Post on 07-Jul-2018
8/18/2019 Coherent Noise Task Update
1/45
Study of Coherent Noise in SiliconStrip Detector at CMS
Atiq ur RahmanDr. Ashfaq Ahmad
National Centre for Physics, Islamabad
4/2/16 1Coherent Noise Task
8/18/2019 Coherent Noise Task Update
2/45
Motivation
Frameor!
Measurement of Noise
Parameteri"ation of coherent noise
Conclusion
4/2/16 2Coherent Noise Task
Outlines
8/18/2019 Coherent Noise Task Update
3/45
8/18/2019 Coherent Noise Task Update
4/45
CMS Tracker overview
4/2/16 Coherent Noise Task 4
8/18/2019 Coherent Noise Task Update
5/45
Module %y&es
4/2/16 Coherent Noise Task 5
8/18/2019 Coherent Noise Task Update
6/45
Motivation
%o measure coherent noise in silicon stri& detector at
CM$.
Develo& a criteria'method to fla( noisy modules in
$ilicon $tri& %rac!er.
%he corrections derived is intended to be used durin(trac! reconstruction
)ould reduce the chance of reconstruction of noisy
clusters and hel& to (et more &recise A#D data for
so&histicated analysis.
64/2/16 Coherent Noise Task
8/18/2019 Coherent Noise Task Update
7/45
*sin( CM$$)++-+-/&atch D0M and $i$tri& &ac!a(es
Root
Identification criteria
Fec1crate'slot'rin('CC*'M#D*23'22D'I-C etc .
4''-'56'-'7 vs DetID
Data1 Pedestal Run
4/2/16 7Coherent Noise Task
Framework
8/18/2019 Coherent Noise Task Update
8/45
8/18/2019 Coherent Noise Task Update
9/45
8/18/2019 Coherent Noise Task Update
10/45
Normali"ed Noise difference
Normali"ed noise difference is assumed to have
a nominal value hen there is no correlated
noise and &oints deviatin( from this line are
considered to be in correlation or anti/correlation
as a first test.
=
4/2/16 10Coherent Noise Task
Noise Calculation Cont!
8/18/2019 Coherent Noise Task Update
11/45
Noise Modes
De&endin( u&on the different trends of noises
hich e encounter in $ilicon stri& trac!er, edefine noise modes
Normal mode
)in( sus&ect Dead or lo noise mode
)eird mode
Normal is one hich is normal trend in trac!er,
in( mode is >in( ? sha&ed noise trend , Dead
mode is none res&ondin( and havin( "ero noise
mode
4/2/16 11Coherent Noise Task
8/18/2019 Coherent Noise Task Update
12/45
Normal Mode
4/2/16 12Coherent Noise Task
8/18/2019 Coherent Noise Task Update
13/45
Correlation coefficients for normal mode
4/2/16 Coherent Noise Task 13
8/18/2019 Coherent Noise Task Update
14/45
"in# Mode
4/2/16 14Coherent Noise Task
8/18/2019 Coherent Noise Task Update
15/45
Correlation Plots for "in# Mode
4/2/16 15Coherent Noise Task
8/18/2019 Coherent Noise Task Update
16/45
Paraolic or $uadratic fit
$i"e of >A? decide hether the &arabola is ideor narro.
Positive >A? shos it is u&ard.
>@? is the (eneral slo&e in equation of line.
>C? shift in the &arabola i.e u& or don
minB/:@'-A;
minB/
:min, min; is verte of the &arabolas
=
4/2/16 16Coherent Noise Task
8/18/2019 Coherent Noise Task Update
17/45
Parameteri%ation of correlations coefficients
4/2/16 17Coherent Noise Task
8/18/2019 Coherent Noise Task Update
18/454/2/16 18Coherent Noise Task
Map for Parameter &
8/18/2019 Coherent Noise Task Update
19/454/2/16 19Coherent Noise Task
Map for the Parameter '
8/18/2019 Coherent Noise Task Update
20/45
8/18/2019 Coherent Noise Task Update
21/45
8/18/2019 Coherent Noise Task Update
22/45
Map for +)min*
4/2/16 22Coherent Noise Task
8/18/2019 Coherent Noise Task Update
23/45
()min* vs &
Only the large value of A do not ensure that thechannel will have large correlation. TheParameter “C” play a decisive role in additionto A . We deicide about the correlated channelwith only !min" not with C or A individually.4/2/16 23Coherent Noise Task
8/18/2019 Coherent Noise Task Update
24/45
()min* vs +)min*
Lager values of !"in# is not hurting
our results4/2/16 24Coherent Noise Task
8/18/2019 Coherent Noise Task Update
25/45
& vs +)min*
All the channels having large value of A parameteralways lies in Physical range of the AP#. $o weshould not worry about the non%physical strips.&asically ' such channels have a almost linear
distribution of correlation coe(cients. That is why weare ettin the lar e )!min".4/2/16 25Coherent Noise Task
8/18/2019 Coherent Noise Task Update
26/45
8/18/2019 Coherent Noise Task Update
27/45
8/18/2019 Coherent Noise Task Update
28/45
+)min* vs C
All the anomalous values of the parametersmostly coming in the Physical range of AP#
$trips.4/2/16 28Coherent Noise Task
8/18/2019 Coherent Noise Task Update
29/45
Channels in the tail of !min" after the chi%$*uare cut As we have very small windowfor y!min" in the distribution !min"+%.,-
Channels in the %ails of :min; for 2eft+fit
4/2/16 29Coherent Noise Task
Channels in the Tails of ()min* for ,eft fit
8/18/2019 Coherent Noise Task Update
30/45
Channels in the Tails of ()min* for ,eft-fit
4/2/16 30Coherent Noise Task
Channels in the tails of ()min* for ,eft fit
8/18/2019 Coherent Noise Task Update
31/45
C a e s t e ta s o ) * o e t- t
4/2/16 31Coherent Noise Task
Channels in the tails of ()min* for ,eft fit
8/18/2019 Coherent Noise Task Update
32/45
Channels in the tails of ()min* for ,eft-fit
4/2/16 32Coherent Noise Task
8/18/2019 Coherent Noise Task Update
33/45
$olution to nois% an& &ea& 'hannel (ro)le"
As we were getting the noisy as well as thecorrelated channels in the tails of the y!min"which were contaminating our results forcorrelated channels. have /agged the noisy or
dead strips as not 0tted to our general*uadratic!as well linear model". We haveobtained the distribution of chi s*uare and got agood cut for channels.
