DATA QUALITY AND STRATEGIES FOR EARLY CALIBRATION at LHC

1. missing energy energy energy ATLAS energy 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) Most examples taken from ATLAS. But apply also to CMS in most cases Susy 2010: Performances with early data C.Guyot

2. 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 ? Susy 2010: Performances with early data C.Guyot

3. How many events per experiment for calibration at the beginning ? l  e or  Assumed selection efficiency: W l, Z ll : 20% tt  l+X : 1.5% (no b-tag, inside mass bin) similar statistics to CDF, D0 today + 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 Susy 2010: Performances with early data C.Guyot

4. Aiming at pragmatic approach to Data quality monitoring and assessment for initial data taking. Sophisticated automatic DQ assessment should follow ! DØ Run II, V. Shary, CALOR04 SUSY ? 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” Susy 2010: Performances with early data C.Guyot

5. 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/y,U,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 Susy 2010: Performances with early data C.Guyot

6. DB from online Config, Calib DCS, DQ status CERN Tier0 storage express Calibration stream 2h Xpress recons RAW data streams: 200Hz, 320MB/s Offline DQA 1 Calibration /Alignment processing 8h status updates updated calib + Calib/align + DQ status + Archives Prompt bulk reconstruction 24h updated status Offline DQA 1 T1 transfer ESD 100MB/s AOD 20MB/s status updates updated status 3 months + Calib/align + DQ status + Archives T1 (Late Reprocessing) Time Data flow and Data Quality Assessment in ATLAS Detector Control System Front-end Trigger Level 1 RODs Online DQA Trigger Level 2 ~500 nodes Event Builder Monitoring farm Online Date Base EF EF EF EF + Shift Log + DQ status + Archives >1000 nodes SFOs Online Susy 2010: Performances with early data C.Guyot

7. ATLAS & CMS: Performance Overview Susy 2010: Performances with early data C.Guyot

8. Expected Day 0 Goals for Physics EMCAL uniformity ~ 1% ATLAS ~ 4% CMS < 1% (e.g. H gg) Lepton energy scale 0.5—2% 0.1% (W mass) HCAL uniformity 2—3% < 1% (Etmiss) Jet energy scale <10% 1% (top mass) Tracker alignment O(5-10 mm) (B tagging) 20—200 mm in Rf Muon spectro alignment 100 mm (ATLAS) 30 mm (ATLAS) (Z’mm) Calibration and alignment • What are the goals in term of momentum/energy scale measurement and resolution? Susy 2010: Performances with early data C.Guyot

9. 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 Susy 2010: Performances with early data C.Guyot

10. EM calibration: ID material mapping Affects electrons and g: energy loss, conversions => Need to know the material distribution to control the electron/g identification efficiency • ATLAS study (in progress): • Use p0->gg 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 Susy 2010: Performances with early data C.Guyot

11. 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 calibration Uniformity 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 Susy 2010: Performances with early data C.Guyot

12. EM calibration from Z-> ee First collisions : ~105 Z -> ee events expected in 2008 Constant term in the calorimeter resolution: ctot = cL+cLR • cL ≈ 0.5% demonstrated at the test-beam over units Δη x ΔF = 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 Susy 2010: Performances with early data C.Guyot

13. 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) Susy 2010: Performances with early data C.Guyot

14. Jet energy scale Validation of the energy of a jet is a BIG challenge Startup: uncertainty ~5-10% , from MC studies, test beam, cosmics First data: embark on data-driven JES derivation e.g. D0: 5 years of run II data: Using +jet and dijet events CMS and ATLAS:  2-3% above 20 GeV after 1-10 fb-1 and 1% eventually? Ambitious! Susy 2010: Performances with early data C.Guyot

15. Jet energy scale calibration (1) Jet energy calibration can be divided in 4 steps • Calorimeter tower/cluster reconstruction • jet making (cone 0.4/0.7, kT…) • jet calibration from calorimeter to particle scale • 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..) Susy 2010: Performances with early data C.Guyot

16. 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 h, 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 t decays (E/p-ratio) • Non Uniformity in h : • from di-jet events • Final In-situ Calibration • with W-mass in tt  WbWb  lnb jjb • with pT balance in Z/g+ jet Monte Carlo Level: minimize c2 function to find calibration constants (weights) to match Ereco with MC truth: ERECO = ΣwiEi Test beam and physics data Susy 2010: Performances with early data C.Guyot

17. longitudinal energy leakage detector signal inefficiencies (dead channels, HV…) pile-up noise from (off-time) bunch crossings electronic noise calo signal definition (clustering, noise suppression ,…) dead material losses (front, cracks, transitions…) detector response characteristics (e/h ≠ 1) jet reconstruction algorithm efficiency jet reconstruction algorithm efficiency added tracks from in-time (same trigger) pile-up event added tracks from underlying event lost soft tracks due to magnetic field physics reaction of interest (parton level) Jet energy scale: Contributions to the jet signal Susy 2010: Performances with early data C.Guyot

