1

Bunch length measurement with the luminous Bunch length measurement with the luminous region Z distribution : region Z distribution :

evolution since 03/04evolution since 03/04

B. VIAUD, C. O’GradyB. VIAUD, C. O’Grady

• Origin of the discrepancies between on- and off-line :Origin of the discrepancies between on- and off-line : • ReminderReminder• Comparison using 2 samples from the same runsComparison using 2 samples from the same runs• Comparison using 2 samples containing the same eventsComparison using 2 samples containing the same events• Differences in the selection between on- and offlineDifferences in the selection between on- and offline

2

Reminder : different results between Reminder : different results between on- and off-line on- and off-line

B. VIAUD, C. O’GradyB. VIAUD, C. O’Grady

Measurement with 2 samples at RF voltage = 3.2 and 3.8 MVMeasurement with 2 samples at RF voltage = 3.2 and 3.8 MV

Data type Data type LERLER HERHER zz22 22 #events (3.2) #events (3.8) #events (3.2) #events (3.8)

online 12.42online 12.420.20 12.050.20 12.050.20 299 8.6 0.56M0.20 299 8.6 0.56M 1.50M 1.50M offline 14.12offline 14.120.15 10.600.15 10.60 311 11.3 0.53M 3.30M 311 11.3 0.53M 3.30M

Important variation between on- and offline. Why ?Important variation between on- and offline. Why ?

3

Comparison of 2 samples made of the same runsComparison of 2 samples made of the same runs

B. VIAUD, C. O’GradyB. VIAUD, C. O’Grady

Z [mm] Z [mm]

<Z> = 1.10 0.1 mm RMS = 7.7 0.1 mm <Z> = 1.27 0.2 mm RMS = 7.8 0.2 mm

online offline

Data type Data type zz22 22 zz

2 2 ββ**(y) (y) 22

online 314 online 314 ~5 ~5 offline 322 offline 322 1 ~5 1 ~5

Hardly consistent

Cuts on the vertex χ2 and on tan(λ1) are applied + same frame

4

Comparing 2 samples containing exactly the same Comparing 2 samples containing exactly the same eventsevents

B. VIAUD, C. O’GradyB. VIAUD, C. O’Grady

The timestamp is available both in online and offline samples: The timestamp is available both in online and offline samples:

• used it to select a sample containing exactly the same events online used it to select a sample containing exactly the same events online and and offlineoffline

• compared the vertex coordinates: compared the vertex coordinates: z differs by ~50z differs by ~5010 10 μμm, m, ΔΔz/z <6%z/z <6% => only a part of the discrepancy=> only a part of the discrepancy

5

Differences in the selection… Differences in the selection…

B. VIAUD, C. O’GradyB. VIAUD, C. O’Grady

offline online

Z

[

mm

]

sqrt(x2+y2) [mm] sqrt(x2+y2) [mm]

Z

[

mm

]

Cuts on the vertex χ2 and on tan(λ1) applied

6

Differences in the selection… Differences in the selection…

B. VIAUD, C. O’GradyB. VIAUD, C. O’Grady

online

Z

[

mm

]

cos(trk1-trk2) in rest frame

Z

[

mm

]

Cuts on the vertex χ2 and on tan(λ1) applied

offline

cos(trk1-trk2) in rest frame

7

Differences in the selection: add cuts on Differences in the selection: add cuts on tan(tan(λλ22), ), cos(trk1-trk2), sqrt(x2+y2)

B. VIAUD, C. O’GradyB. VIAUD, C. O’Grady

Z [mm]

online offline

Data type Data type zz22 22 zz

2 2 ββ**(y) (y) 22

online 314online 314 ~5 ~5 offline 314offline 314 ~4 273 ~4 273

Z [mm]

<Z> = 1.10 0.1 mm RMS = 7.71 0.1 mm <Z> = 1.22 0.2 mm RMS = 7.73 0.2 mm

8

Discrepancies between on- and off-lineDiscrepancies between on- and off-line

B. VIAUD, C. O’GradyB. VIAUD, C. O’Grady

• Not yet completely understoodNot yet completely understood • A small part is due to differences in the reconstructionA small part is due to differences in the reconstruction• Samples built with the same runs lead to consistent results Samples built with the same runs lead to consistent results after a few extra cuts, but: after a few extra cuts, but:

• need more statistics to conclude need more statistics to conclude -> get some other samples built with the same runs-> get some other samples built with the same runs • need to reproduce, as much as possible, the same cuts in both samplesneed to reproduce, as much as possible, the same cuts in both samples

9

On the wayOn the way

B. VIAUD, C. O’GradyB. VIAUD, C. O’Grady

• Codes to subtract the slow and bunch number Codes to subtract the slow and bunch number dependant z variationsdependant z variations

• Codes to fit using unbinned likelihoodsCodes to fit using unbinned likelihoods

• Fit in slices of zFit in slices of z

10

Measurement with 2 samples taken at Measurement with 2 samples taken at RF voltage = 3.2 and 3.8 MVRF voltage = 3.2 and 3.8 MV

