Study of The Diffractive Component of the Inclusive Z->e + e - and Z-> Cross Section Candidato:...

$: Study of The Diffractive Component of the Inclusive Z->e + e - and Z-> Cross Section Candidato: Marone Matteo Relatori: Dott.sa Arcidiacono Roberta.$
Study of The Diffractive Component of the Inclusive

Z->e+e- and Z-> Cross Section

Candidato: Marone MatteoRelatori: Dott.sa Arcidiacono Roberta

Dott. Cartiglia Nicolo’

Scuola di dottorato in Scienza ed Alta Tecnologia,Indirizzo Fisica ed Astrofisica

Ciclo XXIII, Ph.D. final dissertation

Torino- June 20th 2011Matteo Marone –Ph.D. Final Dissertation

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 2009 2010 2011

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LHC

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CMS Detector• 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

CMS physics goals:• Perform precision

measurements in the electroweak sector• Higgs search• Supersimmetry and new

Physics

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Barrel crystals

Pb/SiPreshower

Barrel Supermodule

ECAL

• 36 SuperModules, 1700 Crystal each• 4 Endcap Dees, 3662 Crystals each

• 8 meters long• 90 Tons of Crystal

• More than 75000 channels

Endcap

Barrel crystals

BarrelSupermodule

Trigger

Light

Current

APD

Crystal

Energy

Light

Current

Voltage

Voltage Bit

MGPA ADC

VFE

Bit

Light

FE

DAQ

Optical Fiber

MB

Physics reach of the ECAL, in particular the H-> discovery

potential, depends on its excellent energy resolution.

Requires high precision calibrations

ECAL is an homogeneous calorimeter made of PbWO4 crystals:

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Forward Calorimeters @ CMS

• W absorber & quartz plates sandwich• @14m from IP •coverage -5.2 < < -6.6• signal collection through Cherenkov photons• 16 azimuthal segments in φ and 2 (EM) + 12 (HAD) long. segments. • available on only one side

• @ 11 m from IP• Coverage 3 < | < 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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Hadronic Forward Calorimeter CASTOR Calorimeter


ECAL Thermal stability: Hardware installation,

calibration and commissioning

Commissioning

Read Out Software Development

2008 2009 2010

ECAL Thermal Stability


Why Measure the Temperatures?

M= Photodetector gainLY= Light Yeld

Temperature stability within 0.05/0.1oC

Temperature monitoring system is needed

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Detector Control Units (DCU)

Trigger

Optical Fiber

Light

Current

APDCrystal

Energy

Light

Current

Voltage

Voltage Bit

MGPA ADC

VFE

MB

LVRMB

VFE

FE

The DCUs are special ASIC chips able to read the following quantities:

Very high granularity:8 DCUs per TT ~ 20000 (1 each VFE and 3 in LVR boards)

Useful tool to deeply investigatethe status of the calorimeter

Basic Read-out Geometry: 5X5 crystals (TT)

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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)

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.

Poor granularity: 4 sensor per SMUseful to calibrate the DCU sensors and to double check the results

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Results

• Very good spatial uniformity and stability in time.

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

EB

EE

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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 Analysis:Measurement of the Inclusive

Z->e+e- and Z-> Cross Section

2008 2009 2010 2011

Diffractive Z study


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)

How do we select the diffractive over the non diffractive part?

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X

X

X


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

• @ 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

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

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Z Candidates Selection• Pass HLT trigger (Cluster Et>15

GeV)

• Reconstructed within the fiducial region

• Track trajectory, estrapolated to match the ECAL Cluster

• Reject Barrel Spikes

• EWK standard isolation criteria

• HLT trigger muon pt>9 GeV

<2.1

• X2/NDOF < 10

• Two muon stations fired

• 10 hit in the tracker and 2 in the pixel detector

• Transverse parameter < 2mm

• EWK standard Isolation Criteria

Known problem in the ECAL calibration.No further conditions on the Z mass are requested

Z ->

ee

Z ->

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Definition of the VariablesWe use the following variables:

• SumHF: the energy deposit in the HF Max: max η of energy deposits in the detector

: fractional momentum loss of the scattered proton in the diffractive event

• MinHF: the minimum deposit in one HF side (+/−)

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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.

