E. Devetak – CERN CLIC1 LCFI: vertexing and flavour-tagging Erik Devetak Oxford University...
Transcript of E. Devetak – CERN CLIC1 LCFI: vertexing and flavour-tagging Erik Devetak Oxford University...
E. Devetak – CERN CLIC 1
LCFI: vertexing and flavour-tagging
Erik Devetak
Oxford University
CERN-CLIC
Meeting 14/05/09
Vertexing Flavour Tagging Charge ID Physics examples Integration into ILD/SiD
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LCFI1998-2008 ???
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
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The Algorithm
2 different vertexing algorithms implemented
(ZVRES, ZVKIN)
14 discriminatory variables
2 different neural networks with different topologies (built in and FANN)
Vertexing of Tracks Calculation of Discriminating variables Neural Network using discriminating 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/
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Vertex Finding D. Jackson,
NIM A 388 (1997) 247
Implemented by B. Jeffery
and LCFI collaboration
Probability Tubes
Vertex Function
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
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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
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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
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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!
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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…
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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
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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.
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Performance Performance tested on di-jet events CoM 500 GeV and CoM 91.2GeV Tested on LDC/ILD and SiD (also with alternative ANN)
LDC/ILD 91.2GeV and 500 GeV
(SM dijet sample)
b
c (b-bkgr)
c
SiD FANN 500 GeV
(SM dijet sample)
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Performance -2
c NN output for 1,2,3+ vertex case
b tag NN output for 6 jet t-tbar sample
Can look at results for each neural network
Test results for different samples
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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
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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…
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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!
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Conclusion 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)
Ready to be used… should also be easy!NIM paper has been submitted for review.
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LINKS on How-To presentations:
http://ilcagenda.linearcollider.org/materialDisplay.py?contribId=53&sessionId=20&materialId=slides&confId=1446
Class Structure -Vertexing
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
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Other LINKS:Manual (most up to date really)
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/
http://ilcsoft.desy.de/portal/e14/e17/e18/infoboxContent324/LCFIVertex-v00-03-refman.pdf
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