Fake electrons from QCD

1. Kristin Lohwasser egamma Meeting Fake electrons from QCD Kristin Lohwasser, Oxford Universityk.lohwasser1@physics.ox.ac.uk • Motivation: W analysis background from data • The „reverse ID Cuts“ method • Outlook

2. Kristin Lohwasser egamma Meeting Motivation: QCD background for W analyses • QCD jets faking electrons are dominant background to W analyses • maybe up to 50% of W candidates with standard cuts - e20 trigger and medium ID - lepton |eta| < 2.4 - lepton pT > 25 GeV - Etmiss > 25 GeV • QCD background cannot be fully simulated, uncertainty on rate is estimated to be ~ factor 3 (CSC note) Data-driven QCD background methods needed for early analysis! • Combining different methods will allow cross checks, reducing systematic uncertainties

3. Kristin Lohwasser egamma Meeting The „Reverse ID Cuts“ Method • Idea: - Choose an observable with discriminative power - Build distribution template for fake and real electrons for observable- Use template to extract fraction of signal and background in data • Problem: How to get these distribution templates? • Solution: Reverse electron identification selection (IsEM bits) unbiased with respect to chosen discriminative observable

4. Kristin Lohwasser egamma Meeting The „Reverse ID Cuts“ Method • Idea: Choose observable with discriminative power for fake and real electrons • Here as examples: EtMiss and Isolation • Shape of signal and background completely different • Differences in shape improve fits (small uncertainties) • normalized # electrons • normalized # electrons • Cut • Cut • etcone45/ET_elec • (fraction energy in DR=0.45 cone around electron) • ET Miss [GeV] • Object-based observable allow to apply full set of kinematic selectionswhile EtMiss is part of this type of selections • Fake rate dependend on kinematic selections

5. Kristin Lohwasser egamma Meeting How to get Templates? • Signal: Real electron distribution for discriminative observable • Isolation: from Z->ee (tight, tight), narrow MZ Window-> small background contamination to template • EtMiss: hard to avoid Monte Carlo, difficult to trust-> may use W->mu nu (loose independence between mu and electron analyses) • Fake: Try to find a set of standard electron selections (IsEM bits)that do not modify the shape of discriminative observable • come back to that later...

6. Kristin Lohwasser egamma Meeting How to get Fake Template: Principles • Reverse ID Cuts to get enriched fake sample, i.e. • Use genuine electron identification cuts (IsEM bits) • Apply all electron ID cuts but one*** • Keep all events that satisfy the reverse of this cut (reverse ID cut) • Dijet sample • Wenu sample • NO EtMiss Cut • HadEm defines fake and real electronsin data • HadEm <0.018: electron • HadEm >0.018: fake (reverse ID) • # electrons real electrons fakes • Hadronic leakage from EM to HadCal • ***(for now, for the simplicity of the argument)!

7. Kristin Lohwasser egamma Meeting How to get Fake Template: Principles • Reverse ID Cuts to get enriched fake sample, i.e. • Use genuine electron identification cuts (IsEM bits) • Apply all electron ID cuts but one*** • Keep all events that satisfy the reverse of this cut (reverse ID cut) • Use this fake sample to produce fake template for discriminative observable • Dijet sample • Wenu sample • NO EtMiss Cut • HadEm defines fake and real electronsin data • HadEm <0.018: electron • HadEm >0.018: fake (reverse ID) • # electrons real electrons fakes Fake template: EtMiss distribution • Hadronic leakage from EM to HadCal • ***(for now, for the simplicity of the argument)!

