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Mass Variable Technique for GMSB Signal Separation

Develop a new technique for mass variable calculation to enhance GMSB signal separation from background noise, allowing for better analysis and signal yield optimization. The method involves generating photon and gravitino momentum distributions, mass likelihood calculation, and de-convolution for improved distribution accuracy.

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Mass Variable Technique for GMSB Signal Separation

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  1. QCD Control Sample Shilei Zang University of Colorado, Boulder GMSB Meeting, 1 Aug 2008

  2. γγ • HLT; trkIso<9; HoE<0.1; not electron; at least 2 γ; • Pt1>90; Pt2>30; (dE>0) • eγ • HLT; trkIso<9; HoE<0.1; • Electron: haveSeeds && # of track (Pt>1.5; ΔR<0.1)>=1 • at least 1e1γ; • Pt1>90; Pt2>30; (dE>0) • γγ control • HLT; trkIso<9 or trkIso>12; HoE<0.1; not electron; at least 2 γ; • Pt1>90; Pt2>30; (dE>0); do not satisfy γγ • eγ control • HLT; trkIso<9 or trkIso>12 ; HoE<0.1; at least 1e1γ; • Pt1>90; Pt2>30; (dE>0); do not satisfy eγ

  3. γγ vs. γγ-control γγ vs. γγ-control GMSB Bkg γγ vs. eγ • 1/fb • Blue: γγ • Red: γγ control (or eγ) • γγ : 80 signal; 2698 bkgs • γγ control : 55 signal; 3157 bkgs • eγ : 61 from bkgs Bkg

  4. Jet resolution: • MET resolution: Signal yield: Control sample:

  5. MET Bkg MET GMSB k=5.0 pt1>80, pt2>20 k=5.0 MET vs. for background (left) and for GMSB signal (right). • has powerful separation. • almost no correlation with Pt1, Pt2, trkIso. (but MET has.)

  6. Signal yield: Control sample: • Electron: haveSeeds && # of track (Pt>1.5; ΔR<0.1)>=1 Selected Sample (gumbo & chowder) • HLT; HoE<0.1; not electron; at least 2 γ; • Pt1>80; Pt2>20; Di-photons: Control sample: trkIso <9 >12 trkIso/Pt <0.08 >0.1 Pt1 >120 <90 Pt2 >60 <30 Pt1& Pt2 Pt1>110&&Pt2>50 Pt1<90 or Pt2<30

  7. Pt1 Bkg Pt1 GMSB trkIso1 trkIso1 Pt2 Pt2 Bkg GMSB trkIso2 trkIso2

  8. MET Bkg MET GMSB trkIso1 trkIso1 MET MET Bkg GMSB trkIso2 trkIso2

  9. MET Bkg MET GMSB trkIso1/Pt1 trkIso1/Pt1 MET MET Bkg GMSB trkIso2/Pt2 trkIso2/Pt2

  10. MET Bkg MET GMSB Pt1 Pt1 MET MET Bkg GMSB Pt2 Pt2

  11. MET MET • HoE<0.1; track un-match • Pt1>80; Pt2>20; • Blue: trkIso<9 (4828 evts) • Red: trkIso>12 (550 evts) • HoE<0.1; track un-match • Pt1>80; Pt2>20; • Blue: trkIso/Pt<0.08 (3011 evts) • Red: trkIso/Pt>0.1 (771 evts) trkIso trkIso/Pt