B. VIAUD, C. O’GradyB. VIAUD, C. O’Grady

Data type Data type LERLER HERHER zz22 22 #events (3.2) #events (3.8) #events (3.2) #events (3.8)

online 12.42online 12.420.20 12.050.20 12.050.20 299 8.6 0.56M0.20 299 8.6 0.56M 1.50M 1.50M offline 14.12offline 14.120.15 10.600.15 10.60 311 11.3 0.53M 3.30M 311 11.3 0.53M 3.30M if we subtract the bunch number dependent Z variationif we subtract the bunch number dependent Z variation offline 13.43offline 13.430.15 11.220.15 11.22 306 11.8 306 11.8=> Important variation between on- and offline. Why ?=> Important variation between on- and offline. Why ?

•Large correlation between Large correlation between LERLER and and HER HER ( > 99%) ( > 99%) • too large to find precisely the individual values ? too large to find precisely the individual values ? MC-TOYs have the same correlation and work correctly.MC-TOYs have the same correlation and work correctly.• effect of fitting a PDF which doesn’t describe the data properly ?effect of fitting a PDF which doesn’t describe the data properly ?

• need more MC-TOY tests to check that.need more MC-TOY tests to check that.• several discrepancies observed between on- and offline :several discrepancies observed between on- and offline :

• RMS of both RF distributions 0.1 mm larger in offline dataRMS of both RF distributions 0.1 mm larger in offline data• An offset of ~1mm in Z An offset of ~1mm in Z

=>=> Origin ? Origin ? Different frames ? Something in the slow Z movement subtraction ? Different frames ? Something in the slow Z movement subtraction ? Cuts ? => We’ll try the offline analysis with exactly the cut than online.Cuts ? => We’ll try the offline analysis with exactly the cut than online.

11

Measurement with long coast dataMeasurement with long coast data

B. VIAUD, C. O’GradyB. VIAUD, C. O’Grady

Data type Data type LERLER HERHER zz22 22 #events #events

online 5.6online 5.61.4 14.71.4 14.70.6 247 1.5 140k0.6 247 1.5 140k offline 6.4offline 6.4 14.6 14.6 254 1.2 35k 254 1.2 35k

Not enough stat. Not enough stat. + correlations ?+ correlations ?

12

Z variation as a function of the bunch numberZ variation as a function of the bunch number

B. VIAUD, C. O’GradyB. VIAUD, C. O’Grady

<

Z> [m

m]

Bunch number

Slow Z movement subtractedSlow Z movement not subtracted

Mini-trains ?

13

Z variation as a function of the bunch numberZ variation as a function of the bunch numberhigh vs. low I high vs. low I

B. VIAUD, C. O’GradyB. VIAUD, C. O’Grady

Bunch number

<

Z> [m

m]

High I Low I

14

Z-RMS variation as a function of the bunch Z-RMS variation as a function of the bunch numbernumber

B. VIAUD, C. O’GradyB. VIAUD, C. O’Grady

0 Bunch number 3492

Z-

RM

S [m

m]

15

Systematic uncertaintiesSystematic uncertainties

B. VIAUD, C. O’GradyB. VIAUD, C. O’Grady

• Varying the parameters fixed in the fit within their Varying the parameters fixed in the fit within their known errorsknown errors and re-compute the results. and re-compute the results.

• How to evaluate the uncertainty due to the fact the PDF How to evaluate the uncertainty due to the fact the PDF used in the fit doesn’t describe properly the data ?used in the fit doesn’t describe properly the data ?

• try several PDFs (asymmetric bunches) ? try several PDFs (asymmetric bunches) ? • let Betalet Beta**_y float ?_y float ?• use TOYs to produce distorded distributions compared to use TOYs to produce distorded distributions compared to the nominal PDF ?the nominal PDF ? • ? ?

16

New Since Last Collab Mtg New Since Last Collab Mtg

17

New Offline-Style AnalysisNecessary for analyzing MC/data with same code.

New (simple) cuts:• ntracks==2ntracks==2• Chi2(vertex)<3Chi2(vertex)<3• Mass(2track)>9.5GeVMass(2track)>9.5GeV• E(charged showers)<3GeVE(charged showers)<3GeV• 0.7<tan(lambda1)<2.50.7<tan(lambda1)<2.5

Note that Note that all our units are mmall our units are mm (like PEP). (like PEP).Also, subtract Z motion of beamspot more Also, subtract Z motion of beamspot more

trivially now (new value every 10 minutes).trivially now (new value every 10 minutes).

