Study of The Diffractive Component of the Inclusive Z->e e- and Z->m m- Cross Section

1
Study of The Diffractive Component of the
Inclusive Z-gtee- and Z-gtmm- Cross Section
Candidato Marone Matteo Relatori Dott.sa
Arcidiacono Roberta Dott. Cartiglia Nicolo
Scuola di dottorato in Scienza ed Alta
Tecnologia, Indirizzo Fisica ed Astro?sica Ciclo
XXIII, Ph.D. ?nal dissertation
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Outline

• Introduction
• LHC CMS
• ECAL
• Measurement of
• ECAL Thermal Stability
• DCU
• Results
• Study of the
• Diffractive Component
• Pile-up Removal
• Results

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My Activity during Ph.D.

• My activity in ECAL
• Installation and Commissioning
• Readout Software Development
• Detector Thermal Stability
• Analysis work
• Diffractive Z Production

2008
2011
2009
2010
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LHC
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CMS Detector

• CMS physics goals
• Perform precision measurements in the
• electroweak sector
• Higgs search
• Supersimmetry and new Physics

• Very good muon identification system
• Excellent electromagnetic calorimeter to resolve
  the energy of the electrons/photons
• Efficient tracker system to reconstruct the
  tracks and measure the momentum of the charged
  particles

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ECAL
ECAL is an homogeneous calorimeter made of PbWO4
crystals

• 36 SuperModules, 1700 Crystal each
• 4 Endcap Dees, 3662 Crystals each
• 8 meters long
• 90 Tons of Crystal
• More than 75000 channels

Barrel crystals
Physics reach of the ECAL, in particular the
H-gtgg discovery potential, depends on its
excellent energy resolution. Requires high
precision calibrations
Endcap
Barrel Supermodule
MB
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Forward Calorimeters _at_ CMS
CASTOR Calorimeter
Hadronic Forward Calorimeter

• W absorber quartz plates sandwich
• _at_14m from IP
• coverage -5.2 lt h lt -6.6
• signal collection through Cherenkov photons
• 16 azimuthal segments in f and 2 (EM) 12 (HAD)
  long. segments.
• available on only one side

• _at_ 11 m from IP
• Coverage 3 lt h lt 5
• Steel absorbers and embedded radiation-hard
  quartz fibers for fast
• collection of Cherenkov light
• Two calorimeters (minus and plus side)

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ECAL Thermal stability Hardware installation,
calibration and commissioning
2008
2009
2010
Commissioning
Read Out Software Development
ECAL Thermal Stability
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Why Measure the Temperatures?

• ECAL response sensitive to variations of
• Crystal transparency (irradiation)
• Intercalibration
• Temperature ?(LY)/?T -2/oK
• 1/M(?M/?T) -2/oK
• High voltage 1/M(?M/?V) 3/V
• affect the constant term

Temperature monitoring system is needed
Temperature stability within 0.05/0.1oC
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Detector Control Units (DCU)
The DCUs are special ASIC chips able to read the
following quantities
Temperature 0.012 C
Currents 340 nA
Voltages mV
Basic Read-out Geometry 5X5 crystals (TT)
Very high granularity 8 DCUs per TT 20000 (1
each VFE and 3 in LVR boards)
Optical Fiber
Useful tool to deeply investigate the status of
the calorimeter
Trigger
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ECAL Thermal Stability

• A detailed study of temperature stability has
  been carried on during each collision period.
• DCU system provides one temperature reading every
  10 (25) crystals. Temperature estimation obtained
  driving a known internal current through an
  external thermistor.
• The analysis has been performed using two
  independent monitoring system DCU and Precision
  Temperature Monitoring (PTM)

Poor granularity 4 sensor per SM Useful to
calibrate the DCU sensors and to double check the
results

• Results have been published in
• CMS Paper (CFT-09-004) Performance and
  Operation of the CMS Electromagnetic Calorimeter
  Published on Jinst
• R.Arcidiacono, M.Marone, Ecal thermal stability
  during Cosmic Rays Run 2008, CERN Detector Note
  number DN2010/003 , 2010.

