sapporo 20/06/2007susy 2010: performances with early data c.guyot 1 data quality and strategies for...

Sapporo 20/06/2007 Susy 2010: Performances with early data C.Guyot

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DATA QUALITY AND STRATEGIES FOR EARLY CALIBRATION at LHC

• Strategy for commissioning with early data– Data quality assessment– Calibration and alignment

• Expected performances in early stage– Evaluation of detector performances– Mostly based on MC (+testbeam) studies

Claude Guyot (Saclay)

ATLAS

Most examples taken from ATLAS.But apply also to CMS in most cases

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Expected luminosities for the first year

Proposed scenario for early physics:

First collisions at 14 TeV: July 2008 ? after system and beam commissioning ~4 months of proton-proton physics run in 2008 phase 1: 43 bunches, L ~ 5 x 1030 phase 2: 75 ns, L ~1 x 1031 3 x 1031 End 2008: pilot run 25 ns, L ~ 1 x 1032

Integrated luminosity end of 2008: 100 - 300 pb-1 ? (100 pb-1 = 120 effective days @ 1031 cm-2s-1)

Restart in spring 2009 with L ~ a few 1032

=> Integrated luminosity end of 2009: 1-3 fb-1 ?


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How many events per experiment for calibration at the beginning ?

similar statisticsto CDF, D0 today

l e or Assumed selection efficiency:W l, Z ll : 20%tt l+X : 1.5% (no b-tag, inside mass bin)

+ lots of minimum-bias and jets (107 events in 2 weeks of data taking if 20% of trigger bandwidth allocated)

100 pb-1 3 months at 1031 or few days at 1032 , =50%

1 fb-1 6 month at 1032, =50%

F. Gianotti


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Data Quality Will Drive the Success of LHC Experiments

Though current effort concentrates on building DQA infrastructure, the aim of it is of course to achieve the best possible quality of ATLAS data !

CDF, DØ found that physics output of first 3 years of Run II was limited (a.o.) by:

Calorimeter calibration and noise

Tracking and calorimeter alignment

Luminosity

Perform. & speed of reconstruction

“Monitoring of data quality is the key”

Must have tools ready

Dedicated (recognized!) manpower

Another problem identified :

“Too high standards, perfectionism”

DØ Run II, V. Shary, CALOR04

SUSY ?

Aiming at pragmatic approach to Data quality monitoring and assessment for initial data taking.

Sophisticated automatic DQ assessment should follow !


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Data Quality assessment tools• Main tools for spotting detector and reconstruction problems:

– Online DQA:• DCS: HV, LV, gas, temperatures, optical alignments…• ROD level histos: hit rates, noisy/dead channels, pedestals, T0, drift

time behaviours….• Monitoring farm:

– Full reconstruction of sampled events (rather low stat, a few Hz)=> look at combined recons.

• Trigger checks:– rates, reconstruction quality in high level triggers (LVL2, EF), threshold

curves, first estimate of efficiencies…– Offline DQA at Tier0:

• Express stream (start 1-2h after data taking):– selection of “interesting” events by triggers:

» Events with very high pT leptons (e.g. > 200GeV) or missing ET, multijets, dileptons (J/,,Z), prescaled minimum biased and low pT triggers

– First full reconstruction (calibration constants from previous iteration)– Fast feedback to shift crew through automates tools

• Bulk reconstruction (24-48 hours after data taking):– Full statistics– Use updated calibrations


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Data flow and Data Quality Assessment in ATLAS

RA

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express Calibrationstream

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Prompt bulk reconstruction

T1

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T1 (Late Reprocessing)

updated calib

Offline DQA 1

Offline DQA 1

updated status

updated status

status updates

CERN Tier0 storage DB from onlineConfig, Calib

DCS, DQ status

+ Calib/align+ DQ status+ Archives

Xpressrecons Calibration

/Alignmentprocessing

Time

Offline DQA 1

Offline DQA 1

status updates

+ Calib/align+ DQ status+ Archives

2h

8h

24h

3 m

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Online DQA

Online DQA

RODs

Front-end

Trigger Level 1

Trigger Level 2

Event Builder

EF EF EF EF

SFOs

On

line

Da

te B

ase

+ Shift Log+ DQ status+ Archives

Detector Control System

Monitoring farm

Online

~500nodes

>1000 nodes


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ATLAS CMS

INNER TRACKER

• Silicon pixels + strips• TRT with particle identification• B = 2T

• /pT ~ 2.6% (at 100 GeV, = 0)

• Silicon pixels + strips• No dedicated particle identification• B = 4T

• /pT ~ 1.2% (at 100 GeV, = 0)

