Transcript Document
Studi di Efficienza dell’ITS
Giuseppe E Bruno
Università di Bari and INFN - Italy
Outline
motivations
methods:
ITS: TPC+ITS(-1 layer) tracks
SPD: SPD standalone tracklets
strategy with data
conclusions
intended for
first paper
Plane efficiencies: what’s for ?
from data to a paper
data taking raw data
reconstruction ESD standard AOD
analysis:
standard AOD user AOD
selection of candidates
computation of corrections for acceptance and
reconstruction inefficiencies
estimate of the systematics errors (here
corrections always play a role)
writing of the paper
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Efficiency of a given ITS layer: how ?
build tracks without using that layer
search for clusters compatible with the
track predictions on that layer
Eff=Nsucc/Ntracks
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Implemented ITS segmentation
pixel: chip by chip
Eff= 99%
eff 0.5% eff
n. of tracks
layer 1: 400 chips
layer 2: 800 chips
160K
323K
drift: chip by chip
layer 3: 672 chips
layer 4: 1408 chips
271K
567K
strip: module by module
layer 5: 748 modules
layer 6: 950 modules
302K
383K
The code is flexible enough to easily reduce/enlarge segmentation
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A general scheme
Container Class
Any tracker:
Plane Efficiency
AliITSPlaneEff
• AliITStrackerMI
• ITS standalone
• Trackleter
• Macro: ITS refitted
tracks + clusters
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AliITSPlaneEffSXD
i/o
i/o
Data Base
Root Files:
Efficiencies
histos of residuals, etc.
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Plane efficiency measured using high quality
tracks: TPC + 5/6ITS
track
zoom
ITS
TPC
Not available on day 1: it can be applied after ITS and TPC alignement
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Tuning of AliITStrackerMI
residual misalignment scenario (after alignement):
AliITSRecoParam *AliITSRecoParam::GetPlaneEffParam(Int_t i)
// optimized setting for SPD0 (i==0)
if (i==0 || i==1) {
param->fMinPtPlaneEff = 0.200; // high pt particles
param->fMaxMissingClustersPlaneEff = 1; // at most 1 layer out of 5 without cluster
param->fRequireClusterInOuterLayerPlaneEff = kTRUE; // cluster on SPD1
//param->fOnlyConstraintPlaneEff = kTRUE;
}
if (i==2 || i==3) {
param->fMinPtPlaneEff = 0.200; // high pt particles
param->fMaxMissingClustersPlaneEff = 1; // at most 1 layer out of 5 without cluster
param->fRequireClusterInOuterLayerPlaneEff = kTRUE;
//param->fOnlyConstraintPlaneEff = kTRUE;
}
if (i==4) {
…….
a try also for full misalignement (day one):
method can be applied to SSD; bias in SPD and SDD
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results for 30K pp m.b. events
residual misalignemet
1.0
0.8
0.6
0.4
SPD0
0.2
chip
v4-13-release
1.0
0.8
0.6
SPD1
0.4
0.2
chip
1.0
0.8
0.6
0.4
SDD1
0.2
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chip
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results for 20K pp m.b. events
initial misalignemet
SPD1 (similarly SDD)
module
method can be applied with inital misalignemet only for SSD
SSD0
module
SSD1
module
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Evaluation of the SPD chip efficiencies
using tracklets
?
Motivations:
SPD chip efficiencies with tracks
would not be available on day one
pixel “efficiency” from map of hits is
not an absolute measurements
Strategy:
build tracklets using two “points”:
reconstructed primary vertex (at least Z)
cluster on one layer
search - on the other layer - for a cluster
compatible with the tracklet prediction
Effmes(chip)=N
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succ(chip)/Ntried(chip)
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?
This would be true eff. if
- no “secondaries”
- no background
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Evaluation of the SPD chip efficiencies
using tracklets
Success
Failure
q
Fiducial window
Fiducial window
Df
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Df
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Corrections: (I)
“Non-reconstructables” (largest bias)
E.g. most of
secondary particles
About 15% (30%) of the tracklet
predictions on the outer (inner) layer
would not match a cluster due to
non-reconstructable particles (mainly
secondaries), not to chip inefficiency
A method based on MonteCarlo is needed to correct for
“non-reconstructables” (but it would rely on the
efficiencies themselves)
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Correction for “non-reconstructables” (i)
Eff
mes
N succ
tries
N
N
succ
Eff
tries
N
rec
N
reconstructable tracklets
Eff
true
true
N
rec
N
n.rec
non reconstructable tracklets
n.rec
N
mes
Eff 1 rec
N
N n.rec
can only be determined from MC simulation
rec
N
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Correction for combinatorial background (i)
Another cluster within
the fiducial window from a
different particles
Missing cluster
due to inefficiency
true
bckgnd corrected
Eff
back Eff
n.rec
N
mes
Eff 1 rec back
N
bckgnd
1 Eff
true
bckgnd corrected
Computed using a numerical iteration
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Correction for combinatorial background (ii)
Eff
bckgnd
is the probability to reconstruct a tracklets
from two uncorrelated Rec-Points
It can be evaluated on the data themeself, by (e.g.)
