Status of dE/dx
Offline Software
WANG Dayong
wangdy@mail.ihep.ac.cn
Institute of High Energy Physics
Jan 10,2006
Outline
dE/dx software :OO design and development
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MdcDedxAlg : Reconstruction
DedxCalibAlg : Calibration
DedxCorrecSvc : Public service for dE/dx correction
Calibration and systematic corrections
Important systematic and enviromental effects
Calibration parameteriazation
Reconstruction algorithm studies:
Different estimation of most prob Eloss
Ionization Curve studies
Resolution and residual bias correction
Summary
dE/dx :Particle ID with energy loss
measurements
Principle:
MDC
trackin
g
dE/dx~f(v)
Particle
type
info
P =  · m
Implementation: C++ programming under BOSS
framework
Components: MdcDedxAlg, DedxCalibAlg,
DedxCorrecSvc
Design goal: Resolution 6—7%, good seperation
Requirements and data flow
MDC digits
AIM: to give the partID
information from the list of
pulse heights of hits on the
MDC track, and store them
into TDS
some corrections are
performed to get unbiased
dE/dx information.
Some proper dE/dx
estimators are constructed
MDC digits
MDC Tracking
Tracks
Transient
Data Store
(TDS)
MDC digits
Tracks
Recon dE/dx
Tracks
dE/dx
Reconstruction
Recon dE/dx
Recon dE/dx
Global Particle
partId info
Identification
。。。
Apparent dataflow
Real dataflow
physics analysis
Overview of the software
converter
Transient Data
Store
DST
<<uses>>
<<uses>>
EventDataSvc
MdcDedxRecon
DedxCalibAlg
<<uses>>
<<uses>>
DedxCorrecSvc
<<uses>>
<<uses>>
<<uses>>
CalibDataSvc
Transient Calib
Data Store
Calibration
const
converter
MdcGeomSvc
dE/dx calibration package
Algorithm
DedxCalibAlg
DedxCalib
+initialise()
+execute()
+finalise()
+BookHists()
+FillHists()
+AnalyseHists()
+WriteHists()
+getChargeOffCorr()
+ReadParameters()
+WriteParameters()
DedxCalibWireGain
DedxCalibLayerGain
DedxCalibDriftDist
<<uses>>
DedxCalibParameters
DedxCalibSaturation
DedxCalibZpos
DedxCalibRunByRun
DedxCorrecSvc
IInterface
+queryInterface()
IService
IProperty
+name()
+initialize()
+finalize()
+setProperty()
+getProperty()
Service
IDedxCorrecSvc
+StandardCorrec()
MdcGeomSvc
<<uses>>
<<uses>>
CalibSvc
DedxCorrecSvc
-m_run
-m_calib_flag
-m_calib_const
+StandardCorrec()
+WireGainCorrec()
+DriftDistCorrec()
+SaturCorrec()
+ZdepCorrec()
+LayerGainCorrec()
+GlobalCorrec()
+PathL()
Calibration data structure
double m_wireg[6860];
double m_ggs[4][43];
double m_ddg[4][43];
double m_zdep[4][43];
double m_layerg[43];
double m_gain;
double m_resol;
Sys. effects and dE/dx corrections
①
②
③
④
⑤
⑥
⑦
⑧
Gain variations among cells
Sampling length corrections
Drift distance dependence
Longitude position(z) dependence
Space charge effect
Charge gain non-linearity: from electronics
Corrections related to particle type
Run by run pulse height correction:Dependence
on the sense wire voltage ，temperature,
pressure and other environmental effects…
Parameterizations in calibration
 Gas gain:
 Standard Landau distribution
 Vavilov distribution
 Asymmetric Gaussian distribution:
 Space charge effect: general form of Q’=Q/(1-k(θ)*Q)


BesII: fit with polynomial：a=F(40°)/F(θ) Q’=Q*a
Q
CLEOII formulation:
 (
)
'
cos



Q  Q
δ:longitude range of avalanche
Q
1  (
)
 Babar formulation:
cos   
 Parameterization of other effects:
These parameterizations are
to be tested by long-model
data analysis
 3 order polynomials (presently implemented)
 Chebyshev series with the 1st kind of Chebyshev polynomials
Comparison and choice of dE/dx curve
 Sternheimer(A) is better
Comparison of
Sternheimer and
Va’vra formula:
A
at high momentum end
 Va’vra(B) is relative
better at low momentum
end
 Practical global
parameterization of curve
is prefered
B
BESIII Simulation Preliminary
Landau formula X
P2~0 4-par fit X
Global 5-parameter fit for phmp_nml vs 
p5