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Results

• The RMS distribution of every temperature sensors
  estimates the detector thermal stability

EB
EE
Period RMS EE (C) RMS EB (C)
2010 BEAMII 0.009 0.007
2010 BEAMI 0.015 0.008
CRAFT09 0.011 0.006
CRAFT08 0.017 0.009

• Very good spatial uniformity and stability in
  time.

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Results (2)

• Integration of the DCU in the readout (online)
  software
• Calibration of detector temperature thermistors
• Measured the Barrel and Endcaps temperature
  stability to be within the specification
  (0.05/0.1oC). Measured the detector thermal time
  constant (in the turn on transition) to be 2
  hours in the barrel and 6 in the Endcaps
• Help the ECAL community to investigate front end
  problems (APD leakage, dead channels,.. ) using
  the DCU data

ECAL Front-End Monitoring in the CMS experiment
presented at CHEP09 International Conference On
Computing In High Energy Physics And Nuclear
Physics, March 2009
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Data AnalysisMeasurement of the Inclusive
Z-gtee- and Z-gtmm- Cross Section
2008
2011
2009
2010
Diffractive Z study
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Diffractive Physics at LHC

• The study of hard diffraction at LHC is feasible
  and it will offer the possibility to explore and
  test the ideas and models developed at much lower
  energies.
• Diffraction inherently present in p-p collisions
  (30 of ?tot)
• Pomeron (IP) successful description within Regge
  theory of diffractive scattering

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Data Samples

• The data are divided in two periods
• Pythia 6 (tune D6T and Z2) has been
• used to simulate the Drell-Yan (DY) events
  decaying into ee (µµ)
• PomPyt has been used to simulate
• Single Diffractive Z boson production
• Dissociative (or Double Diffractive)

X
LHC Run A LHC Run B
Period (2010) 04-08 09-10
BX Inst.Lumi. 0.1-0.2 1030cm-2s-1 0.2-0.6 1030cm-2s-1
X
X
How do we select the diffractive over the non
diffractive part?
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Rapidity Gaps

• In diffraction the hadronization of the final
  states X and Y happens independently. If s is
  large enough, then there is a gap in rapidity
  in between X-Y

• Since gaps are exponentially suppressed in QCD
• fragmentation, a cut on rapidity gap
  increases the relative
• fraction of diffractive events.

• _at_ LHC, s, MX and My are very large
• The particles can easily cover a large zone
• of the CMS detector total acceptance

We select diffractive events requiring visible
rapidity gap
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Z Candidates Selection

• Pass HLT trigger (Cluster Etgt15 GeV)
• Reconstructed within the fiducial region
• Track trajectory, estrapolated to match the ECAL
  Cluster
• Reject Barrel Spikes
• EWK standard isolation criteria

Z -gt ee

• HLT trigger muon ptgt9 GeV
• h lt2.1
• X2/NDOF lt 10
• Two muon stations fired
• 10 hit in the tracker and 2 in the pixel detector
• Transverse parameter lt 2mm
• EWK standard Isolation Criteria

Z -gt mm
Known problem in the ECAL calibration. No further
conditions on the Z mass are requested
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Definition of the Variables

• We use the following variables
• SumHF the energy deposit in the HF
• hMax max ? of energy deposits in the detector
• z fractional momentum loss of the scattered
  proton in the diffractive event
• MinHF the minimum deposit in one HF side (/-)

Particle Position Threshold (GeV)
Charged Particle CMS Pt gt 0.5
Neutral Barrel Et gt 1.5
Neutral Endcap Et gt 2.0
Neutral HF Et gt 4.0
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Diffractive Selection with MC
The conventional way to recognize a diffractive
event is to look for rapidity gap in its particle
flow. Since gaps are exponentially suppressed in
QCD fragmentation5, the cut on rapidity gap
increases the relative fraction of diffractive
events.

