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WP 1 – Simulation and Physics Studies

LCFI OsC meeting, 15 th February 2008. WP 1 – Simulation and Physics Studies. Introduction Progress with tool development Progress with benchmark physics studies Future Plans. Sonja Hillert (Oxford). Introduction.

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WP 1 – Simulation and Physics Studies

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  1. LCFI OsC meeting, 15th February 2008 WP 1 – Simulation and Physics Studies • Introduction • Progress with tool development • Progress with benchmark physics studies • Future Plans Sonja Hillert (Oxford)

  2. Introduction • Aim: optimisation of vertex detector parameters and evaluation of its performance • by assessment of different detector designs and developments of tools needed • for such studies (internationally considered responsibility of Vertex Detector R&D groups) • Have further strengthened collaboration with other groups in the UK and abroad: • Essential contributions to work of ILD(combined LDC/GLD group preparing common LoI) • use of common tools and MC samples permits studies that LCFI could not do on its own • Gained higher profile in SiD: A. Nomerotski one of the SiD-benchmarking conveners • LCFIVertex code improved & extended, second version released in autumn • Dedicated effort to provide up-to-date default configuration, to feed into benchmark • physics studies of LCFI and of the wider ILC community, in preparation of the LoIs • Physics benchmark studies have begun: analysis code is being developed • and first promising results have been obtained.

  3. interface internal format to SGV interface SGV to internal format interface LCIO to internal format interface internal format to LCIO output of LCFI Vertex Package input to LCFI Vertex Package ZVTOP: ZVRES ZVKIN vertex information track attachment for flavour tag track attachment assuming b jet track attachment assuming c jet find vertex- independent flavour tag inputs find vertex- dependent flavour tag inputs neural net flavour tag find vertex charge

  4. Extensions of the Vertex Package • Vertex fitter for IP fit: replaced by Kalman filter approach (Gorbunov, Kisel) •  significant improvement in run time performance (reduced to ~ 1/3 of previous value) • Vertex charge reconstruction: implementation corrected, moved to separate processor • New diagnostic features: detailed plots and performance tables added; • these permit the effect of parameter changes to be monitored (needed for parameter tuning) • Flavour tag inputs: one of the inputs, the joint probability for all tracks to originate from • the primary vertex, uses parameters obtained from fits of impact parameter significance; • fit macro written and tested; will be used for parameter tuning • CCD digitizer: simulation of vertex detector hits including processes in CCD sensor • (before, MarlinReco framework only provided Gaussian smearing & processor for DEPFET)

  5. Purity of reconstructed track-vertex association (%) MC track       Reconstructed track-vertex association    origin        Two-vertex case         Three-vertex case                      Pri.    Sec.    Iso.       Pri.    Sec.    Ter.    Iso.     Primary   90.5    1.61    27.7      97.2    4.66    2.43    46.5    B decay   7.2    48.6    35.1       1.91    74.9   9.99    24.5    D decay   2.28    49.8    37.2     0.935    20.5    87.6   29    All above  47.3      41    11.7      36.8    32.2    24.9    6.07 New diagnostic features Diagnostic plots and tables will permit us to choose package parameters that are tuned to full MC and reconstruction and will be used in the “central reconstruction” of MC samples for ILD optimisation.

  6. b c (b-bkgr) c using FullLDCTracking, LDC01_05Sc open: previous parameters full: new joint probability parameters Fit macro for joint probability calculation: results Obtained new values with full MC & reconstruction (left): values change as expected, flavour tag performance stable.

  7. Transition to full MC and reconstruction • Reconstruction chain: • Track reconstruction > particle flow algorithm > jet finding > vertexing & flavour tag • need to check our code runs smoothly with other packages in MarlinReco • Initial full MC results obtained with “Wolf” PFA; in the meantime, the PandoraPFA • code has become the best performing PFA in the ILC community; • ensure both PFAs can be used with LCFI code & performance is understood • First tests: Wolf+VP better than Pandora+VP: tracked back to differences in steering • now checked: running PandoraPFA with same tracking settings as Wolf does not impair • PFA performance and gives consistent flavour tag results compared to Wolf • Also intensely working on understanding effect of and developing corrections for • effect of photon conversions, • KS and L decays • hadronic interactions: contributed to high-level detector description required for this

  8. b c (b-bkgr) c using FullLDCTracking, LDC01_05Sc open: Wolf particle flow (used in validation) full: PandoraPFA Contribution to checks of reconstruction chain • After careful comparison of steering for track reconstruction, performance of • Wolf + LCFIVertex and of PandoraPFA + LCFIVertex in excellent agreement

  9. Optimisation of Vertex Package parameters • LCFI code is steered by various parameters: track selection, ZVTOP parameters, • parameters for flavour tag and vertex charge and neural networks (for tag) • Current settings were obtained with fast MC SGV / GEANT3-based full MC and TESLA detector: • can expect them not to be optimal for GEANT4-MC & current detector models (LDC, GLD, SiD) • Work on deriving a reasonable default configuration of our tools is underway. • This configuration will be used in the LCFI benchmark studies and will feed into the • ILD detector optimisation effort: • There is consensus in the ILD group that the LCFI code will form part of the • centrally run reconstruction chain for ILD optimisation. • We are developing a semi-automatic set-up for the CPU-intensive parameter tuning. • This will permit tuning to be repeated for other detector configurations in the future.