8. Kristin Lohwasser egamma Meeting Real Life: Creating the Fake sample • 2 or 3 cuts of the following were reversed to create the Fake Electron Sample • 2 or 3 is a good trade-off for low background and low bias • logical OR of all the possible combinations Δ Estrips: Energy difference between 2nd shower max and min in strips Rmax2: Energy of the two cells of 2nd max of shower in strips divided by ET of cluster Wstot: Shower width inside the first layer Ws3: Shower width for three strips around maximum strip Fracside: Energy outside shower in strips • Reverse cuts are shower variables • See left picture for EM calo how they are determined • possible also with track ID cuts (under investigation)

9. Kristin Lohwasser egamma Meeting Electron contamination in Fake Sample • From the fake sample obtained in the previous slide: • example here: EtMiss as discriminative variable Fraction of true electrons failing the ID cuts: 0.0025 -> Fake template practically signal free

10. Kristin Lohwasser egamma Meeting Check bias of MET fake template • To check for bias: Compare EtMiss distribution from reverse ID sample (fakes) to one with all medium ID cuts (''real'' fakes) • Etmiss distributions of fakes passing ID cuts fakes failing (some) ID cuts • no obvious bias within statisticstotal number events in each template: • passing: 2266 • failing: 476 • simulation used here: 0.035 pb-1data: 1000 times more with 10 pb-1 • lower plot: ratio between fakes passing ID cuts and failing (some) ID cuts

11. Kristin Lohwasser egamma Meeting Check bias of MET fake template • To check for bias: Compare EtMiss distribution from reverse ID sample (fakes) to one with all medium ID cuts (''real'' fakes) • Etmiss distributions of fakes passing ID cuts fakes failing (some) ID cuts • no obvious bias within statisticstotal number events in each template: • passing: 2266 • failing: 476 • simulation used here: 0.035 pb-1data: 1000 times more with 10 pb-1 • lower plot: ratio between fakes passing ID cuts and failing (some) ID cuts Have now fake template to describe discriminative observable

12. Kristin Lohwasser egamma Meeting Extraction of signal and background fraction • filtered dijet sample, v13 • includes W + QCD only • Data with all cuts (medium ID, pt>25GeV) • Now use templates to fit this distribution

13. Kristin Lohwasser egamma Meeting 3) Extraction of signal and background fraction • template fits total fit true elecs (W MC) fakesROOT: TFractionFitter • Template fits: extract bkg and signal fractions • Fit returns normalization factor to each template • Integral over normalized template in signal region is background prediction

14. Kristin Lohwasser egamma Meeting Extraction of signal and background fraction • template fits true ele template fit fakes template fit • generator info ▲ true elecs ▼ true fakes • Background fractions: • MC: 0.44+/-0.02 (stat.) • Fit estimate: 0.46+/-0.02 (stat.) • systematic uncertainties estimated as difference between the two results • same level of uncertainty as statistical error

15. Kristin Lohwasser egamma Meeting Summary of the method • 1) Create the (pure) fake sample by reversing some ID cuts • Would be rejected in an analysis, because they do not pass ID cuts • Very low true electron fraction • 2) Test agreement between the fakes failing ID cuts and passing ID cutsfor the test distribution used to cut between signal and background • 3) Test extraction of real (signal) and fake (background) electron fraction • it works....

16. Kristin Lohwasser egamma Meeting Summary of the method • 1) Create the (pure) fake sample by reversing some ID cuts • Would be rejected in an analysis, because they do not pass ID cuts • Very low true electron fraction • 2) Test agreement between the fakes failing ID cuts and passing ID cutsfor the test distribution used to cut between signal and background • 3) Test extraction of real (signal) and fake (background) electron fraction • it works.... Using independent samples extracted correct estimates for the QCD background • - fakes failing ID cut • - electrons from W->enu MC samples are the fit templates • - all passing electrons from filtered dijet sample (W and QCD) are the„data“

17. Kristin Lohwasser egamma Meeting Backup slides

18. Kristin Lohwasser egamma Meeting Check bias of MET fake template • To check for bias: Compare EtMiss distribution from reverse ID sample (fakes) to one with all medium ID cuts (''real'' fakes) • Etmiss distributions of fakes passing ID cuts fakes failing (some) ID cuts • no obvious bias within statisticstotal number events in each template: • passing: 2266 • failing: 476 • simulation used here: 0.035 pb-1data: 1000 times more with 10 pb-1 • lower plot: ratio between fakes passing ID cuts and failing (some) ID cuts