tools for optimising and assessing the performance of the vertex detector
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Simulation Software Meeting DESY, 27 / 28 June 2005. Tools for optimising and assessing the performance of the vertex detector. from MIPS to physics high-level reconstruction tools outlook: plans for Snowmass. Sonja Hillert (Oxford) on behalf of the LCFI collaboration. - PowerPoint PPT PresentationTRANSCRIPT
Sonja Hillert, University of Oxford Simulation Software Meeting ~ DESY 28 June 2005 p. 1
Tools for optimising and assessing the performance
of the vertex detector
Sonja Hillert (Oxford)
on behalf of the LCFI collaboration
Simulation Software Meeting DESY, 27 / 28 June 2005
from MIPS to physics
high-level reconstruction tools
outlook: plans for Snowmass
Sonja Hillert, University of Oxford Simulation Software Meeting ~ DESY 28 June 2005 p. 2
Typical event processing at the ILC
reconstruction
of tracks, CAL-cells
energy flow objects
first order
jet finding
b-jets
c-jets
uds-jets
gluon-jets
tune track-jet
association
for tracks
from SV or TV
contained in
neighbouring
jet
associate with parent jet in some cases;
tag some as c-cbar or b-bbar
classify B
as charged
or neutral
classify D
as charged
or neutral
charged B
charged D
neutral B
neutral D
charge dipole,
protons, charged
kaons or leptons
from SV, TV
charged kaons
or leptons
b
bbar
b
c
cbar
c
cbar
bbar
flavour
identification
Sonja Hillert, University of Oxford Simulation Software Meeting ~ DESY 28 June 2005 p. 3
From MIPS to physics
To optimise design of vertex detector and evaluate its physics performance need:
1) sufficiently accurate reconstruction (‘from MIPS to tracks’)
2) high-level reconstruction tools,
e.g. flavour tagging, vertex charge reconstruction, … (see previous page)
3) study of benchmark physics channels based on these tools
Step 1 comprises, e.g., simulation of :
signals from the sensors: charge generation/collection, multiple scattering
data sparsification: signal & background hit densities, edge of acceptance
Other parameters to be determined from the results obtained with the entire chain:
overall detector design: radial positions (inner radius!) and length of detector layers,
arrangement of sensors in layers, overlap of barrel staves (alignment), strength of B-field
material budget: beam pipe, sensors, electronics, support structure (material at large cos )
Sonja Hillert, University of Oxford Simulation Software Meeting ~ DESY 28 June 2005 p. 4
Current status
so far focused on high-level reconstruction tools
(in particular flavour-tag, vertex charge) using mainly fast MC simulation SGV
and (for part of the studies) JAS3;
SGV: core of the program well tested ( DELPHI), allows fast change of geometry
lacks: accurate description of processes in sensors & readout chain, and of
multiple scattering
JAS3: full MC under development, but not ready / robust for the time being;
tracking used in the fast MC available under JAS3 less precise than SGV
(SGV: Billoir algorithm)
Sonja Hillert, University of Oxford Simulation Software Meeting ~ DESY 28 June 2005 p. 5
LCFI proposed independent development of full GEANT4-based description
of processes in vertex detector sensors and readout chain to UK funding agencies
(see also 59th DESY-PRC, May 05)
while such an approach is in principle appreciated, the current funding situation
in the UK does not allow an effort in this field at the level needed to implement
the full ‘MIPS to physics’ programme
looking for ideas how to form international effort to develop the essential
simulation and reconstruction tools
Current status cont’d
Sonja Hillert, University of Oxford Simulation Software Meeting ~ DESY 28 June 2005 p. 6
Future plans
future programme will depend on further negotiations – outline of plans preliminary!
