Transcript PowerPoint プレゼンテーション

MEG DCH Analysis
MEG Review Meeting
February
2009
18 18
February
2009
W. Molzon
For the DCH Analysis Working Group1
DCH Analysis
Outline
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Impact on MEG performance
Analysis algorithms
DCH Calibration
DCH position resolutions: Rf and q
Positron reconstruction
– Momentum
– Position and angle at target
– Projection to timing counters
• DCH efficiency
• Required improvements in analysis
– Drift model
– Reducing noise and its impact on resolution
– Improved tracking efficiency with lower than expected DCH efficiency
– Fitting
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DCH Analysis
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Positron Spectrometer Impact on MEG Performance
• Select on positron energy within interval near52.8 MeV
– For fixed m→eg acceptance, BG/S proportional to dp
• Select on qeg near p
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For fixed acceptance, BG/S proportional to df x dq
photon position resolution ~ 6 mm sRMS
Track fitting angle uncertainty
Position of stopping target: uncertainty 0.5 mm
(MEG prediction sRMS=180 keV/c)
(MEG prediction sRMS = 8x8 mrad2)
 ~9 mrad both f and q
 12 mrad f, 6 mrad q
 ~6 mrad f
• Project to target and timing counter and correct te for propagation delay
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For fixed acceptance, BG/S proportional to dt
(MEG prediction sRMS = 64 ps, ~2 cm)
Projection to target has negligible uncertainty
Uncertainty in timing counter projection dominated by scattering and E loss after spectrometer
Improvements needed from incorporating position at timing counter and material between
spectrometer and timing counter into fit.
• For all effects, tails in resolution function  loss of acceptance proportional to integral
in tail, small increase in background because source of background is uniform
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DCH Analysis
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Tracking Analysis
• Outline of algorithms
– Extract hits from waveform on each cell: two anode ends, four pads
– Extract hit position in Rf from hit time and in Z from anode and pad charges
– Form clusters of hits on a particular chamber coming from single particle
– Form track candidates from groups of hits consistent with Michel positron
– Fit the hits from track candidates to form tracks
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DCH Analysis
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Waveform Analysis
• Based on waveforms on 2 anode ends and 4 pads associated with each cell
waveform noise limits resolution
– DRS voltage calibrated with on-board constant voltage presented to input of DRS
– DRS time calibrated with off-board sine-wave of known frequency presented to each board.
Bin-by-bin time calibration done for each DRS channel (~2x105 points)
– Readout rate dependent baseline offset for some DRS bins not corrected, trigger waveform
crosstalk onto DRS not corrected – hardware improvements anticipated
– Improvement in noise level would significantly improve resolution
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DCH Analysis
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DCH Calibrations and Corrections
• Alignment of chambers – radial offsets, z offsets, chamber tilts
– All from fits to Michel data
– Typical systematic residuals after alignment small ( < 100 mm)
• Calibration of preamp gains, effective wire length
– Use known periodicity of cathode pads to calibrate anode preamp gains, input
impedance, wire resistivity that affects anode z position
– Calibrate relative gains of cathode pads by ratio of signal on two ends of pads to
sine function with variable relative gain
• Correct drift times for signal propagation on wire – reduce dispersion on
time difference between two ends by ~20%
• Identify and correct for incorrect pad cycle assignment due to errors in
anode Z position exceeding 2.5 cm
• Measure effect of noise on pad charge measurements on Z resolution –
optimize integration time to minimize effect of noise
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DCH Analysis
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Hit Finding
• Smooth waveforms to reduce high frequency noise
• Determine constant baseline offset event-by-event for each waveform
– Only time before hit used; small slope from earlier hits not corrected
• Find max peak in anode waveform – iterate after removing signal in peak
• Integrate around peak in limited time interval to get 2 anode, 6 pad charges– optimized
to minimize impact of noise on charge integration: typically 50 ns.