• We have studied which was the best size of the
  rapidity gap to
• reject the background and select signal

We select events requiring HF0 (2 units of gap)
In the data, LRG suppressed by the presence of
the Pile-up
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Pile-up

• The number of PU events follows a
• Poisson distribution

• A possible way to remove PU can be
• to require only one vertex in the
• event. The number of events having
• one vertex decreases when
• luminosity increases.

• PU interaction can be classified into
• hard PU. Visible interactions (2.4lt h). Can be
  removed requiring 1 vertex
• soft PU. Interaction not detected and therefore
  not removed by the one vertex selection

To correct for this loss of selection efficiency
a method is presented
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Event reweight
The conventional way to recognize a diffractive
event is to look for rapidity gap in its particle
flow. Since gaps are exponentially suppressed in
QCD fragmentation5, the cut on rapidity gap
increases the relative fraction of diffractive
events.
Events collected at higher luminosity have less
probability of being selected. Fit the fraction
of events with no energy in HF as a function of
the BX inst. luminosity. assign to each event
a weight
One vertex only
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z distribution in diffractive events
The conventional way to recognize a diffractive
event is to look for rapidity gap in its particle
flow. Since gaps are exponentially suppressed in
QCD fragmentation5, the cut on rapidity gap
increases the relative fraction of diffractive
events.
Using PomPyt, we simulate the z distribution with
and without the HF0 cut The
simulations show that the diffractive signal is
contained within the kinematic region 0-0.03 z.
Limiting the analysis to this kinematic region
will also produce a good signal enhancement.
HF0 HF0 zlt0.03
PomPyt 0.19 0.18
Pythia D6T 0.67 10-3 0.40 10-3
Pythia Z2 2.33 10-3 1.53 10-3
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Final Selection

• Diffractive events have been selected requiring
• energy below a minimum
• threshold in HF- or HF
• calorimeters
• only one vertex with a
• quality cut to avoid
• reconstruction of fake
• vertices
• Value of ? within 0 lt ? lt 0.03

To measure the signal, the kinematic region has
to be split in a certain number of bins
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Migration

• The reconstructed ? is almost always
  underestimated if compared with the true value,
  because of
• incomplete detector coverage
• particle thresholds.
• Consequently a migration from
• high ?gen values to small ?rec value
• is expected.

To evaluate the impact of the migration effect,
we have studied the resolution, purity and the
migration maps. We chose then number of bins
requiring the following limits
Resolution lt 30-40
Purity gt 50
Efficiency 50-150
Influence the number of z bins
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Resolution
Relative Resolution
Absolute Resolution
? measured is, on average 30 lower than the
generated value, and its resolution is
28. kinematic region divided in two equal
bins (0 ? 0.015 and 0.015 ? 0.03). Migration
maps, purity and efficiency have been checked to
be good
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Unfolding of data distributions
We have used the Pythia 6 D6T and Z2 Monte Carlo
samples, generated without pile-up events
necessary to remove the pile-up contribution from
the data events before being able to compare

• Example MinHF Unfolding
• Divide the distribution in energy bins
• For each bin, calculate the fraction of events as
  a function of BX Instantaneous Lumi
• Extrapolate to zero Lumi to obtain the pile-up
  free number of events

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Which MC fits better?

• Discrepancy between data and Monte Carlo in the
  description of the energy flow in the forward
  region.
• Impossible to choose one single Monte Carlo
  model for the description of the non diffractive
  part
• Forced to use two Pythia tunes,
  D6T and Z2

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Selected Events

• Data and MC events which pass the above
  selection
• Different behavior of the two Pythia tunes.
• The number of selected data events is small,
  especially if compared to
• the Z2 tune prediction.
• Diffractive PomPyt events which pass the
  diffractive selection cuts is
• very large compared to data