n rest of the channels we get only correlatedchannels in the tails of !min".
114/2/16 33Coherent Noise Task
8/18/2019 Coherent Noise Task Update
34/45
4/2/16 Coherent Noise Task 34
Chi s*uare &istri)ution )efore an&
8/18/2019 Coherent Noise Task Update
35/45
Chi s*uare &istri)ution )efore an&after 'ut
12
The 'hannels in the tail of Left Chi+$*uare)efore the 'ut are (resente& in later sli&es, -t'an .ag even a s"all "issing of stri(s
4/2/16 35Coherent Noise Task
e c anne s n e a s o e %
8/18/2019 Coherent Noise Task Update
36/45
e c anne s n e a s o e $*uare
we have got almost all the noisy and deadchannels in the tails of chi%s*uare which were in
the tail of the !min" before the chi%s*uare cut. The Chi s*uare de0nition is highly sensitive forthe channels which have small number of noisystrips.
The *uadratic 0t model is a general 0t and linear0t is a special case of *uadratic 0t with parameter“A” e*ual to 3ero.
All the non%Physical )!min" have nominal valuesof parameter A and !min". They do not hurt ourresults. This indicate that they have astonishinglylinear distribution of correlation coe(cients.
144/2/16 36Coherent Noise Task
M f Chi S
8/18/2019 Coherent Noise Task Update
37/45
Map for Chi.S$uare
154/2/16 37Coherent Noise Task
Channels in the tails of chi.S$uare for
8/18/2019 Coherent Noise Task Update
38/45
16
Channels in the tails of chi S$uare for
,eft-fit
4/2/16 38Coherent Noise Task
Channels in the tails of chi/$quare for 2eft+fit
8/18/2019 Coherent Noise Task Update
39/45
174/2/16 39Coherent Noise Task
Channels in the tails of chi.S$uare for ,eft-fit
8/18/2019 Coherent Noise Task Update
40/45
89
$ -
4/2/16 40Coherent Noise Task
8/18/2019 Coherent Noise Task Update
41/45
Staility of the ()min* in SST TI'
8/18/2019 Coherent Noise Task Update
42/45
y ) *
Does this y:min; is stable in the other $ub detectorsE
TI'
4/2/16 42Coherent Noise Task
T0C1
8/18/2019 Coherent Noise Task Update
43/45
T0C
4/2/16 43Coherent Noise Task
TO'
8/18/2019 Coherent Noise Task Update
44/45
TO'
4/2/16 44Coherent Noise Task
Conclusions
8/18/2019 Coherent Noise Task Update
45/45
Conclusions
%he :min; is used to decide about the correlation of the channels. No
any individual &arameter can be used to quantify the correlation. >A? and
>C? are both sensitive but lar(e value of >A? may not decide that thischannel is sensitive for correlation. $imilarly , the only value of >C? do
not ensure us about correlation only.
@ut in the case of terribly linear distribution of correlation coefficients, e
can say that the >C? &arameter becomes same as :min;.
%he chi/$quare value for the quadratic fit (ives a (ood criteria to
eliminate the noisy channels. %he channels hose chi/square value for
the quadratic fit is (reater than to are all noisy.
%he values of :min; ill be used for fine tune of cluster char(e.
%his criteria can be used to correct the CM$ data and can reduce the
chance of noisy clusters. ere is our ti!i &a(e htt&s
1''ti!i.cern.ch'ti!i'bin'vieauth'CM$'$i$tri&NoiseCorrelation
https://twiki.cern.ch/twiki/bin/viewauth/CMS/SiStripNoiseCorrelationhttps://twiki.cern.ch/twiki/bin/viewauth/CMS/SiStripNoiseCorrelationhttps://twiki.cern.ch/twiki/bin/viewauth/CMS/SiStripNoiseCorrelationhttps://twiki.cern.ch/twiki/bin/viewauth/CMS/SiStripNoiseCorrelation