1 / 22

LCFI: vertexing and flavour-tagging

Erik Devetak Oxford University. LCFI: vertexing and flavour-tagging. Vertexing Flavour Tagging Charge ID Physics examples Integration into ILD/SiD. CERN-CLIC Meeting 14/05/09. LCFI. 1998-2008 ???. Universities participating (2005-2008):

cybil
Download Presentation

LCFI: vertexing and flavour-tagging

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. E. Devetak – CERN CLIC Erik Devetak Oxford University LCFI: vertexing and flavour-tagging • Vertexing • Flavour Tagging • Charge ID • Physics examples • Integration into ILD/SiD CERN-CLIC Meeting 14/05/09

  2. E. Devetak – CERN CLIC LCFI 1998-2008 ??? • Universities participating (2005-2008): Bristol, Edinburgh,Glasgow, Liverpool, Nijmegen, Oxford, RAL AIMS • Develop Vertex detector for ILC: point resolution of 3.5 μm • Develop tools that will exploit the vertex detector (vertexing of secondary, tertiary + flavour tagging) • Perform relevant physics analysis

  3. E. Devetak – CERN CLIC The Algorithm • Vertexing of Tracks • Calculation of Discriminating variables • Neural Network using discriminating variables 2 different neural networks with different topologies (built in and FANN) 2 different vertexing algorithms implemented (ZVRES, ZVKIN) 14 discriminatory variables • All Modular • Part of the ILC-Soft Reconstruction Software • Used in both the SiD and ILD detector concepts NIM paper in the process of internal review, package available: http://ilcsoft.desy.de/portal/software_packages/lcfivertex/

  4. E. Devetak – CERN CLIC Vertex Finding D. Jackson, NIM A 388 (1997) 247 Implemented by B. Jeffery and LCFI collaboration • LCFI implemented general ZVRES algorithm: • Represent tracks with Gaussian ´probability tubes´ • Calculate vertex function • Search 3D-space for maxima of this function • Combine close-by vertices - resolve ambiguities Probability Tubes Vertex Function

  5. E. Devetak – CERN CLIC Vertexing - Results • Used sample with CoM = 91.2GeV • Good performance • Also for finding tertiary vertices! • Two Main issues: • One prong Vertices ( one charge track in each vertex!) • Resolving Vertices close to IP

  6. E. Devetak – CERN CLIC Tagging Inputs • Really have three set of inputs: • No secondary vertex found (6 inputs) • At least one secondary vertex found (6 inputs) • Always used Joint Probability (2 Inputs) • Joint Probability = Probability all tracks from primary vertex! • For distribution of tracks from IP, use tracks with negative impact parameters. • Calculate probability track has impact parameter significance larger that what reconstructed • Recombine probabilities of all tracks into single value

  7. E. Devetak – CERN CLIC Tagging Inputs – no sec. vertex • No secondary vertex found → impact parameters significances • Use two tracks with highest significances in z0 and d0! • But also use momentum of these tracks!

  8. E. Devetak – CERN CLIC Tagging Inputs – sec. vertex • Use Vertex information. • Decay length and its significance, momentum of vertex, number of non primary tracks and probability all from same secondary…

  9. E. Devetak – CERN CLIC Pt Corrected Vertex Mass • Most important parameter (particularly for b tagging) • Calculate vertex mass from charged tracks • Use error matrix of vertices to correct for neutrals

  10. E. Devetak – CERN CLIC Combining the Inputs • Data mining problem → many possible techniques • LCFI implemented ad hoc neural network approach. • Trained 9 different networks: • Dependence on number of vertices (1,2,3+) • Definition of signal (b, c, c with b only background) • SiD also tried using FANN package networks • Simpler Method train only 3 networks (b, c, c with b only background) • Always use all Inputs (when available else set to default) • Add 2 Inputs Number of Vertices, Energy of Jet.

  11. E. Devetak – CERN CLIC c (b-bkgr)‏ b c Performance • Performance tested on di-jet events CoM 500 GeV and CoM 91.2GeV • Tested on LDC/ILD and SiD (also with alternative ANN) SiD FANN 500 GeV (SM dijet sample) LDC/ILD 91.2GeV and 500 GeV (SM dijet sample)

  12. E. Devetak – CERN CLIC Performance -2 • Can look at results for each neural network • Test results for different samples c NN output for 1,2,3+ vertex case b tag NN output for 6 jet t-tbar sample

  13. E. Devetak – CERN CLIC Vertex Charge • Package contains also two vertex charge calculation. • Assumption B meson (use all secondary vertices) • Assumption D meson (use only furthest secondary vertex!) • Also tested extension by using more sophisticated reconstructions of many variables; for now • Momentum weighted vertex charge • Momentum weighted jet charge combined in one parameter Combined Charge

  14. E. Devetak – CERN CLIC Physics Example – hadronic ttbar • Often used in the SiD and ILD LOIs • In the Hadronic ttbar example used to: • Reject background • Reduce jet combinatorics • B-bbar quark fb assymetry • But also used in: • H→cc • Additional SiD sbottom analysis • Additional ILD ZHH…

  15. E. Devetak – CERN CLIC Integration in ILD/SiD framework • LCFIVertex has been fully coded in the Marlin ILD/framework. • Makes use of LCIO data structure • Inherits its dependencies. • But works also with SiD: • Detector geometry independent (well almost need fake file to make the framework happy and change one line of code) • Hence can do Sid Reconstruction up to jet finding and then move to LCFI • But you need to install the whole ILD framework! (many dependencies!) • Analysis done in such way are just as valid!

  16. E. Devetak – CERN CLIC Conclusion Ready to be used… should also be easy!NIM paper has been submitted for review. • Presented the LCFI Vertex package. Capabilities: • Vertexing • Flavour Tagging • Charge reconstruction • Showed performance and usage in ILD and SiD Detector! • Flexible and easy to use! • LCIO allows for easy usage of software of both detector concepts! • Gave examples of why it can be a very useful tool! • Physics studies! (very varied)

  17. E. Devetak – CERN CLIC LINKS on How-To presentations: Class Structure -Vertexing http://ilcagenda.linearcollider.org/materialDisplay.py?contribId=53&sessionId=20&materialId=slides&confId=1446 Flavour Tagging Discriminants http://ilcagenda.linearcollider.org/materialDisplay.py?contribId=56&sessionId=20&materialId=slides&confId=1446 Flavour Tagging http://ilcagenda.linearcollider.org/materialDisplay.py?contribId=57&sessionId=20&materialId=slides&confId=1446

  18. E. Devetak – CERN CLIC Other LINKS: Manual (most up to date really) http://ilcsoft.desy.de/portal/e14/e17/e18/infoboxContent324/LCFIVertex-v00-03-refman.pdf Download (here find also information on other ILD packages LCFI Depends on. Note ilcinstall this should install everything for you) http://ilcsoft.desy.de/portal/software_packages/

  19. E. Devetak – CERN CLIC BACKUP

  20. E. Devetak – CERN CLIC

  21. E. Devetak – CERN CLIC

  22. E. Devetak – CERN CLIC

More Related