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Signal Significance
ni
Significance defined as
Range D6T Z2
0-0.015 1.38 -0.89
0.015-0.03 2.26 0.38
TOTAL 2.62 -0.07
Assuming D6T to be the correct background
description, then we would have a significance of
about 2.6 s. Considering the Z2 tune, this value
drops down to 0 s. To assess at 3 s the
presence of a signal, we would need 11 pb-1.
The 5 s signal is instead assessed with 29
pb-1.
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Cross Section Measurement
Cross Section evaluated as Where, A is the
acceptance L the (effective) integrated Lumi eZ
the efficiency of the Z boson selection eD
efficiency of the diffractive selection
MonteCarlo Z-gtee (pb) Z-gtmm (pb) Combined (pb)
D6T 3312 98 4215
Z2 1412 -98 515
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Prospects for 2011
The request of no energy in both CASTOR (-6.6 ?
-5.2) and HF calorimeters corresponds to a gap
of 3.5 units, which makes this selection
virtually background-free.
MonteCarlo PomPyt Pythia D6T Pythia Z2
HF0 9.6 0.002 0.005
HF0 CASTOR 8.0 0.0002 0.0006
CASTOR calorimeter has suffered of intermittent
calibration problem during 2010. This study
shows the possibility to use this cut to obtain a
cross section measurement during 2011
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Conclusions

• In this thesis we have proposed and employed a
  novel method to select diffractive events.
• We have derived a weight function that weights
  diffractive events on the probability of having a
  rapidity gap at a given luminosity
• The extraction of the diffractive signal from the
  events that pass our selection criteria is
  further complicated by the current discrepancy
  between data and Monte Carlo in the description
  of the energy flow in the forward region.
• This mismatch, which is actually quite important,
  did not allow us to choose one single Monte Carlo
  model for the description of the non diffractive
  part but has forced us to use two Pythia tunes,
  D6T and Z2, which bracket the range of
  uncertainties.

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Conclusions (2)

• Within these constrains, and due to the quite low
  luminosity, we were not able to establish the
  presence of diffractive Z production, but only to
  see a production excess over one of the two
  Pythia tunes prediction.
• We are confident that the tools developed for
  this analysis can be applied to the much larger
  sample of the 2011 data, and we are looking
  forward to do the analysis in the next few months.

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Spares
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Read-out detector software
The digitized data from the FE are read by the
the off-detector electronics, consisting of 54
Readout Units each comprising three type of VME
boards Clock and Control System (CCS) Trigger
Concentrator Card (TCC) Data Concentrator Card
(DCC). Data reduction is achieved using a
Selective Readout algorithm based on the
classification of the detector in high al low
interest regions (SRP)
The ECAL Online software is responsible for the
operation of the ECAL detector during data
taking. The system is built on top of the CMS
data acquisition frameworks (XDAQ) and interfaced
with the run control (RCMS).
In parallel, other relevant front end parameters
are read out by the DCU system, heavily used
during the commissioning phase
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Off-Detector Electronics
CCS (clock and control system) LHC clock and
control signals front-end initialization
DCC (data concentration card) Data reduction
Transmission to central DAQ (at Level 1 rate)
TCC (trigger concentration card) Encoding of
TT Regional Calorimeter TT TT importance
transmission to SRP (at Level 1 rate)
SRP (Selective Readout Protocol) send to the
DCC the list of trigger towers to be read out
Overall the off-detector electronics is made by
18 VME-9U and 1 VME-6U crates controlled by 28
crate mounted PCs
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What is monitored

• APD
• currents (1 DCU for xtal 1700/SM)?
• temperatures (1 DCU every 10 xtals 170
  values/SM)
• VFE LVR
• DCU internal temperatures (8x68 values /SM)?
• MEM box
• VDD_1, VDD_2, 2.5 V, Vinj (4X2 values / SM)?
• DCU internal temperatures (1x2 values /SM)?
• LVR
• 3 thermistors
• 2.5 V (12x68 values / SM)?
• 4.3 V (2X68 values / SM)?
• 0.1 V inhibit (1X68 values /SM)

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DCU Software Architecture
XDAQ
DCUConverter
DATA
DCU Reader
CondDB
Calibrations
DCS Detector Control System
Soap
PC Storage Data
Files
Converter
Write
CondDB
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Detector Calibration

• Calibrations aim at the best estimate of the
  energy of e and ?s
• Energy deposited over multiple crystals
• Ee/? Fe/? G ?i ci Ai
• Amplitude in ADC counts Ai
• Intercalibration uniform single channel response
  to a reference ci
• Global scale calibration G
• Particle-specific corrections (containment,
  clustering for e/?s) Fe/?