MAGNETS

• Solenoid + Air-core muon toroids• Calorimeters outside field • 4 magnets

• Solenoid• Calorimeters inside field• 1 magnet

EM CALORIMETER

• Pb-liquid argon• /E ~ 10% / E 0.5% • Uniform longitudinal segmentation • Saturation at ~ 3 TeV

• PbWO4 crystals • /E ~ 3–5% / E 0.7% • No longitudinal segmentation• Saturation at 1.7 TeV

HAD CALORIMETER• Fe+scint. + Cu-liquid argon (10 ) • /E ~ 50% / E 0.03

• Brass+scint.• /E ~ 100% / E 0.05

MUON SPECTROMETER• Air /pT ~ 10.5% at 1 TeV

(standalone)

• Fe /pT ~ 5% at 1 TeV

(combined with tracker)

ATLAS & CMS: Performance Overview


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Calibration and alignment

• What are the goals in term of momentum/energy scale measurement and resolution?

O(5-10 m) (B tagging)20—200 m in RTracker alignment

1% (top mass)<10%Jet energy scale

< 1% (Etmiss)2—3% HCAL uniformity

0.1% (W mass)0.5—2%Lepton energy scale

< 1% (e.g. H )~ 1% ATLAS~ 4% CMS

EMCAL uniformity

Goals for PhysicsExpected Day 0

Muon spectro alignment 100 m (ATLAS) 30 m (ATLAS) (Z’)


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EM calibration

• Online (electronic) calibration:– Used to monitor and correct for short term (< day)

response variations (pedestal, gains, noisy/dead channels)

– Allow connection with test beam calibrations (ATLAS) => 1% EM calo uniformity and <2% on electron energy scale at Day0

– So far in ATLAS Liquid Argon EM calorimeter, less than 0.1% of dead channels => no significant impact on physics

•In situ calibration using physics data: mapping ID material with photon conversions in EM calo understanding isolated e identification and reconstruction intercalibration with Z→e+e- E/p from W→e±ν and inclusive electrons


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EM calibration: ID material mapping

Affects electrons and : energy loss, conversions => Need to know the material distribution to control the electron/ identification efficiency

ATLAS study (in progress):• Use 0-> from minimum bias and ratios of double conversion to single conversion and no conversion to determine the amount of material in front of the calo.

Provided that we know the reconstruction efficiency of converted photons, one can get the total X0 with 1% error in a few days


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Events for EM calibration

• Expected statistics of interesting events for calibration and for assessing the EM calorimetry performances

Only at very low lumi

Low energy calibrationUniformity studies

•Use W samples for E/P (calo/tracker) studies•Challenge: How to extrapolate from medium Z->ee energies to very high energies (>200GeV) => rely on test beam and MC simulations


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EM calibration from Z-> eeFirst collisions : ~105 Z -> ee events expected in 2008Constant term in the calorimeter resolution: ctot = cL + cLR

• cL ≈ 0.5% demonstrated at the test-beam over units Δη x Δ = 0.2 x 0.4

• cLR ≡ long-range response non-uniformities from unit to unit (400 total)

(module-to-module variations, different upstream material, etc.)

Use Z-mass constraint to correct long-range non-uniformities:From full simulation : ~ 250 e/ unit are needed to achieve cLR 0.4% => ~105 events

=> Get the absolute energy scale at <0.1%

Main difficulty: Disentangle energy scale with material effects


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Electron reconstruction efficiencyusing Z-> ee events

• Based on Tag&probe method:– Well identified electron (based on ID+calos info) on one side (tag electron)– Simple object (e.g. isolated ID track or calo EM cluster) on the other side

(probe electron)– Efficiency derived from the number of events in the Z mass window

Tag&probe agrees with truth matching to ~0.1% in average!

A similar tag&probe method can be used for muon reconstruction efficiency (use ID and Muon spectrometer)


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Jet energy scaleValidation of the energy of a jet is a BIG challenge

Startup: uncertainty ~5-10% , from MC studies, test beam, cosmicsFirst data: embark on data-driven JES derivation

e.g. D0: 5 years of run II data:

showeringresponseF

offsetEE rawcor

Using +jet and dijet events

CMS and ATLAS: 2-3% above 20 GeV after 1-10 fb-1

and 1% eventually? Ambitious!


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Jet energy scale calibration (1)

Jet energy calibration can be divided in 4 steps1. Calorimeter tower/cluster reconstruction2. jet making (cone 0.4/0.7, kT…)

3. jet calibration from calorimeter to particle scale4. jet calibration from particle scale to the parton scale

Difficulties:• Different response to EM and non EM showers•Correct for escaping and invisible energy (K0, neutrons, dead matter in calo..)


