Shi-Lei ZangOn behalf of the CMS GMSB group

APS April MeetingMay 5, 2009

G~

01

~

01

~

p p

q

q

q

q ~…

…

jet

jet

jet

jet

G~

~

NLSP LSP + photon Prompt decay Experimental signature

• high pT photons

• large MET due to gravitinos

• multi-jets

2

GMSB with Photons

G~~0

1

%8.11:~~

%2.88:~~

01

01

BRZG

BRG

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)• Wenu, Zee

With CMSSW_1_6_7; optimized for the luminosity of 1032 cm-2s-1.

3

Datasets

4

HLT Paths for Photons

6 Trigger paths with thresholds

Single Photon (S)

Relaxed Single Photon (RS)

Double Photon (D)

Relaxed Double Photon (RD)

EM-High-Et (H)

EM-Very-High-Et (VH)

ET (GeV) >30 >40 >20 >20 >80 >200

ECAL Isolation (GeV) <1.5 <1.5 <2.5 <2.5 <5.

HCAL Isolation (GeV)(Barrel)

<6. <6. <8. <8.<12.

HCAL Isolation (GeV) (Endcaps)

<4. <4. <6. <6.

Track Isolation(# tracks)

<1 <1 <3 <3 <4

Signal Eff. (GM1e) 68.04% 68.10% 40.64% 46.34% 72.53% 46.10%

Rate (Hz) 8.96 2.93 0.26 1.90 0.63 0.13

Optimize the 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 with some 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

log(ε)/ log(b) <a

a=1.0

a=0.7a=0.5a=0.3a=0.2

a=0.1a=0.01

ε >ba

(1.,1.)

(0.,0.)

ε

b

• 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.

Optimize a selection with all the other selections applied.

7

log(ε)/log(b) vs. Cuts (default EM-High-Et)

0.0350.129

0.0170.102

• ITrack is better than IEcal and IHcal.

Each figure is plotted with other cuts applied.

Work in Progress

8

• 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.

Trigger H, VH oH, oVH H, VH, RS oH, oVH, D

Rate (Hz) 0.753 2.888 3.510 3.139

Efficiency(GM1e)

81.9% 92.1% 89.6% 93.3%

2.14 Hz

98.5% events have signal photons at generator level; after SusyAnalyzer, only 93.5% events have reconstructed photons.

Optimization Results

9

Modification

I. ITrack<2 can cut out most of the converted photons.

II. ITrack<2 cost significant loss in electron efficiency for other physics (duplicate tracks, or converted photons).

III. We need control sample for background study, which needs enough isolation space between online triggers and offline selection.

IV. 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

• 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.

Trigger variables for photons

QCD Jets

N QCD Jets

N Photon Jets

N Bkg N

0_15 295,613 _470 88,086 0_15 500,000 Wenu 205,707

15_20 1,255,976 _600 55,000 15_20 509,825 Zee 162,219

20_30 2,513,934 _800 21,974 20_30 606,680

30_50 2,416,441 _1000 33,330 30_50 510,094

50_80 2,451,439 _1400 5,299 50_80 169,741

_120 1,161,823 _1800 _120 164,000

_170 499,389 _2200 _170 69,993

_230 428,888 _2600 _300 24,993

_300 172,619 _500 15,554

_380 82,998 _7000 6,666

Number of background events processed for rate estimation

13

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(NS ε), 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). statistics log(ε)/ log(b) for the optimization

15

• N is 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.

Information Theory (II)

16

Optimization Procedure

• For each ai=log(εi)/ log(bi) , we get a minimum point aimin;

suppose a1min < a2

min < a3min <… < ak

min .

• 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 from

a1min up to a2

min, or vary a1 = a2 from a2min up to a3

min, …, 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)

• Et>60; ITrack<2

• ITrack is better only when the ITrack cut point <5

0.022Min=0.174

Min=0.190 0.068

Work in Progress

Et>40GeV

Et>40GeV Et>40GeV

Relaxed Single Photon candidates

18

Et, IEcal, IHcal, ITrack Distributions for Signal and Bkg

Work in Progress