Extracting SAMPA response function · 2018. 5. 30. · Extracting SAMPA response function Pulse...

ExtractingSAMPAresponsefunction

Pulse from Shaper

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ns0 200 400 600 800 1000

0

0.5

1

1.5

2 / ndf 2χ 14.39 / 19

Prob 0.7606p0 13.12± 10.64 p1 96.62± 189 p2 0.1058± 2.336 p3 11.51± 129.6 p4 0.06333± 1.367

/ ndf 2χ 14.39 / 19Prob 0.7606p0 13.12± 10.64 p1 96.62± 189 p2 0.1058± 2.336 p3 11.51± 129.6 p4 0.06333± 1.367

SAMPA palse shape

• UsingplotdigitizertoreadoffpointsfromtheplotanddoafittogettheSAMPAresponsefunction

• Shapereasonablywelldescribedbyfunctional form:

1

𝑝"𝑡𝑝$

%&𝑒(

)%*

+,

Responsefunctioncomparison:SAMPAvsAPV25

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ns0 200 400 600 800 1000 1200

ADC

0

50

100

150

200

250 APV palse shape, shaping time = 56ns

SAMPA palse shape, shaping time = 160ns

palseShape• TheSAMPAresponsecurveismuchlongerthanAPV25

• TheshortestsamplingtimeforSAMPAis50nswhileAPV25uses25ns

• Thesetworeasonswilllikelyincreasethepile-upeffectsandoccupancies

• Itisunlikelywewillhavegoodresults(>90%)fortrackingifweonlytake1samplewithSAMPA

• Itwillbebettertohaveatlest3samplesusingSAMPA

• Forcurrentstudy,Iuse6timesamples

Signaltopedestalnoiseratio

3

• Iftakingonlyonetimesample,typicallywewillhavetheworstsignaltopedestalnoiseratio

• Ifwehavemultiplesamples,wecanusetheaverageofthesamples,whichismorelikelytohavebettersignaltopedestalnoiseratio,asthesignalisalwayspositivebutnoiseisrandomlyfluctuatingaround01. IfthenoisetheGaussian(around0),thereshouldbealwayscancelationifwesummoresamples2. Ifthereisalsosinusoidalnoise,thecancelationmaynotbeobviousifthesamplingtimeismuch

shorterthantheperiodofthenoise

• Forthecurrentpedestalnoiseweputinthedigitization,itisaGaussian+sinusoidalnoisewithperiod200ns(digitizationmodel)

• Ifwetakeonesample,andlookatthepedestalnoisedistribution,itisstillquiteGaussianwithwidth=20.7ADC

• Eventhoughitisgoodtomaximizethesignaltopedestalnoiseratio,weshouldalwaystherecordthefullleadingedgeofthesignalasitcontainsmostofthetimeinformation(unlessweplannottousetimingatall)

Signaltopedestalnoiseratio– SAMPA(50nssamplingperiod)

4number of samples0 2 4 6 8 10 12 14 16

wid

th o

f ave

rage

d pe

dest

al

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Graph

UsingGaussianmodelUsingdigitizationmodel20.7/sqrt(N)

Signaltopedestalnoiseratio– APV25(25nssampleperiod)

5number of samples0 2 4 6 8 10 12 14 16

wid

th o

f ave

rage

d pe

dest

al

4

6

8

10

12

14

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20

22

Graph

UsingGaussianmodelUsingdigitizationmodel20.7/sqrt(N)

6

h1Entries 71864Mean 273.6RMS 186

ADC0 200 400 600 800 1000 12000

50

100

150

200

250

300

h1Entries 71864Mean 273.6RMS 186

efficiency = 0.035

h1


h1Entries 71864Mean 167.4RMS 126.9

ADC0 200 400 600 800 1000 12000

100

200

300

400

500 h1Entries 71864Mean 167.4RMS 126.9

efficiency = 0.026

h1

UsingADCsonstripatclustercenter

Redlineindicates4sigmaofpedestalwidthfor1sample

Redlineindicates4sigmaofpedestalwidthfor3samples

Using1sample(maximumone) Averageof3samples

DuetooverflowofSAMPA(10bits)

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ADC0 200 400 600 800 1000 12000

50

100

150

200

250

300

350

400


efficiency = 0.004

h1h1

Entries 71864Mean 158.5RMS 121.6

ADC0 200 400 600 800 1000 12000

100

200

300

400

500


efficiency = 0.003

h1



Using6samples Averageof9samples

Backgroundsimulationindigitizationandnoiserejection

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• WhenusingAPV25,weuseda275nstimewindowforthebackgroundsimulation(200nsbeforetriggerstarttimeand75nsafter),becausethepulselengthisshortandweconsideratmosttaking3samplesafterthetriggerstarttime

• WhenusingAPV25with3samples,wecomparedtherelativeamplitudesbetweensamplestorejectout-of-timeevents(requireleadingedge)

