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

E. Devetak – CERN CLIC2

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


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/


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


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


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


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!


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…


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


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.


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)


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


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


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…


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!


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.


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


Flavour Tagging



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


BACKUP

E. Devetak – CERN CLIC1 LCFI: vertexing and flavour-tagging Erik Devetak Oxford University...

Documents

Transcript of E. Devetak – CERN CLIC1 LCFI: vertexing and flavour-tagging Erik Devetak Oxford University...