dE
p1 
1
p4
 
 p 4  p 2    ln  p3  

dx  
   


 binning with nearly the
same statisticsat each point
to reduce the error
Beam-gas proton
Using
garbage events in
order to fastly calibrate this
curve for BESIII in future
Cosmic rays
BESII data
Radiative bb
A
uniform formula to
avoid discrete expression
for density effect
The
curve fit the BESII
data OK
The best dE/dx curve obtained
BESIII Simulation Preliminary
p5




dE
p1 
1
p4

 
 p 4  p 2    ln p3  

dx  
   


1. In whole BesIII momentum range: 0.15—2GeV/c, good uniformity is seen
with different particles and with momentum overlap;
2. Quality of curve fitting is good in the whole range
3. The fitting results is quite stable
Algorithm studies: different estimation
of most probable energy loss
Landau distribution has no definite mean. The algorithm
used must estimate the most probable energy loss
 Truncated mean
 Double truncated mean: truncate at both ends
idea:these methods
 Median
give less bias to large
 Geometric mean
values,then the
satured hits have less
 Harmonic mean
effect to give better
shape and better
 Transformation:
seperation
 Logorithm truncated mean: studies based on BESII data
Different estimation of most probable
energy loss: resolution
5.51%
5.34%
6.06%
5.09%
BOOST MC,
MIP muon
5.75%
5.71%
Truncation rate: 0.7
5.44%
2.61%
Different estimation of most probable
energy loss: seperation power
Pi/K
Pi/P
0.7GeV 1.2GeV
Pi/K
Pi/P
0.6GeV 1.1GeV
Pi/K
Pi/P
0.7GeV 1.2GeV
Pi/K
Pi/P
0.75GeV 1.3GeV
BOOST MC,
MIP muon
Pi/K
Pi/P
Pi/K Pi/P
0.7GeV 1.3GeV
0.7GeV 1.2GeV
Pi/K
Pi/P
0.7GeV 1.3GeV
Pi/K
Pi/P
0.75GeV 1.3GeV
Comparison of linear&logorithm TM
Logorithm TM(right figure),compared to plain TM(left figure):
Suppress high-end residual Landau tail
The distribution more Gaussian like
shape is more Gaussian-like
Pull width: 1.020
Cosmic rays
0.9995
BESII DATA,
J/Psi hadrons
shape is more Gaussian-like
Pull width: 0.8477
Radiative Bhabha
0.9304
Study of truncated mean method
Well established
method of dE/dx
estimation
Simple and robust
Rejection of lower end
hits to remove
contributions from noise
and background
fluctuation
Truncation of higher
tail to remove Landau
tail due to hard collisions
Just cooresponding
to ~5% lower cut
Landau tail
After truncation,
distribution just
Gaussian-like
BOOST MC,
1GeV electrons
Resolution curve with
different truncation rates
70% truncation ratio is
adopted for the algorthm
Number of good hits is
required to no less than
10 for each track
 Resolution from
perfect MC consistent
with empirical formula
BOOST MC,
1GeV electrons
Calibration of σdE/dx
Q dependence of
σdE/dx
σ /Q= p0+p1*ln（Q） ，
p0,p1 is fitting parameters
Hits number and polar
angle dependence
Empirical
formula :
 
81.0n 0.46  
I
n 
2.35
0.32
  n -0.46 ,   sin  0.32
  0.153
Z
1
 t  2
A

σdE/dx~ polar angle relationship
 norm  p1sin 
p0
σ ~ hits number relationship
dE/dx
 norm  p1Nhit
p0
Present performance(I)
5.96%
Software are robust
Basic calibration and correction,and need more
dE/dx resolution can reach design requirements: 6-7%
Present performance(II)
χ distribution for Kaon sample
Distribution of

(dE / dxmea  dEdxexp )
 dE / dx
Prob（K）distribution for Kaon sample
is nearly a normal N(0,1)distribution
Distributions of probability function are flat
Our estimation is unbiased and can provide good partId info
Present performance(III)
dE/dx seperation for 5 particles(MC)
seperation power with dE/dx
Good particle seperation in a wide range for different particles
The important π/K seperation(3 σ )can reach nearly 800MeV/c
Particle identification efficiency is more than 90% with MC samples
summary
 OO designed dE/dx software is developed
under BOSS, released and used for physics
 Calibration algorithms and service are
developed and many corrections performed to
get unbiased estimation of dE/dx
 Different reconstruction algorithms are
explored to get best performance
 Particle id is tested with MC samples, dE/dx
resolution, distributions, pid efficiency is
satisfactory.
 To reach BESIII design goals, there are still
much to understand and deal with
Thank you
谢谢！
Backed -up slides…