• CurrentlyforSAMPA,Iuseintotal1100nstimewindowforthebackgroundsimulation(600nsbeforetriggerstarttimeand500nsafter),becausethepulselengthgetsmuchlongerandwewilllikelyneedupto9samples

• Stillsimplyusetherelativeratiobetweensamplestorejectout-of-timeevents,havinginmindthattherearemoreadvancedalgorithmforthispurpose(likefittingtogetmoreprecisetimeinfoforinstance)

Backgroundsimulationindigitizationandnoiserejection

9

• WhenusingAPV25,weuseda275nstimewindowforthebackgroundsimulation(200nsbeforetriggerstarttimeand75nsafter),becausethepulselengthisshortandweconsideratmosttaking3samplesafterthetriggerstarttime

• WhenusingAPV25with3samples,wecomparedtherelativeamplitudesbetweensamplestorejectout-of-timeevents(requireleadingedge)

• CurrentlyforSAMPA,Iuseintotal1100nstimewindowforthebackgroundsimulation(600nsbeforetriggerstarttimeand500nsafter),becausethepulselengthgetsmuchlongerandwewilllikelyneedupto9samples

• Stillsimplyusetherelativeratiobetweensamplestorejectout-of-timeevents,havinginmindthattherearemoreadvancedalgorithmforthispurpose(likefittingtogetmoreprecisetimeinfoforinstance)• Requirethemaximummustbeeitherthe2nd,3rd or4th sampleandthefirstsamplemusthavelessADC

thanthemaximum

Occupancy- 1sample

10

• Rawoccupancymeansthe#of stripsabovethresholdcut/total#ofstrips• Noiserejectedoccupancymeansthe#ofstripsabovethreshold cutandout-of-timenoiserejectioncut/total#ofstrips• For1sample,rawoccupancywouldbethesameasnoiserejectedoccupancy

Rawoccupancy Noise-rejectedoccupancy

SIDIS plane1 4.00% -




SIDISplane5 3.36% -

SIDISplane6 2.50% -

Occupancy- 6sample

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• Rawoccupancymeansthe#of stripsabovethresholdcut/total#ofstrips• Noiserejectedoccupancymeansthe#ofstripsabovethreshold cutandout-of-timenoiserejectioncut/total#ofstrips


SIDIS plane1 10.0% 4.33%




SIDISplane5 8.67% 3.80%


Occupancy- 9sample

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• Rawoccupancymeansthe#of stripsabovethresholdcut/total#ofstrips• Noiserejectedoccupancymeansthe#ofstripsabovethreshold cutandout-of-timenoiserejectioncut/total#ofstrips








Trackingresults– SIDISFA

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Track Multiplicity-0.5 0 0.5 1 1.5 2 2.5 30

10

20

30

40

50

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70

80

90

100

Efficiency

Track Accuracy-0.5 0 0.5 1 1.5 20

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Accuracy

Accuratetrack Non-Accuratetrack

• Accuratetrackrequiresallhitsofthetrackmustbethe”best”reconstructedhitfortheMChit• “best”reconstructedhitrequiresthehitmustbetheclosestreconstructedhitfortheMChit, itmustcontains

contribution fromtheMC,andthereconstructedhitcannotbeover3stripsawayfromtheMChit• NumberweightedbyDIScrosssection

Forsingletrackevent

Trackingresults– SIDISFA

14momentum (GeV)

0 2 4 6 8 10

(deg

)θ

6

8

10

12

14

16

1

10

210

310

vs pθ

EfficiencyinredAccuracyinblack

85.1%76.0%

84.8%75.4%

88.1%76.9%

87.1%78.1%

87.0%79.1%

88.5%81.9%

89.2%82.5%

88.1%81.6%

92.7%86.0%

91.2%83.8%

91.2%85.4%

91.4%87.9%

92.0%88.8%

Track Accuracy-0.5 0 0.5 1 1.5 20

10

20

30

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Accuracy

Trackingresults– SIDISLA

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Accuratetrack Non-AccuratetrackTrack Multiplicity-0.5 0 0.5 1 1.5 2 2.5 30

10

20

30

40

50

60

70

80

90

100

Efficiency

• Accuratetrackrequiresallhitsofthetrackmustbethe”best”reconstructedhitfortheMChit• “best”reconstructedhitrequiresthehitmustbetheclosestreconstructedhitfortheMChit, itmustcontains

contribution fromtheMC,andthereconstructedhitcannotbeover3stripsawayfromtheMChit• NumbersweightedbyDIScrosssection

Forsingletrackevent

Trackingresults– SIDISLA

16momentum (GeV)0 2 4 6 8 10

(deg

)θ

16

18

20

22

24

26

28

30

-110

1

10

vs pθ

EfficiencyinredAccuracyinblack

86.8%87.7%

86.3%86.2%

88.5%90.9%

90.3%91.0%

Extracting SAMPA response function · 2018. 5. 30. · Extracting SAMPA response function Pulse...

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