Higgs J CP Measurement in the 4l Channel Using BDT

1. Higgs JCP Measurement in the 4l Channel Using BDT Kareem Hegazy Bing Zhou

2. Introduction to ATLAS • The ATLAS detector is a multipurpose detector at the LHC designed to search for new physics at the TeV scale. • Inner Detector • Electromagnetic Calorimeter • Hadron Calorimeter • Muon Spectrometer

3. Introduction to the Higgs Boson The Higgs Boson is an elementary particle of the Higgs field. The Higgs Mechanism, which is an interaction between the Higgs field and massless fundamental particles, is how massive particles acquire their mass.

4. Higgs Boson Spin and Parity Why care and what to do about it?? • We must determine if the new particle is the Standard Model Higgs • Measure decay properties in the 4 lepton channel

5. Higgs Boson JCP Discriminants Cosθ1 Cosθ2 Φ

6. Higgs Boson JCP Discriminants Φ1 Cosθ*

7. Higgs Boson JCP Discriminants ZZ* Higgs

8. Measurement Procedure • Due to limited statistics, a multivariable analysis must be used: Boosted Decision Tree (BDT) • Use BDT outputs to create 2D pdfs • BDT for different JCP and for Higgs vs. ZZ* • Run a hypothesis test using the 2D pdfs to generate distributions for different JCP • Thousands of pseudo-events • Compare data to JCP distributions

9. Boosted Decision Trees (BDT) BDTs optimize the differentiation between samples based upon the order in which the variables are used to make cuts, and the cut values.

10. Higgs Boson vs. ZZ* Discriminants Higgs Higgs ZZ* ZZ* Higgs ZZ*

11. PDF Creation 0+ vs. ZZ 0+vs. 0- • Plot BDT output of 0+ vs. 0- against 0+ vs. ZZ* • PDFS must be smoothed due to limited statistics • PDFs are used as input into the hypothesis test

12. PDF Creation 0+0- 0+ μμμμ 0- ZZ*

13. PDF Creation • Smoothing is done via the KDE method • Gaussian Kernel • RooNDKeysPDF Before Smoothing After Smoothing

14. Hypothesis Test • Test Statistic: • μsignal: Signal strength • L : Integrated Luminosity • : Signal/Background hypothesis • ε : Fraction of first signal hypothesis • Parameter of Interest • Nuisance Parameters: L, Nsignal, Nbackground

15. Hypothesis Test • Generate pseudo-experiments (toys) with fixed Nsig to construct sampling distributions for the two hypothesis • Each signal sample has the same background. • Background is normalized to estimates from data and MC.

16. Results (Preliminary) 0- • μ = 1 • SM Predictions • 20,000 toys • Not including • Z+Jets • Systematic Errors • Asimov Data • Central value 0+ Asimov Data

17. Conclusion • Analysis is still a work in progress • Currently working on adding systematics • 0+ vs. [2-h, 2+/-, 1+/-, 0+h, 2+h] • BDT analysis technique provides a good measurement method • Hope to reach 3 σ separation between 0+ vs. 0- with fitted signal strength • μ = 1.7