applying a rotation of 180° around the z axis to all the
RecPoints on a given layer
All the introduced quantities have to be evaluated chip by chip,
e.g.,
N succ (chip)
N n.rec (chip) Eff bckgnd (chip)
N tries (chip)
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N rec (chip)
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Implementation of the “trackleter” and results
The trackleter has been developed,
intensively tested and tuned
It is in AliRoot since v4-13-Release:
ITS/AliITSTrackleterSPD
Two MC productions (30K events each) for
testing/developing the procedure
1. expected efficiencies: ~ 100% for working chips,
0% for a few dead modules/chips
2. unrealistically low chip efficiencies (67%)
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Results: sample 1
30K pp min-bias
Expected efficiency:
overall:# dead < 0.1% 100% efficiency
1 dead half stave in the inner layer
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Eff true
Eff mes
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Results: sample 2
30K pp min-bias
Ad hoc created Pixel dead map
11 contiguos dead columns in each chip (32 columns/chip)
block of dead columns randomly placed inside each chip
Exspected chip efficiency =
11/32 + 21/32*chip_overlapping + 0.16/32 =
65.6% +
≈1.3%
+ 0.5% =
67.5%
about 2%
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charge diffusion
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Results: sample 2
true
Eff
mes
Eff
Expected ≈67.5%
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Procedure for data
special macros to be run after standard
reconstruction for sub-set of data (chuncks)
recplaneeff.C (x6) ITS+TPC tracking
EvaluateSPDEffWithTracklets.C (in
$ALICE_ROOT/ITS)
output: root files with PlaneEff statistics,
histos, etc.
2nd task to collects outputs from several
chuncks, sum up statistics and update OCDB
Implemented and tested in the official PDC08 MC
productions for first physics this summer
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Results of a mock analysis for one of
such productions
“data” = Pythia (10k events)
MC=Phojet (100k events)
Field= 0.5T
Energy=900GeV
Eff corr
Eff raw
SPD0
chip n.
Eff corr
Eff raw
SPD1
chip n.
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Conclusions
a framework for the evaluation of the ITS
efficiency has been developed (since our last
meeting in Frascati)
it has been designed for being exportable to
other barrel detectors (e.g. TOF,TRD)
a special method for SPD with tracklets has
been implemented in view of the first paper
Tools are integrated in the standard
reconstruction
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Backup slides
Thank you Andrea
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Code implementation for ITS
AliITSRecoParam: steer
special setting of tracking
for Plane Efficiency
determination, e.g.
SetLayerToSkip(i)
Reco Param
AliITSRecoParam
ITS Tracker
AliITStrackerMI
Plane Efficiency
AliITSPlaneEff
AliITSPlaneEffSXD
AliITStrackerMI:
tracking without the
plane under study
search for the clusters
on the skipped layer
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i/o
Data Base
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i/o
Root Files:
histos of residuals, etc.
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possible developments
require special constraint of the track
to the primary vertex
Improve spatial precison of track at
intermediate layers
use innermost points
to build a 2nd
track
improve track
parameter by
combining the
two halfs
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SDD segmentation for efficiency evaluation
layer 3:
14 ladders
1 ladder=6 detector
tot. 84 detector
layer 4:
22 ladders
1 ladder=8 detector
tot. 176 detector
each detector divided
in 8(chips) times,
eventually 2 (drift
direction) zones
layer 3: 672 (*2) zones
layer 4: 1408(*2)zones
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Correction for “non-reconstructables” (ii)
N n.rec
(chip) can only be determined from MC simulation
rec
N
N n.rec
N rec
moreover, for the chips on a given layer,
depends on the efficiencies
of the chips on the other layer (that used to build the tracklet prediction)
In fact, Nrec(chip) and Nn.rec(chip) are computed by counting the
number of tracklet predictions on the given chip with or without,
respectively, at least one MC TrackRef associated to the same particles
which generated the RecPoints used to build the tracklet prediction
(info from MC Stack), which is inside the fiducial window, as for real
tracklets
In principle, one should apply an iterative procedure to compute
this ratio; actually the usage of Effmes(chip) is enough
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