18. Jet energy scale (ATLAS): MC level • 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) 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,...) Susy 2010: Performances with early data C.Guyot

19. 3 jets with largest ∑ pT 2 jets M(jj) ~ M(W) t Isolated lepton pT> 20 GeV t 4 jets pT> 40 GeV ETmiss > 20 GeV Jet Energy Scale: In situ calibration • Several in-situ calibrations being actively worked on: • e/p: minimum bias, t decays (isolated pions) • via energy flow using phi symmetry • Light (< 200 GeV) JES from W  jj from tt events • ~5000 tt  lnbWb 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 Susy 2010: Performances with early data C.Guyot

20. Jet Energy Scale: In situ calibration (2) • g/(Z->ll) + jets energy balance Jet at EM scale Jet at particle scale • Potential biases: • sensitivity to ISR/FSR (more to ISR) • contributions from the underlying event • h dependent corrections • jet clustering effects • pileup effect (high lumi) Susy 2010: Performances with early data C.Guyot

21. Jet Energy Scale: In situ calibration (3) • pT balance in QCD dijet for h/F 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 vector sum of all others Advantage: - Huge statistics available Disadvantage: - Indirect method, JES is determined in respect to a lower pT region which has to be sufficiently known - Intercalibration in h and F required to utilize full statistics Susy 2010: Performances with early data C.Guyot

22. Jet Energy Scale: In situ calibration (4) Bootstrapping 300k events JES assumed to be known up to 350 GeV 10 Million events JES assumed to be known up to 380 GeV Susy 2010: Performances with early data C.Guyot

23. Conclusion on Jet Energy Calibration Susy 2010: Performances with early data C.Guyot

24. Missing transverse energy: ETmiss Goal: Look for escaping particles via transverse momentum unbalance But: - detector effects (holes, noise…) - finite resolution - wrong assignment of clusters (EM vs hadron) - fake and wrongly reconstructed muons - QCD jets can have real ETmiss Expected “Nominal” resolution deduced from pT balance analysis of dijet and minimum bias events ETmiss resolution Punch-through at very high ET Difficult! Day-1: poor resolution Susy 2010: Performances with early data C.Guyot

25. 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 Susy 2010: Performances with early data C.Guyot

26. In situ EtMiss scale determination fromZ    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 PRODUCEDyet starting from 70000 Z    lepton-hadronevents, only ~215 events are selected ~5% on EtMiss scale achievable after ~3 months The same event sample (with different cuts) can be used to assess the tjet scalefrom the reconstructed visible mass Susy 2010: Performances with early data C.Guyot

27. Inner detector alignment At start-up: hardware based-alignment, plus cosmics  20-200 m accuracy at startup ATLAS: frequency scanning interferometry in silicon strip detector 842 grid line lengths measured precisely  measures structure shapes, not sensors  monitor movements over ~hours CMS: laser alignment Track-based alignment using minimum bias, Zee,  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) Susy 2010: Performances with early data C.Guyot

28. ideal After alignment Inner detector alignment with tracks (ATLAS) • 3 procedures for SCT/pixel alignment: • Robust and Local c2 Algorithms: • Break up 36k×36k matrix into 6×6 matrices for each module • Correlations are incorporated through iterations • Robust: • Use only residuals from hits in adjacentoverlapping modules (overlap residuals) • Global: • Invert a giant 36k×36k matrix • Limitation: 9 GB of memory, speed M(Z->mm)after alignment • When using only pointing tracks, the fit nicely converges (residuals <10mm), 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 Susy 2010: Performances with early data C.Guyot

29. 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 Susy 2010: Performances with early data C.Guyot

30. MDT tube calibration with first data (ATLAS) Single tube resolution will not restrict the spectrometer performance at the start Susy 2010: Performances with early data C.Guyot

31. Muon spectrometer alignment (ATLAS) Muon spectrometer alignment primarily based on optical systems (~10000 CCD/CMOS sensors) End Cap Barrel In the absolute mode (based on positioning and calibration precision of the sensors), a level of ~100-200 mm on sagitta measurement should be reached. But, from X-ray tomography, we know that a significant fraction of the sensors are badly positioned (>500mm, especially in the barrel) Susy 2010: Performances with early data C.Guyot

32. 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 ~100mm on sagitta measurement can be reached. Use the optical system in relative mode (precision<20mm) to measure the movements when field is switched on Full precision obtained with ~10000 muons/towers (2009). Susy 2010: Performances with early data C.Guyot

33. 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 Susy 2010: Performances with early data C.Guyot

34. 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 Susy 2010: Performances with early data C.Guyot

35. Summary and comments (2) • Hope that we can start LHC operations with reasonably efficient and calibrated detectors: • E.g. <2% on e/g and m 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 importance • DQA 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 Susy 2010: Performances with early data C.Guyot