1 / 32

Modeling Fake Missing Transverse Energy with Bayesian Neural NetwoRkS

Silvia Tentindo Florida State University ACAT 11, Brunel University, UK. Modeling Fake Missing Transverse Energy with Bayesian Neural NetwoRkS. Outline. Motivation Modeling Missing Transverse Energy Results Summary and Conclusions. Motivation – PHysics.

Download Presentation

Modeling Fake Missing Transverse Energy with Bayesian Neural NetwoRkS

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Silvia Tentindo Florida State University ACAT 11, Brunel University, UK Modeling Fake Missing Transverse Energy with Bayesian Neural NetwoRkS

  2. Outline • Motivation • Modeling Missing Transverse Energy • Results • Summary and Conclusions Silvia Tentindo ACAT 11

  3. Motivation – PHysics

  4. Missing Transverse Energy @ the LHC • Missing Transverse Energy (MET) is an important observable in many analyses at the LHC: ElectroWeak, Top, SUSY, Exotica, Higgs, … Silvia Tentindo ACAT 11

  5. SM Higgs : Production and Decay yields Higgs production Total Cross Section At LHC (7 TeV) Gluon Gluon ~ 10pb@MH=150GeV Higgs branching ratios: H->ZZ, H->WW And H->bb are dominant at MH=150GeV Silvia Tentindo ACAT 11

  6. Missing Transverse Energy @ the LHC • In particular, it is important in searches for the Standard Model (SM) Higgs boson in the channels: H  W,W  (l,ν),(l,ν) H  Z, Z  (l,l) (ν,ν) H  Z, Z  (l,l) (b,b); (l,l) (j,j) H  Z, Z  τ,τ • We will focus here on the following channels: Silvia Tentindo ACAT 11

  7. SM Higgs Production and Decay dominant Higgs production mechanism gluon gluon fusion vector boson fusion Higgs decay modes to di-leptons: HH l H  W W -> (l,v),(l,v) Z l W v l v H H ->Z Z -> (l,l) (v,v) Z v H W l v MET Silvia Tentindo ACAT 11

  8. Di-Muon Event Observed by CMS A Higgs candidate event: muon muon MET Silvia Tentindo ACAT 11

  9. transverse plane view Silvia Tentindo ACAT 11

  10. MODELING MISSING ET

  11. Monte Carlo Simulation of Variables • Detector simulations at the LHC are able to describe accurately most of the variables that characterize an event • For example: Pt transverse momentum simulated vs measured (ATLAS) Silvia Tentindo ACAT 11

  12. Monte Carlo Simulation of Missing Et • Missing transverse energy is a complex observable. The quality of its measurement depends on: • the hermeticity and granularity of the detector, • pile up effects, • jet multiplicity, etc. • Missing transverse energy comprises both real missing ET from escaping weakly interacting particles as well as fake missing ET Silvia Tentindo ACAT 11

  13. Missing ET – definition and measurement Definition: Measurement: METfake MET true H Z Z  (l, l) (v, v) SIGNAL q q  Z (l,l) + Jets MAIN BACKGROUND Silvia Tentindo ACAT 11

  14. Fake Missing Et in a typical background event p p --> Z (l l) + jets Silvia Tentindo ACAT 11

  15. Monte Carlo simulation of Missing ET Missing Et from simulation and data The present simulation of missing Et is satisfactory, but future conditions from the machine (increased luminosity, pile up effects, and increased energy) motivate exploring data driven modeling of missing Et Silvia Tentindo ACAT 11

  16. Modeling Fake Missing Et • Use photon + Jets data to model fake missing Et: --- Photon + jets events are kinematically and topologically similar to Z + jets events --- The cross section for photon + jets >> cross section for Z + jets --- The energy of the photon is very well measured C : Use fake missing Et distribution in photon + jets data to model the fake missing Et in Z + jets events Pavlunin arXiv:0906.5016v1 Silvia Tentindo ACAT 11

  17. a typical Photon + Jet event Photon + jets events are kinematically and topologically similar to Z + jets events METfake Silvia Tentindo ACAT 11 q q  Photon + Jets

  18. a typical Photon + Jet event transverse plane view Silvia Tentindo ACAT 11

  19. Modeling Fake Missing ET The Photon pT and the Fake Missing Et (MET) are related DeltaPHI (Photon, jets) DeltaPHI(MET, jets) photon MET jets MET Silvia Tentindo ACAT 11

  20. Modeling Fake Missing ET Fake Missing Et vs Pt of Photon photon MET jets MET Silvia Tentindo ACAT 11

  21. Modeling Fake Missing Et by BNN 1 – The Z pt (Photon pt ) and the fake missing Et (MET) are related: p(MET | pT, …). Moreover, the MET could be related to other observables. 2 – The density p(MET | pT) should be the same for Z + jets and for Photon + jets 3 – Given pZ(pT) of Z , model the fake MET in Z + jets events using Silvia Tentindo ACAT 11

  22. Modeling Fake Missing ET with BNN 4 – Use a Bayesian neural network (BNN) to approximate where U(MET) is a known density (e.g., a uniform). (MC) training data: MET, pT from photon + jets (target = 1) MET from U(MET) and pT from photon + jets (target = 0) MET pT bnn(MET, pT) Silvia Tentindo ACAT 11

  23. Modeling Fake Missing ET with BNN 5 – Then the desired density can be written as: MET pT bnn(MET, pT) Silvia Tentindo ACAT 11

  24. RESULTS

  25. Results of BNN Training MET distributions for a fixed bin in photon pT Silvia Tentindo ACAT 11

  26. Results of BNN Training – Closure Test MET distribution integrated over photon pT spectrum Preliminary results of closure test look promising Silvia Tentindo ACAT 11

  27. Summary and Conclusions • As the LHC luminosity increases (and the energy), we expect that the simulation of MET could become harder • We proposed a method to extract the Fake Missing Et spectrum from photon + jets data and approximate it with a Bayesian neural network. • The method could be useful in modeling the Fake Missing Et for Z + jets events, which are the dominant background in the Higgs to 2l, 2v channel. Silvia Tentindo ACAT 11

  28. Backup slides

  29. x1 y(x,w) x2 Bayesian Neural Networks The weights are sampled from a probability density function defined on the neural network parameter space Silvia Tentindo ACAT 11

  30. BNN details • Use ~ 15,000 simulated photon + jets events • Use neural networks (NNs) with 2 inputs and 15 hidden nodes • Generate ensemble of NNs with Flexible Bayesian Modeling (FBM) package by Radford Neal • Average over 100 independent NNs Silvia Tentindo ACAT 11

  31. Modeling Fake Missing ET Basic Idea: extract fake missing ET distribution from: Silvia Tentindo ACAT 11

  32. Fake Missing ET Z + jets Fake missing ET (MET) largely due to jet mismeasurements jet Z Courtesy ATLAS+CMS Expect MET to be aligned or anti-aligned with jet Silvia Tentindo ACAT 11

More Related