18

New Offline Analysis Code New Offline Analysis Code

Z [mm] Z [mm]

Check we see the same effect in data (from late July 2005)Check we see the same effect in data (from late July 2005)

=> Similar effect. Similar values of the fitted parameters=> Similar effect. Similar values of the fitted parameters

fixedmmmm

mmy

HERz

LERz

HERLER

3.1105.08.10

25.06.14)(

,

,

*/

fixedmmmm

HERz

LERz

3.1101.02.12

,

,

~10 ~3

19

Monte Carlo with a gaussian Monte Carlo with a gaussian Z distributionZ distribution

• Z-distribution is generated in the mu-pair MC as a gaussian with Z-distribution is generated in the mu-pair MC as a gaussian with

<Z>=0 mm and <Z>=0 mm and = 8.5 mm = 8.5 mm

=> => No obvious effect due to the No obvious effect due to the reconstruction / selectionreconstruction / selection

~1.1

mmFITz 006.0508.8,

Z [mm]

20

Z vertex resolution from MC

• 30um resolution is very small on the scale we are looking, so feels difficult for it to be a resolution effect.

Z Reconstructed – Z True (mm)

21

Z-distribution/bunch length measurement Z-distribution/bunch length measurement as a function of bunch currentas a function of bunch current

• Data/theory discrepancy could be due to Beam-Beam effect Data/theory discrepancy could be due to Beam-Beam effect proportional to the bunch current proportional to the bunch current

=> => Compare Z-distribution at high and low currentCompare Z-distribution at high and low current• Used data taken on July the 31Used data taken on July the 31stst and July the 9 and July the 9thth LER: 2.4 A -> ~ 0.7 ALER: 2.4 A -> ~ 0.7 A

HER: 1.5 A -> ~1050 AHER: 1.5 A -> ~1050 A

Selected each time the first and last runs of the period Selected each time the first and last runs of the period during which the currents drop. during which the currents drop.

22

3131stst of July of July • Standard fit (waists Z-position or Standard fit (waists Z-position or **(y) not allowed to float ) (y) not allowed to float )

No obvious difference at this statistics. When No obvious difference at this statistics. When waists Z-position or waists Z-position or **(y) are (y) are allowed to float : Chi2 ~ 1, fitted values of allowed to float : Chi2 ~ 1, fitted values of Zwaist andZwaist and **(y) similar to those (y) similar to those obtained with the usual sample.obtained with the usual sample.

Z [mm]

~1.4 ~1.3

High current Low current

RMS=7.0 mm

Z [mm]

RMS=7.14 mm

23

99thth of July of July • Standard fit (waists Z-position or Standard fit (waists Z-position or **(y) not allowed to float ) (y) not allowed to float )

No obvious difference at this statistics. When No obvious difference at this statistics. When waists Z-position or waists Z-position or **(y) are (y) are allowed to float : Chi2 reduced, fitted values of allowed to float : Chi2 reduced, fitted values of Zwaist andZwaist and **(y) ~ consistent (y) ~ consistent with those we usually see.with those we usually see.

~1.4 ~1.3

High current Low current

RMS=7.14 mm RMS=7.0 mm

High current Low current

~1.6 ~0.8

Z [mm]

Z [mm]

RMS=7.2 mm RMS=7.01 mm

24

Conclusions

• No obvious z-distribution distortion observed when analysis run on monte-carlo

• With available statistics, no obvious beam-beam effects in high/low beam-current runs.

25

How do we proceed?• Analyze monte-carlo with correct hourglass shape (tried

once, but hourglass in monte-carlo was not correct we believe). Unlikely cause, IMHO.

• Backgrounds (tau, 2-photon)? Unlikely cause, IMHO.• Effect of parasitic crossings (now have bunch number in

ntuples … so should be easy). Unlikely cause, IWHO.• Think about asymmetric bunches more• Perhaps help Ilya/Witold study at simulation?• Some machine studies?

26

Reminder IReminder I • Fit the following PDFFit the following PDF on the luminous region Z distribution:on the luminous region Z distribution:

N 2,1 Number of particles per bunch, Number of particles per bunch, ZZcc : Z where the bunchs meet : Z where the bunchs meet

2*,

,02

2*,2*

,

,02

2*,, 11

LERyx

LERyLERx

HERyx

HERyHERxyx

ZZZZ

2

2

3

2

21 exp2

2z

c

yx z

ZZNNL

Allowed to float

2,

2, LERzHERzz

27

Reminder II Reminder II •The theoretical distribution cannot describe the The theoretical distribution cannot describe the shapeshape of the data. of the data.

Trying to understand this before proceeding with bunch length Trying to understand this before proceeding with bunch length measurement!!measurement!!

~ 7.25 mm

~13

Z [mm]

28

Reminder III Reminder III • Better data/theory agreement if the waists Z-position or Better data/theory agreement if the waists Z-position or **(y) are allowed to (y) are allowed to

float in the fit float in the fit

Waists Z positions / Waists Z positions / **(y)(y) values seem unlikely ! values seem unlikely ! Are they real ? Which other effect could simulate this lack of focalisation ??Are they real ? Which other effect could simulate this lack of focalisation ??

~2.2

Z [mm] Z [mm]

mmZ LER 32.04.12,0

mmZ HER 23.01.9,0

~2.4

mmy LER 6.08.14)( *

mmy HER 5.08.14)( *