• Get hit time from simple threshold discriminator on unsmoothed waveform – correct for
propagation along wire using Z coordinate
• Get Z first from anode charge division, then from interpolation with pads
Time difference two ends
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DCH Analysis
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Cluster Finding
• Find group of hits consistent with coming from single charge particle
– Start with groups of hits in contiguous wires on chamber
– Split clusters that have hits at inconsistent Z locations
– Identify and fix clusters that have hits separated in Z by one pad cycle (5 cm) due to
incorrect anode Z position
– Reassess assignment of hits to clusters during track-finding, when track angle at
the cluster is known
– After all clusters with > 1 hit are found, assign unmatched hits as “single hit clusters”
Single hit clusters
Correct wrong
pad cycle
3,4 hit clusters
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DCH Analysis
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Track Candidate Finding
• Find group of clusters consistent with coming from single charge particle with
fixed momentum going through spectrometer
• Self-contained code, independent of TIC (i.e. for track time)
• Start with seed with 3 clusters in 4 adjacent chambers at R>Rmin
• Given a seed, propagate in both directions, adding hits within range in dR and
dZ consistent with Michel momentum
• At each stage, determine track candidate time from drift times
– Consistent radial coordinate, consistent Z coordinate
– Track time that minimizes residual of hit positions to local helix fit
– L/R resolution by minimizing deviations from local helix fit
– Hits can be removed from clusters at tracking stage
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DCH Analysis
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Track Fitting
• Kalman filter using hits found by trackfinder
– Uses fully aligned chamber coordinates from optical alignment + software alignment
– Use hit-by-hit uncertainty in Rf and Z coordinates parameterized as function of hit
charge, magnitude of drift distance (determined from data)
– Phenomenological corrections to drift time vs. drift distance based on
parameterization of data
– Removes hits that are inconsistent with positron trajectory
– Group of clusters consistent with coming from single charge particle with fixed
momentum going through spectrometer
– Optimization of fitting algorithm for sparse hits to be done
– Incorporation of TIC position into filter to improve trajectory after spectrometer to be
done
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DCH Analysis
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Intrinsic Drift Chamber Performance from Tracking
• Rf position resolution
– Look at difference in hits in 2 planes in chamber projected to central plane using trajectory information:
insensitive to multiple scattering
– Typical spatial resolution of 260 microns
– Systematic effects with drift distance and angle – ad-hoc corrections applied
dr for opposite side more
sensitive to errors in track time
sRMS of central region ~260 mm
non-Gaussian tails, larger for
opposite side hits
• Z position resolution
– Similar technique to that for Rf resolution
Inferred
sz = 0.15 cm
sRMS=1.61
(z1 – z2) / √(σ2z1+ σ2z2)
Za-Zb
18 February 2009
normalized Za-Zb
DCH Analysis
Za-Zb vs charge
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Definition of Selection Criteria for Tracking Efficiency, Resolution
Criterion
Loss
c2/dof < 20
Nhits > 7
[25%]
dE < 0.0012
df < 2°
dq < 0.6°
4.59° < |qe-90| < 21.49°
[25%]
|f| < 57.3°
Target |Ze| < 7.5 cm
Target |Ye| < 3.5 cm
|tDCH| < 50 ns
[12%]
|tDCH-tTIC-dnom| < 100 ns
dr+1.9 < 6
dz-0.8 < 20
Tight Cuts have additional requirements Nhits > 9,
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DCH Analysis
dE < 0.0006
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Momentum Resolution from Monte Carlo
• No source of fixed momentum particles – fit to
edge of Michel spectrum, first MC
• Generate Michel spectrum, including radiative
decays – in this study without inefficiencies
• Fit convolution of generated MC spectrum
with single Gaussian to reconstructed MC
spectrum
• Fit range (51.5-54.0) MeV/c
• Done for “tight cuts”
• Resolution worse than original MEG
predictions: DRS noise + ?
• Tails from large angle scattering, pattern
recognition?, others?
sRMS=420 keV
sRMS=420 keV
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DCH Analysis
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Momentum Resolution from Data
• No source of fixed momentum
particles to measure response
function
• Fit to edge of Michel spectrum
to demonstrate resolution
– Generate Michel spectrum with
radiative corrections
– Impose momentum dependence
of TIC acceptance x efficiency –
measured using DCH triggered
data
– Fit measured energy distribution
to convolution of acceptancecorrected Michel spectrum and
hypothetical resolution function
– Edge of spectrum most sensitive
to Gaussian part of resolution
function – fit of high energy tail
very dependent on model for tail
in resolution function
– Currently worse than MC by a
factor of 2, but inefficiencies not
yet in MC resolution fits
18 February 2009
early data
sRMS = 830 keV
tight cuts,
early data
sRMS = 772 keV
late data
sRMS = 1002 keV
tight cuts,
late data
sRMS = 795 keV
DCH Analysis
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Check of Angular Resolution
• No source of positrons of known direction
• Fitting provides event-by-event estimate of dq, df
Calculated
uncertainty
in q, data
6 mrad
Monte Carlo
Calculated
uncertainty
in f, data
12 mrad
Y
Data f~0
Y
• Target designed with holes
to test of resolution in
projection to the target
 infer dq, df
Z
• Take slice in target projection around hole, try to match depth of dip
data to MC
• Position of hole vs. angle of track with respect to target normal
sensitive to target position