  12. MET/σ(γPt) MET/σ(γPt) Bkg GMSB trkIso1 trkIso1 MET/σ(γPt) MET/σ(γPt) Bkg GMSB trkIso2 trkIso2

  13. MET/σ(γPt) MET/σ(γPt) Bkg GMSB trkIso1/Pt1 trkIso1/Pt1 MET/σ(γPt) MET/σ(γPt) Bkg GMSB trkIso2/Pt2 trkIso2/Pt2

  14. MET/σ(γPt) MET/σ(γPt) Bkg GMSB Pt1 Pt1 MET/σ(γPt) MET/σ(γPt) Bkg GMSB Pt2 Pt2

  15. MET/σ(γPt) Bkg MET/σ(γPt) GMSB Pt1+Pt2 Pt1+Pt2 Pt1 Pt1 Bkg GMSB Pt2 Pt2

  16. MET/σ(γPt) MET/σ(γPt) • Blue: trkIso<9 (4828 evts) • Red: trkIso>12 (550 evts) • Blue: trkIso/Pt<0.08 (3011 evts) • Red: trkIso/Pt>0.1 (771 evts) trkIso trkIso/Pt

  17. MET/σ(γPt) MET/σ(γPt) • Pt1>90; w/o trkIso • Blue: Pt2>60 (3098 evts) • Red: 20<Pt2<30 (2238 evts) • Pt2>30; w/o trkIso • Blue: Pt1>120 (2452 evts) • Red: 80<Pt1<90 (2355 evts) Pt1 Pt2

  18. w/o trkIso • Blue: Pt1>110 && Pt2>50 (2249 evts) • Red: Pt1<90 or Pt2<30 (5410 evts) MET/σ(γPt) Pt1 & Pt2

  19. MET/σ(MET) MET/σ(MET) GMSB Bkg Pt1 Pt1 MET/σ(MET) MET/σ(MET) Bkg GMSB Pt2 Pt2

  20. MET/σ(MET) MET/σ(MET) • Blue: trkIso<9 (4828 evts) • Red: trkIso>12 (550 evts) • Blue: trkIso/Pt<0.08 (3011 evts) • Red: trkIso/Pt>0.1 (771 evts) trkIso trkIso/Pt

  21. MET/σ(MET) MET/σ(MET) • Pt1>90; w/o trkIso • Blue: Pt2>60 (3098 evts) • Red: 20<Pt2<30 (2238 evts) • Pt2>30; w/o trkIso • Blue: Pt1>120 (2452 evts) • Red: 80<Pt1<90 (2355 evts) Pt1 Pt2

  22. MET/σ(γPt) MET Di-photons; 1/fb MET/σ(MET) Separation: MET/σ(γPt) > MET > MET/σ(MET)

  23. The best choice now: MET/σ(γPt); Pt1, Pt2 together for the control sample (we can get enough control events!) MET/σ(γPt) MET/σ(γPt) Pt1 & Pt2 Di-photons; 1/fb

  24. A new technique for mass variable (stopped for the moment) • Momentum of two photons (known) • Momentum of two gravitinos: P1x, P1y, P1z; P2x, P2y, P2z. (unknown) • MET: METx, METy. (known) Generate P1x, P1y, P1z; P2x, P2y, P2z distributions according to GMSB MC truth. (take all GM1b-GM1g GMSB simulated events for this.) sample A. For each event i (not in sample A; already passed all cuts: iPt1>80, iPt2>20, iMET>80), use all events in sample A with |Pt1-iPt1|<ic1, |Pt2-iPt2|<ic2, |MET-iMET|<ic3 to calculate 4 neutrilino-mass variables: Photon1 Photon2 Photon1 Photon2

  25. Require |mass(j)-mass(k)|<mass-cut (j, k=1, 2, 3, 4) • For each event i, calculate the mass likelihood: • Take the maximal point (maximal likelihood) as the mass of event i. • For all events, we get themass distribution. • Between step 3) and step 4), we can also de-convolute the mass(j) distribution to get a narrower mass distribution, this may recover some information and improve the analysis. • Narrow distribution for GMSB signal • Wide distribution for Background • Treat GM1b-GM1g at the same time (parameter independent) • Can maximize the final significance. Good properties:

  26. New Mass (preliminary) GMSB (GM1e) Bkg GMSB GMSB formula mass MET Pt1>90; Pt2>30 Pt1>90; Pt2>30

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