Trigger study for GMSB with photons

1. Shi-Lei Zang On behalf of the CMS GMSB group APS April Meeting May 5, 2009 Trigger study for GMSB with photons

2. GMSB with Photons • NLSP  LSP + photon • Prompt decay • Experimental signature • high pT photons • large MET due to gravitinos • multi-jets g jet q q jet … p p q … jet q jet g

3. Datasets • Summer 2007 samples, generated with Pythia6, 14TeV • GMSB signal: • Five GMSB samples with different Λ parameter • Background: • Photon+jets (all pT_hat bins) • QCD jets (all pT_hat bins) • Wenu, Zee • With CMSSW_1_6_7; optimized for the luminosity of 1032 cm-2s-1.

4. HLT Paths for Photons • Optimizethe two triggers: EM-High-Et and EM-Very-High-Et. • ECAL (HCAL) means Electromagnetic (Hadron) Calorimeter.

5. Optimize Trigger Thresholds • How to optimize the trigger thresholds with figure of Efficiency vs. Rate in an objective way ? • In Physics Analysis Threshold, how to set? • Selections are optimized withsome statistics: error of BR; Significance; 90% CL limit, etc. • In mass measurement (e.g. top mass measurement),minimize the mass uncertainty.

6. log(ε)/log(b) method • N events, the amount of information :log N. • log(ε)/ log(b) as the optimization criteria. • The smaller log(ε)/ log(b), the better.  to satisfy the resources (electronic readout, storage, CPU, etc.). • It’s blind analysis, since log(ε)/ log(b) depends only on the amount of information. (1.,1.) a=0.01 ε a=0.1 • Optimize a selection with all the other selections applied. a=0.2 a=0.3 a=0.5 a=0.7 ε>ba a=1.0 log(ε)/ log(b) <a b (0.,0.)

7. log(ε)/log(b) vs. Cuts (default EM-High-Et) • ITrack is better than IEcal and IHcal. 0.129 0.035 Work in Progress 0.102 0.017 • Each figure is plotted with other cuts applied.

8. Optimization Results • Optimized two triggers: • EM-High-Et (oH): Et>60GeV, Itrack<2 • EM-Very-High-Et (oVH): Et>120GeV • Propose to use: above two plus double photon trigger (D) for our physics. 2.14 Hz • 98.5% events have signal photons at generator level; after SusyAnalyzer, only 93.5% events have reconstructed photons.

9. Modification ITrack<2 can cut out most of the converted photons. ITrack<2 cost significant loss in electron efficiency for other physics (duplicate tracks, or converted photons). We need control sample for background study, which needs enough isolation space between online triggers and offline selection. In offline analysis, we use high Et cuts on photons (1st>90GeV; 2nd>30GeV).  we do not need loose Et cut in triggers. • We propose to modify the triggers as: • EM-High-Et: Et>80GeV, ITrack<3 (or 4) • EM-Very-High-Et: Et>120GeV (or 160GeV)

10. Summary • The EM-High-Et and EM-Very-High-Et triggers are optimized for GMSB with photons. • We propose to modify and use 3 triggers for GMSB photons, which can reach ~93% efficiency with ~3.2 Hz. • We find a new statistics log(ε)/ log(b)to optimize trigger thresholds. • The method is blind analysis, and it may be useful in physics analysis, skims, multivariate analysis. • CMS Note: CMS IN-2008/016 Thank you!

11. Backup Slides

12. Trigger variables for photons • L1Match: Reconstructed super-cluster in the ECAL is required to match L1 energy deposit in some eta and phi windows. • Et : Et of super-cluster in the ECAL is required to exceed a threshold. • IEcal: ECAL isolation, total Et of all clusters with ΔR<0.3 around the photon candidate, excluding those belonging to the super-cluster itself. • IHcal:HCAL isolation, total Et of hadron calorimeter towers with ΔR<0.3 around the photon candidate. • ITrack:Track isolation, number of tracks with Pt>1.5GeV inside a cone ΔR<0.3 of photon candidate.

13. Number of background events processed for rate estimation

14. Information Theory (I) • N events, the amount of information :log2 N. • Amount of information: log(NS ),log(NB ) • Signal efficiency ε and background efficiency b • After the cut: log(NSε), log(NB b) • Reductions of information: -log(ε), -log(b) • Ratio of the reductions: log(ε)/ log(b) • Suppose -log(ε)-log(b) = -log(ε’)-log(b’) , • log(ε)/ log(b)< log(ε’)/ log(b’) cut is better than cut’ • the smaller log(ε)/ log(b), the better • log(ε)/ log(b) <a  ε > ba (0< ε, b, a ≤1). • statisticslog(ε)/ log(b) for the optimization

15. Information Theory (II) • Nis number of messengers; physical results are the meaning of information taken by such N messengers. • For branching ratio (BR), number of messengers is the meaning of information; • For width, mass, … , meaning of info. is taken by the messengers; depends on the kinematics (not just on the number of events). • Good property: blind analysis! • log(ε)/ log(b) depends on the amount of information; does not depend on the meaning of information. • this method will give worse physical results, but they can be trusted. • Attention: log(ε)/ log(b) to optimize a selection with all the other selections applied.

16. Optimization Procedure • For each ai=log(εi)/ log(bi) , we get a minimum point aimin; suppose a1min < a2min < a3min <… < akmin . • if finally we are not satisfied with efficiency (too small), we can loose ak, drop ak, loose ak-1, drop ak-1, …, until we are satisfied with efficiency. • if we are not satisfied with rate (too large), we can vary a1 froma1min up to a2min, or vary a1 =a2 from a2min up to a3min, …, until we are satisfied with rate. •  for the physics analysis, we vary the selections similar as above procedure to be satisfied with the MC predicated physical results.

17. log(ε)/log(b) vs. Cuts (optimized EM-High-Et) Min=0.174 • Et>60; ITrack<2 • ITrack is better only when the ITrack cut point <5 0.022 Work in Progress Min=0.190 0.068

18. Et, IEcal, IHcal, ITrack Distributions for Signal and Bkg Et>40GeV Relaxed Single Photon candidates Work in Progress Et>40GeV Et>40GeV