figure. contribution of SUSY background

  6. 3. Correction of Background Estimation • correction of normalization • Normalization factor is obtained in the region of mET=100-115GeV instead of 100-200GeV • SUSY effect can be reduced, we use lower region of mET=70-115 if trigger effect is taken into account correctly. (now, no trigger information we don’t use mET=70-100GeV) • correction of Control Sample • SUSY contamination is removed from Control Sample as follows. • This figure show mET distribution of SUSY signal. Line shows MT>100GeV point and points with error bar show (normalized) MT<100GeV. Distribution is similar for MT>100GeV and MT<100GeV. This is valid for various SUSY point. [figure] Similarity : normalized Control Sample (points with error bar ) and Signal Region [MT>100GeV] (Bold Line)

  7. [fig.1] MT distribution Signal Region Control Sample 3. normalization factor of the SUSY component • Shape of SUSY signal in Control Sample can be estimated in MT>100GeV region,but we need a new normalization factor. • The MT distributions of SUSY signal are similar to each SUSY points [fig.1], so the events ratio (Control Sample:MT<100) over (Signal Region : MT>100) is almost constant for varies SUSY point. [table] • the event ratio (normalization factor) [# of MT<100]/[# of MT>100]~0.6 SUSY component of (Control Sample) is similar to about 0.6 times scaled (Signal Region)

  8. #(0-100) CS 2 *#(100-150) #(100-200) SUSY point true ratio estimated SU1 : Coannihilation 0.583 0.614 SU1 173.916 164.732 136.585 SU2 : Focus Point 0.556 0.684 SU3 : Bulk 0.591 0.612 SU2 21.4488 19.5048 16.2 SU4 : Low Mass 0.687 0.689 SU3 323.865 283.926 240.641 SU4 3530.3 2986.78 2240.8 tt-bblnln 169.22 165.021 121.858 tt-bblnqq 1465.52 55.3218 28.6766 W 668.41 25.1715 15.2572 3. an experimental way to estimate the SUSY ratio 0.6 • If we believe MC we can use this ratio “0.6” . This normalization factor can be estimated with out of MC information as follows. • [fig.1] and [table.1] show that the lowest Signal Region (MT=100-150GeV) still has the same amount of SUSY events per MT as Control Sample (MT<100GeV) , because SUSY distribution is almost flat. • With information of the region [MT=100-150] , we can estimate the amount of SUSY in Control Sample and the event ratio to Signal Region (cut off mET<200 region : to reduce BG contamination ). The estimated value in each Model is in [table.2]. [table.2] estimated normalization factor [fig.1]Transverse Mass (MT) [table.1] # of SUSY events in each MT bin

  9. Leading Jet PT 3. New Background Estimation (SU3) • With this new method we can obtain the correct distributions for various variables. These figures show new estimated background of MT>100 region. • Red points with error bar shows estimated background , hatched area is real background and green line is old background estimation. mET @ Signal Region (MT>100) # of mET>300 estimate BG 18.6±4 real BG 15.8±4 Lepton PT There is some discrepancy in PT of lepton distribution. Estimated distribution is softer than truth distribution.

  10. 3.SUSY point dependence ( mET ) SU2 : Focus Point SU1 : Coannihilation 16.1±4 16.0±4 Red value is the # of estimated BG in the region [mET>300] , (real BG : 15.9±4) SU3 : Bulk SU4 : Low Mass 18.7±4.5 71.5±8.5

  11. 3.Error and applicable range of this method • large deviation (a factor 5 to 6) at SU4 (Low Mass)  mainly normalization factor : even in the new region (mET=100-115GeV), Control Sample includes substantial SUSY events.  needs another method. • Other Points ( about 20%) model uncertainty and error of the SUSY events ratio estimation (10%) , similarity between Control Sample and Signal region (10%), normalization constant (less than 10% and can reduce it). SU4 : Low Mass

  12. 4.Conclusion & Outlook • Conclusion • Original methods overestimate the background by a factor two to three. • With this new MT method, the background of Signal Region (MT>100) is correctly estimated (about 20% accuracy). • Outlook • application to [No-Lepton Mode] and [Di-Lepton Mode] • estimate the effect of lepton efficiency • proceed to var.12 and Full Simulation • estimation : the systematic error

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