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Jet Calibration Approaches• Global Jet Calibration

– use towers or clusters on EM-scale as input to jets– match a truth particle jet with each reco jet– fit a calibration function in , E to all matched jet pairs

• Local Hadron Calibration– calibrate 3D clusters independent of any jet algorithm

making an assumption on their EM or non EM nature– make jets out of calibrated clusters

• Hadronic Scale:– tune simulation to describe reco jet level and map to

corresponding truth particle jet– from single isolated hadrons in test-beam, minimum

bias events and decays (E/p-ratio)• Non Uniformity in :

– from di-jet events• Final In-situ Calibration

– with W-mass in tt WbWb lb jjb– with pT balance in Z/+ jet

Monte Carlo Level:minimize 2 function to find calibration constants(weights) to match Ereco with MC truth:

ERECO = ΣwiEi

Test beam and physics data


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Jet energy scale: Contributions to the jet signal

physics reaction of interest (parton level)

detector response characteristics (e/h ≠ 1)

electronic noise

dead material losses (front, cracks, transitions…)

pile-up noise from (off-time) bunch crossings

detector signal inefficiencies (dead channels, HV…)

longitudinal energy leakage

calo signal definition (clustering, noise suppression ,…)

jet reconstruction algorithm efficiency

lost soft tracks due to magnetic field

added tracks from underlying event

jet reconstruction algorithm efficiency

added tracks from in-time (same trigger) pile-up event


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Jet energy scale (ATLAS): MC level

To correct for the missing 8%, further calibration is needed due to:

-misclassification of EM/hadron clusters

-magnetic field (bending of tracks, charged particles don't reach calorimeter)

-physics (fragmentation, pile-up, underlying event,...)

Compare reconstructed jets with MC truth (parton level) for the following corrections (local hadron calibration algorithm):• EM scale (red) • Weighted (hadronic/EM difference) (blue)• Weigthed + Out Of Cone corrections (green)• Weighted + OOC + Dead Material losses (black)


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Jet Energy Scale: In situ calibration

Several in-situ calibrations being actively worked on:

•e/p: minimum bias, decays (isolated pions)•via energy flow using phi symmetry

• Light (< 200 GeV) JES from W jj from tt events~5000 tt lbWb reconstructed events for 1fb-1 JES already limited by systematic:

Mainly biases due to pT cuts Studies still in progress

Note: Colourless dijets from W are different from QCD dijets:

Effect of underlying event may lead to a different JES when referred to the parton scale

Calorimeter response uniformity

Isolated lepton pT> 20 GeV

ETmiss > 20 GeV

4 jets pT> 40 GeV

2 jets M(jj) ~ M(W)

3 jets with largest ∑ pT

t

t


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Jet at EM scale

Jet at particle scale

Potential biases:• sensitivity to ISR/FSR (more to ISR)• contributions from the underlying event • dependent corrections • jet clustering effects • pileup effect (high lumi)

Jet Energy Scale: In situ calibration (2)

• /(Z->ll) + jets energy balance


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• pT balance in QCD dijet for dependence

• Bootstrapping for high energy scale

Basic idea: – select events with at least 3 jets, one having significantly more pT than all others– Balance this jet with the vectorsum of all others

Advantage: - Huge statistics availableDisadvantage: - Indirect method, JES is determined in respect to a lower pT region which has to be sufficiently known- Intercalibration in and required to utilize full statistics


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Bootstrapping

10 Million eventsJES assumed to be known up to 380 GeV

300k eventsJES assumed to be known up to 350 GeV


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Conclusion on Jet Energy Calibration


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Missing transverse energy: ETmiss

Goal: Look for escaping particles via transverse momentum unbalanceBut: - detector effects (holes, noise…) - finite resolution - wrong assignment of clusters (EM vs hadron) - fake and wrongly reconstructed muons - QCD jets can have real ET

miss

Difficult! Day-1: poor resolution

Punch-through at very high ET

ETmiss resolution

Expected “Nominal” resolution deduced from pT balance analysis of dijet and minimum bias events


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Missing ET performance assessment

In situ ETmiss scale determination from Z lepton-hadron channel

• Use Single lepton Trigger events

• Select Z lepton-hadron candidates (Opposite Sign lepton-hadron)

• Reconstruct the invariant Z mass (need assumptions on neutrino directions)

• Subtract the backgrounds using the Same Sign lepton-hadron events

• Use the reconstructed invariant mass to tune the EtMiss scale with the first data: what can we do with 100pb-1 ?