Intercalibration together with global scale feeds
directly into the constant term
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DCU graphical interface
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Z-gt ee In situ Intercalibration
The electromagnetic shower spreads over several
crystals. linear system associated to a huge
matrix have to be inverted in order to get the
single inter-calibration factor
Single region intercalibration coefficient can
be obtained with an iterative method Can be used
to tune Barrel/Endcap
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Intercalibration

• Problem1 the same photon (or electron) gives a
  different answer (in ADC counts) depending upon
  the crystals it hits.
• each crystal has a specific light yield
• each photodetector has its specific gain
• Solution find 75848 coefficients which make
  every crystal answer in the same way

• Intercalibration has been achieved in several
  ways, with different precision
• EXAMPLEBARREL
• - Using data collected in the laboratories
  4.5-6
• Cosmic ray (all) expose each SM to cosmic rays
  1-2
• TestBeam (9 SM) electrons at a given E in each
  crystal 0.3

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Z-gtee events selection
(Leptonic)

• At the nominal LHC c.m. energy, the leptonic Z
  cross section is 2nb
• Decreasing to 0.9nb at 7 TeV
• Main background is due to QCD Dijets and ? Jet
• High transverse momentum leptons are the strong
  signature for Z decay

Channel Cross section (nb)
QCD Dijets 5x105
? Jet 2x102
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Global scale

• Problem2 the ECAL response depends on the energy
  of the incoming particles itself.
• The linearity of the calorimeter must be
  studied at the level of the per mille.
• Solution find absolute references to tune the
  energy scale
• Z and W decays, J/Psi, pi Zero and others.

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Energy reconstruction in ECAL
The measurement of the electron E is hampered by
the amount of tracker material and by the strong
magnetic field. Electrons radiate brem. photons
in the azimuthal direction F
Brem
35 of the photons radiate more than 70 of
their energy
The ECAL superclustering is designed to take
into account the spread and the brem
Clustering
e 99 for pgt7GeV
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Temperature Measurements
This chip drives an internal (known) current
across a thermistor glued on the back of the
crystals The thermistor temperature response has
been studied prior in laboratory The in situ
read-out circuit differs from the one used in
calibration Another calibration has been
performed using an independent monitoring system
Precision Temperature Monitoring PTM
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Z-gtee variables
pb
1276 1271 353
H/E lt 0.1
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ECAL Dead Channels
ECAL shows a certain number of problems ( 1 of
dead channels, DAQ related errors). Any missing
channel directly affects the energy
reconstruction. Therefore systematic studies are
necessary to tune the official reconstruction
algorithm with the real data.
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Cross Section Measurement

• We measure the inelastic pp cross section using
  pile-up (PU) events
• The probability of having npileup
• depends on the total s(pp)
• cross section.

• The pile-up depends on the Luminosity per bunch
  crossing (Lbx) max. during 2010 0.6 1030
  cm-2 s-1
• ? Cross checked using the number of triggers in
  each bunch (L s Nevents)

• Pile up events are recorded by a high efficient
  stable trigger (e.g. Double ee, pt gt 10GeV)

• The goal of the analysis is to count the number
  of vertices
• as a function of luminosity

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Result - fits

• Using the correction functions, we unfold
  the measured vertex distributions to obtain the
  correct distributions which we fit with a
  Poissonian function

PU Vertexes 1
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Results - Cross section

• For each of the PU distribution we obtain a
  value of the cross section and then these 9
  values are averaged

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Proton Dissociation
Diffractive events in which the proton, after the
Pomeron exchange, splits into a leading baryon
and into a system of particles (Y)
It is interesting to calculate the Ratio
Dissociative/Diffractive
Proton 10 h HFlt 5 h Dh of 5 -gt ln(M2Y) 5
MY 5 GeV hits HF
1/2.5
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Migration Studies Other Results
Requiring 2 bins, migration map, efficiency and
purity are within the limits
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