  10. Studies of benchmark physics processes • Vertex detector design will influence ILC detector capabilities in many areas including • Higgs physics, SUSY studies and indirect searches for new physics. • LCFI has taken up the following studies, in close coordination with the ILD • detector optimisation group and the SiD benchmarking group: • Higgs branching ratios: development of GEANT4-based analysis underway (Bristol) • e+e-  bb: study of left-right asymmetry, depending on quark charge sign-selection (Oxford) • e+e-  tt: anomalous Wtb coupling, also requires quark charge (Oxford, RAL) • e+e-  ZHH: measurement of Higgs self-coupling (Oxford) • SUSY study: light sbottom decaying to soft b-jets in the final state (Montenegro, RAL, Oxford)

  11. Higgs self-coupling study: overview • Aim: determine precision with which Higgs self coupling can be measured in • the process e+e-  ZHH  bbbbqq • Requires excellent b-tagging, may profit from quark charge sign-selection • Study performed in coordination with SiD benchmarking group, where this • has already been studied by Tim Barklow • Initially using Tim Barklow’s U.S. fast MC samples (LCIO data format permits • LCFI code to be run in the Marlin framework using these files as input) • At present, no preselection applied in LCFI study • Main event selection variable: sum of b-tag neural network outputs for all jet • Distributions of this variable for signal and background look very encouraging • Comparison with Barklow’s result shows potential for improvement of • background suppression wrt U.S. study • First time LCFI code used for multi-jet events: successful

  12. Higgs self-coupling study: first results Tim Barklow Tomas Lastovicka, Andrei Nomerotski (Oxford) • potential for improvement of background suppression wrt U.S. study

  13. Future Plans: Vertex Package • Finding a default configuration for the LCFI code currently has the highest priority • A Focused team effort will be kept up to meet requirements of physics studies • of LCFI and the community • This includes development of correction procedures for photon conversions, KS and L • decays and hadronic interactions • Envisage to study if vertex charge and flavour tag can be improved further: • Reassess parameters for vertex charge reconstruction (not included in default • configuration, as only used by some of the analyses underway) • Explore effect of different choice of flavour tag inputs & neural net architecture • These studies may in part require dedicated effort, in part be best performed in • conjunction with the ongoing benchmark studies.

  14. Future Plans: benchmark physics studies • In the near future major progress also expected for all the benchmark studies. • First detailed performance evaluation for baseline detector configuration • aimed at by the summer for all the studies in progress • Analysis code will be set up such that comparison with different detector • geometries will be straightforward • example: Detailed plans for e+e-  ZHH study: • Addition of further backgrounds • Implementation of event selection; explore NN-based event selection • Determine uncertainty of cross section and of Higgs self-coupling • Replace fast MC with “Perfect PFA” (realistic tracks + cheated calorimeter treatment) • Investigate improvements possible by using vertex charge • Further input to the work of the ILD and the SiD LoI groups will result from both • the work on the tools and from the study of benchmark processes.

  15. Additional Material

  16. A macro to improve calculation of one of the inputs for the flavour tag-NN • has been implemented: • probability that a track from primary vertex has impact parameter significance > b/sb is • the symmetric function needs to be obtained from the negative side of the b/sb • distribution; • first release of the Vertex Package uses hard coded parameterisation obtained from fast MC • Erik Devetak has implemented a module to obtain parameters properly from a fit procedure. • joint probability (flavour tag input) for ensemble of tracks to come from primary vertex is Flavour tag improvement

  17. Joint probability macro: example output

  18. CCD digitizer • Short-term student project (RAL): modification of A. Raspereza’s DEPFET code • to simulate processes in sensor & digitization to describe processes in CCD, • based on java-code by N. Sinev. • Along particle trajectory, ionisation points assigned energy loss (GEANT4) • Each charge packet simulated individually • Charge packets in the bulk of the CCD discarded • In undepleted part of epi-layer, possibility of reflection at p/p+ edge taken into account • Magnetic effects taken into account only for depleted region: Lorentz angle calculated from bias voltage, magnetic field, electron mobility and temperature • Noise in the sensor and readout added (including simulation of pixel saturation) • Using Raspereza’s clustering: pixel-threshold (200 e-), larger weight for central pixels • Initial comparisons with Raspereza’s digitizer encouraging; some details remain • to be understood

  19. b c (b-bkgr) c using FullLDCTracking, LDC01_05Sc open: Wolf particle flow (used in validation) full: PandoraPFA Initial Pandora – Wolf comparison (different settings) PandoraPFA run with the tracking settings used for PFA studies – different from what LCFI had been using for code validation with Wolf-PFA; now resolved (see main talk).

  20. γγ → bb Detector acceptance sbottom signal SUSY study: light sbottom decays • Some MSSM models favour light sbottom decaying to final state with low-momentum b-jet • identification of these jets will be particularly sensitive to material amount in the VXD • Initial study performed at hadron level, some discriminators also studied at parton level to • explore possible event selection strategies • Main background consists of two-photon events: for bb channel, cross section for this • background is 1000 times the signal cross section parton level hadron level

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