In 100 pb-1, expect: ~150000 Z

~ 70000 Z lepton-hadron

~ 7000 with pTe or pTmu> 15GeV


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In situ EtMiss scale determination from Z lepton-hadron channel

Backgrounds: Inclusive W e (filter cuts: pte>10GeV, |h|<2.7) Inclusive W (filter cuts: ptm>5GeV, |h|<2.8) tt decaying to at least 1 lepton Z ee (filter: mee> 60 GeV, 1e: |h|<2.7, pt>10 GeV) Jets…bb events NOT PRODUCED yet

starting from 70000 Z lepton-hadron events, only ~215 events are selected

The same event sample (with different cuts) can be used to assess the jet scale from the reconstructed visible mass

~5% on EtMiss scale achievable after ~3 months


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Inner detector alignmentAt start-up: hardware based-alignment, plus cosmics

ATLAS: frequency scanning interferometry in silicon strip detector

CMS: laser alignment

Track-based alignment using minimum bias, Zee,

20-200 m accuracy at startup

842 grid line lengths measured precisely measures structure shapes, not sensors monitor movements over ~hours

Few days of data taking: sufficient statistics (~105 clean tracks).

Challenge: <10 m precision (5 m for the pixel layers) 120000 parameters (CMS), 36000 parameters (ATLAS)


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When using only pointing tracks, the fit nicely converges (residuals <10m), but towards a geometry leading to momentum shifts (presence of so-called weak modes) Need to add non-pointing tracks (e.g. cosmics)Work still in progress

Inner detector alignment with tracks (ATLAS)

3 procedures for SCT/pixel alignment:• Robust and Local 2 Algorithms:

Break up 36k×36k matrix into 6×6 matrices for each moduleCorrelations are incorporated through iterations

•Robust:Use only residuals from hits in adjacent overlapping modules (overlap residuals)

•Global:Invert a giant 36k×36k matrixLimitation: 9 GB of memory, speed

idealAfteralignment

M(Z-> after alignment


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ATLAS muon spectrometer calibration and alignment (1)

• Muon reconstruction efficiency and resolution depends on the knowledge of the following effects :

– Chambers Positions (Alignment)– Chamber Deformations (Including Temperature Effects)– Wire Sag control (MDT)– T0, R-T Relations (MDT)– B Field map determination (good progress,should be OK)– Dead / Noisy / Anomalous Channels (data quality)– n / Cavern Background– Geometric Material Distribution– Reconstruction Algorithm Optimization

Use first data to evaluate the cavern background level and the validity of the MC calculations


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MDT tube calibration with first data (ATLAS)

Single tube resolution will not restrict the spectrometer performance at the start


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Muon spectrometer alignment (ATLAS)

Muon spectrometer alignment primarily based on optical systems (~10000 CCD/CMOS sensors)

End CapBarrel

In the absolute mode (based on positioning and calibration precision of the sensors), a level of ~100-200 m on sagitta measurement should be reached.But, from X-ray tomography, we know that a significant fraction of the sensors are badly positioned (>500m, especially in the barrel)


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Muon spectrometer alignment (2)

Alignment with pointing straight tracks (run with B=0 in the toroids) is required. With ~1000 tracks per chamber tower (600 towers in ATLAS, run a few days at L=1031)), a precision of ~100m on sagitta measurement can be reached.

Full precision obtained with ~10000 muons/towers (2009).

Use the optical system in relative mode (precision<20m) to measure the movements when field is switched on


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High mass di-muon pairs

High mass: sensitive to Z’, graviton resonances, etc.Also: large extra dimensions: deviations from SM spectrum

Impact of misalignments on signal and Drell Yan background reconstruction


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Summary and comments (1) Not an exhaustive review!No discussed:-Tau calibration-B tagging-All trigger level calibrations-Trigger efficiencies assessment (using pass-through triggers)-B field -Relative ID-Muon spectro alignment with tracks (ATLAS)-Very low energy muon identification (calo only or calo + first muon layer)-Gamma, jet pointing precisions..…+- Test beam results- Simulation aspects (detector description and Geant4 tuning to reproduce the detectors response at test beams-Software infrastructures (online + offline)-….many other topics related to Data Quality!

Thanks to the numerous ATLAS collegues who provided me with a lot of this material


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Summary and comments (2)

•Hope that we can start LHC operations with reasonably efficient and calibrated detectors:

E.g. <2% on e/ and energy scale, <5% on jet scale and <10% on ETmiss.Should be sufficient for hunting the possible non-standard phenomena which can show up with the first data (Susy, Black holes!...)

•Data Quality Assessment will be of utmost importanceDQA software infrastructure should be ready in time for fast feedback on the hardware side:

Dead/noisy channels, cabling maps, wrong calibration/ alignment constants,…Fast feedback on trigger from trigger performance assessment is also mandatory

Keep it simple at the start (few basic histos) but flexible (changing conditions)

• Commissioning is very important: Do as much as possible with cosmics and beam halo prior to collisions:

e.g. alignment, dead/noisy channels, cabling maps…DQA tools

sapporo 20/06/2007susy 2010: performances with early data c.guyot 1 data quality and strategies for...

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weeks of data

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