envisage top-down approach: select physics channels requiring variety of
higher level reconstruction tools; develop/improve and assess those in parallel
processes to be studied (both requiring flavour tagging, vertex charge reconstruction):
Higgs self-coupling:
• might profit from improving track-jet association using vertex information
Left-right forward-backward asymmetry in e+e- b bbar, c cbar:
• sensitive to polar angle dependence, decays outside the vertex detector (at high energy),
• could be used to assess performance of charge dipole reconstruction
(yielding quark charge measurement for neutral hadrons)
use these processes as benchmarks to determine sensitivity to detector design
parameters on a timescale of ~ 2-3 years
Sonja Hillert, University of Oxford Simulation Software Meeting ~ DESY 28 June 2005 p. 7
Visualisation toolsPurpose: flavour tagging & vertex charge reconstruction can be improved
by looking at cases, where the reconstruction fails, on an event by event basis
top: written in Python with Coin/HEPVis
wrappers; input read from XML file (D. Bailey)
right: root-based tool; so far MC tracks only;
reconstruction level to be added (B Jeffery)
Sonja Hillert, University of Oxford Simulation Software Meeting ~ DESY 28 June 2005 p. 8
OO reference version of ZVTOP
ZVTOP in use for ~10 years, several versions (SLD LEP ILC …
variable transformations; differences in what is included in the different versions)
LCFI therefore decided to develop an object oriented (C++) version of ZVTOP,
and to check it against the SLD code (a Java-based version is being developed at SLAC)
latest version of ZVTOP (ZVTOP3) comprises two branches:
´ZVRES´ and ´ZVKIN´ (also known as the ´ghost track algorithm´)
ghost track algorithm should:
• cope with cases with a 1-prong B decay followed by a 1-prong D decay
• allow reconstruction of the charge dipole (information on neutral B´s)
• at the ILC: improve flavour tagging capabilities
development of the class structure in progress;
estimated timescale for development and verification: ~ 1 year
Sonja Hillert, University of Oxford Simulation Software Meeting ~ DESY 28 June 2005 p. 9
Neural Network Tool
neural nets used for flavour tagging, vertex charge reconstruction, …
C++ based code developed in Bristol allows implementation of
feed-forward nets of arbitrary topologies:
3 response functions available: sigmoid, tansigmoid, linear, can be combined
(i.e. different neurons in same net can have different response functions)
4 training algorithms: 3 based on back-propagation, 1 ‘genetic’ algorithm
networks generated with this tool can be serialised as plain text or in XML format
for retrieval from a web server
tar-file available at
http://www.phy.bris.ac.uk/research/pppages/DaveB/NeuralNet.tar.gz
Sonja Hillert, University of Oxford Simulation Software Meeting ~ DESY 28 June 2005 p. 10
Vertex charge reconstruction
Vertex charge reconstruction studied in using SGV framework
Procedure: find vertices and vertex axis (ZVTOP)
assign tracks to B decay chain & sum their charge:
can either use a neural net or assign all tracks found in
‘inner vertices’ (methods work equally well at ECM = 200 GeV)
Status:
extending study to range of centre of mass energies:
larger fraction of B hadrons decay outside vertex detector
find steep drop in 2D seed vertex decay length
at the vertex detector edge drop of efficiency
indications that this is due to faulty track selection
Plans:
extend study to ccbar events, combine with flavour tagging
Sonja Hillert, University of Oxford Simulation Software Meeting ~ DESY 28 June 2005 p. 11
Flavour tagging Study e+e- qqbar events (all flavours except for ttbar), so far using ‘JAS3’ framework
neural net used for flavour tagging: including the primary vertex momentum (left) as input
variable, in addition to secondary vertex parameters, improves b/c jet separation by 10%
performance for c-tag
c-tag efficiency
b-j
et
mis
-ta
g p
rob
ab
ilit
y
0
0.1
0.2
0.440.30
blue: use only secondary vertex parameters
magenta: also use primary vertex momentum
Sonja Hillert, University of Oxford Simulation Software Meeting ~ DESY 28 June 2005 p. 12
Plans for Snowmass
presentation of results on vertex charge reconstruction over range of ECM values
comparison of SiD detector concept and formerly European concept
in terms of vertex charge reconstruction using SGV;
in particular look at performance at edge of polar angle range,
where difference between the detectors is expected
(SiD vertex detector includes forward disks, LCFI-detector does not)
Sonja Hillert, University of Oxford Simulation Software Meeting ~ DESY 28 June 2005 p. 13
Additional Material
Sonja Hillert, University of Oxford Simulation Software Meeting ~ DESY 28 June 2005 p. 14
Vertex charge reconstruction
Vertex charge reconstruction studied in at ,
select two-jet events with jets back-to-back, contained in detector acceptance
need to find all stable B decay chain tracks – procedure:
run vertex finder ZVTOP: the vertex furthest away
from the IP (‘seed’) allows to define a vertex axis
reduce number of degrees of freedom
cut on L/D, optimised for detector
configuration under study, used to
assign tracks to the B decay chain
by summing over these tracks obtain Qsum (charge), PTvtx (transverse momentum), Mvtx (mass)
vertex charge
Pt-corrected mass used as b-tag parameter
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Sonja Hillert, University of Oxford Simulation Software Meeting ~ DESY 28 June 2005 p. 15
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Changes since LCWS 2004
between LCWS04 and ECFA workshop (Durham) :
optimised cut on L/D, masked KS and
dropped ISR while studying vertex charge reconstruction for fixed jet energy
(otherwise lose ~ 85% of generated events through back-to-back cut on jets)
include information from inner vertices: seed vertex is ZVTOP vertex furthest from IP;
assigning tracks contained in ‘inner vertices’ to B decay chain regardless of their
L/D value improves vertex charge reconstruction (for large distances of seed vertex
from IP, L/D cut is much larger than required to remove IP tracks)
Lmin ~ 6mm for D ~ 30 mm
an atypical event
with a large distance of
the seed vertex from the IP
Sonja Hillert, University of Oxford Simulation Software Meeting ~ DESY 28 June 2005 p. 16
Improvement of reconstructed vertex chargeA
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