• Difficult to quantitatively match distributions
– Beam spot has different shape
– Hole on falling distribution
– Work in progress
– First try requires increasing resolution in dZ, dY by 50%
Data f>0
Data f<0
• Position of hole good to at least 1 mm –
neglibible contribution to qeg uncertainty
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DCH Analysis
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Project to TIC, Require Space and Time Match, Calculate Propagation Time
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Need to correct for track propagation delay to precision of 50 ps  track length to 1.5 cm
Trajectory known from target plane through spectrometer to very good precision
Projection to TIC complicated by material after spectrometer causing scattering, energy loss
Currently, project to fixed f of timing counter with signal using propagation of Kalman state
vector
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No correction for mismatch with reconstructed position in timing counter
Typical propagation distance is of order 1 m
Systematic uncertainties in dR, dZ seen, of order 1 cm
First attempts at simple corrections to path-length based on dR, dZ not successful
• Fully corrected photon-positron timing difference currently at level of 150 ps in RD signal with
photon energy above 40 MeV
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DCH Analysis
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Use DCH Data and Analysis to Study Timing Counter
• Use DCH trigger data
– Require 4 hits in 5 contiguous chambers
– Run standard analysis, positron selection criteria
– Measure probability of having a TIC hit
Loose matching criteria
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Tighter position match, timing criteria
DCH Analysis
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Tracking Efficiency From Monte Carlo
• Put actual typical patterns of inefficient chambers into Monte Carlo
• Generate signal events over extended region |f| < 1, cos(q) < 0.45
• Define efficiency as
(# positrons accepted in fiducial region)
(# positrons generated in fiducial region)
Chamber Pattern
MC Efficiency
Date
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90.4
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58.0
12-Sep
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47.0
20-Sep
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21.0
7-Oct
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15.0
30-Nov
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12.0
14-Dec
• Efficiency loss due to track-finding and fitting requirements:
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<2 missing chambers in seed
at least one chamber with 2 planes in seed
<2 missing chamber in track extension each direction
at least 8 hits on fitted track
difficulty with getting track time and resolving L/R ambiguity with many single plane chambers
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DCH Analysis
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Reconstructed Tracks per Trigger
• Look at fraction of events with at least one reconstructed track at high
momentum – measure of relative (not absolute) tracking efficiency
– Absolute scale depends on trigger purity, other factors not relevant to DCH
performance
arrows correspond to typical configurations
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Can We Estimate Tracking Efficiency from Data
• Use highly pre-scaled timing counter
trigger data
• ~ 6000 C total live protons on target
2.8 x 107 m/s/2mA (assume livetime
same for MEG, other triggers
Implies ~ 8400 x 1010 total muon stops
Nm→enn
= 11895
= 8.4x1013
X 107
X 0.30
X 0.182
X (0.92-1.0)
X 0.091
X eDCH
satisfying selection cuts
Number of muon stops
prescale factor
TIC acceptance x efficiency for Michel
fraction of Michel spectrum > 48 MeV
conditional trigger efficiency for TIC
Michel geometric acceptance
drift chamber reconstruction & cuts
counted
calculated
known
measured
calculated
measured*
assumed
unknown
eDCH = 11895 x 107 / 0.3 / 0.182 / 0.92 / 0.091 / 8.4 / 1013 = 0.28-0.31
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Conclusions
• Tracking efficiency in current run is poor, mostly due to chamber performance
• Intrinsic resolutions are not as good as expected
– Rf resolution close to expectations, but tails are more than originally anticipated
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time-distance relationship
operation at less than optimal voltages
noise
perhaps other causes
– Z resolution significantly worse than planned, almost all due to noise
• Momentum resolution not as good as expected with measured Gaussian
uncertainties as input to fitter
– reflection of tails in Rf and Z resolution
– reduced number of hits and shorter tracks
– full inefficiencies not yet represented in MC
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Conclusions
• DCH analysis currently adequate for data with MEG sensitivity of order few x 10-12
– Find radiative decay signal requiring track projecting to timing counter hit, timing
correction for track propagation
– Trigger on and reconstruct with good precision Michel positrons to help with calibration
and understanding of TIC performance
– Reduced background rejection due to reduced momentum resolution adequate at current
MEG sensitivity to m→eg
• Significant improvement in MEG sensitivity per day of running can be achieved
– Improvements in central part of resolution function
• improved chamber efficiency (hardware)
• some (non-trivial) tuning
 reduction of background by 1/2
• Improved noise performance (hardware)
 additional background reduction by 1/2
– Higher chamber efficiency will increase reconstruction efficiency
 increase in sensitivity per day by 3
• Strong effort is needed to achieve MEG sensitivity goal
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DCH Analysis
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