Diffractive Selection with MC

In the data, LRG suppressed by the presence of the Pile-up

We select events requiring HF=0 (2 units

of gap)

CMS

Ln(M2x)

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

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Pile-up

€

P(npileup ) =(L ⋅σ )n pileup

npileup!⋅ e−(L ⋅σ )• 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 (2.4< ). 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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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.

Event reweightEvents collected at higher luminosity have less probability ofbeing 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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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.

distribution in diffractive eventsUsing PomPyt, we simulate the distribution with and without the HF=0 cut

The simulations show that the diffractive signal is contained within the kinematic region [0-0.03] Limiting the analysis to this kinematic region will also produce a good signal enhancement.

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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 ζ within 0 < ζ < 0.03

Final Selection

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

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MigrationThe reconstructed ζ is almost always underestimated if compared with the true value, because of: • incomplete detector coverage • particle thresholds.

Consequently a migration from high ζgen values to small ζrec valueis 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:

Influence the number of bins

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Resolution

ζ measured is, on average 30% lower than the generated value, and its resolution is 28%.

kinematic region divided in two equal bins (0≤ ζ ≤0.015 and 0.015≤ ζ ≤0.03).Migration maps, purity and efficiency have been checked to be good

AbsoluteResolution

RelativeResolution

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Unfolding of data distributionsWe 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

1)Divide the distribution in energy bins2)For each bin, calculate the fraction of events as a function of BX Instantaneous Lumi3)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 EventsData 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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ni

Signal SignificanceSignificance defined as:

Assuming D6T to be the correct background description, then we would have a significance of about 2.6 σ. Considering the Z2 tune, this value drops down to 0 σ. ∼To assess at 3 σ the presence of a signal, we would need 11 pb∼ −1. The 5 σ signal is instead assessed with 29 pb∼ −1.

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Cross Section MeasurementCross Section evaluated as:

Where,A is the acceptanceL the (effective) integrated LumiZ the efficiency of the Z boson selectionD efficiency of the diffractive selection

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Prospects for 2011The request of no energy in both CASTOR (-6.6≤ η ≤-5.2) and HF calorimeters corresponds to a gap of 3.5 units, which makes ∼this selection virtually background-free.

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


Read-out detector software

The digitized data from the FE are read by the the off-detector electronics, consisting of54 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


Off-Detector Electronics

CCS (clock and control system) : LHC clock and control signals + front-end initialization

TCC (trigger concentration card): Encoding of TTRegional Calorimeter TT TT importance transmission to SRP (at Level 1 rate)

DCC (data concentration card):Data reduction Transmission to central DAQ (at Level 1 rate)

Overall the off-detector electronics is made by 18 VME-9U and 1 VME-6U crates controlled by 28 crate mounted PCs

SRP (Selective Readout Protocol): send to the DCC the list of trigger towersto be read out


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)

What is monitored


DCU Software Architecture

DCUConverterDCUConverter

DCU ReaderDCU ReaderCondDBCondDB

PCStorage

Data

PCStorage

Data

FilesConverterConverter

DCS – Detector Control System

DCS – Detector Control System

Soap

Write

Calibrations

XDAQXDAQ

CondDBCondDB

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DATA


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


DCU graphical interface

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Z-> 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 methodCan be used to tune Barrel/Endcap


2000 ADC

2100 ADC

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

Intercalibration has been achieved in several ways, with different precision:EXAMPLE:BARREL- 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 %


Z->ee events selection

• 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 γ + Jet:

• High transverse momentum leptons are the strong signature for Z decay

Channel Cross section (nb)

QCD Dijets ~5x105

γ + Jet ~2x102

(Leptonic)

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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.

Global scale


Energy reconstruction in ECAL

Brem

Clustering

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 Φ

The ECAL “superclustering” is designed to take into account the spread and the brem

~ 35% of the photons radiate more than 70% of their energy

ε ~ 99% for p>7GeV

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Temperature MeasurementsThis 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


Z->ee variables

H/E < 0.1

pb12761271353


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 (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 * = Nevents)

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

€

P(npileup ) =(L ⋅σ )n pileup

npileup!⋅ e−(L ⋅σ )

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


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


Results - Cross section

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


Proton DissociationDiffractive 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

~ 1/2.5


Migration Studies: Other ResultsRequiring 2 bins, migration map, efficiency and purity are within the limits

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