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The CMS Computing Software and Analysis Challenge 2006. N. De Filippis. Department of Physics and INFN Bari. On behalf of the CMS collaboration. Contributors. Tommaso Boccali <Tommaso.Boccali@cern.ch> Andrea Sciaba' <Andrea.Sciaba@cern.ch> Luca Lista <Luca.Lista@cern.ch>

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slide1

The CMS Computing Software

and Analysis Challenge 2006

N. De Filippis

Department of Physics and INFN Bari

On behalf of the CMS collaboration

slide2

Contributors

Tommaso Boccali <Tommaso.Boccali@cern.ch>

Andrea Sciaba' <Andrea.Sciaba@cern.ch>

Luca Lista <Luca.Lista@cern.ch>

Sergio Fantinel <sergio.fantinel@lnl.infn.it>

Daniele Bonacorsi <Daniele.Bonacorsi@bo.infn.it>

Marco Paganoni <Marco.Paganoni@cern.ch>

Giacinto Donvito <giacinto.donvito@ba.infn.it>

Alessandra Fanfani <Alessandra.Fanfani@bo.infn.it>

Giorgio Maggi <giorgio.maggi@ba.infn.it>

Stefano Belforte <stefano.belforte@ts.infn.it>

Giuseppe Bagliesi <Giuseppe.Bagliesi@cern.ch>

Francesco Safai Tehrani <fsafai@gmail.com>

Giuseppe Codispoti <giuseppe.codispoti@bo.infn.it>

Salvatore My <my@ba.infn.it>

Marcello Abbrescia <Marcello.abbrescia@ba.infn.it>

Antonio Pierro <antonio.pierro@ba.infn.it>

Paolo Capiluppi <capiluppi@bo.infn.it>

William Bacchi <william.bacchi@bo.infn.it>

Livio Fanò <livio.fano@cern.cht>

Carlos Kavka <carlos.kavka@ts.infn.it>

Ugo Gasparini <Ugo.Gasparini@pd.infn.it>

Paolo.Meridiani <Paolo.Meridiani@cern.ch>

Frederic Ronga <Frederioc.Ronga@cern.ch>

Federico CALZOLARI<Federico.Calzolari@cern.ch>

Guido Cuscela <Guido.Cuscela@ba.infn.it>

Massimo Biasotto <massimo.biasotto@lnl.infn.it>

Federica Fanzago <fanzago@pd.infn.it>

Maria.Damato<Maria.Damato@ba.infn.it>

Marco Corvo <marco.corvo@cern.ch>

HAJDU Csaba <hajdu@sunserv.kfki.hu>

Simon Metson <s.metson@bristol.ac.uk>,

StuartWakefield<stuart.wakefield@imperial.ac.uk>

Mona Aggarwal <m.aggarwal@imperial.ac.uk>

Olivier van der Aa <o.van-der-aa@imperial.ac.uk> Giuseppe Mazza <g.mazza@qmul.ac.uk>

Alex Martin <a.j.martin@qmul.ac.uk>

Dave Newbold <dave.newbold@cern.ch>

David Colling <d.colling@imperial.ac.uk>

t1-admin@cnaf.infn.it

Paolo.Bartalini <paolo bartalini@cern.ch>

Filippo Ambroglini <filippo.ambroglini@cern.ch>

Giuseppe.Cerati <giuseppe.cerati@cern.ch>

Patrizia Azzi <Patrizia.Azzi@cern.ch>

Ezio Torrassa <Ezio.torassa@pd.infn.it>

Martino.Margoni <Martino.margoni@pd.infn.it>

Laura Edera <Laura Edera@cern.ch>

slide3

What was CSA06?

  • A50 millionevent exercise to test the workflow and dataflow as defined in the CMS computing model
  • A test at 25% of the capacity needed in 2008
  • Main components:
    • Preparation of large MC simulated datasets (some with HLT-tags)
    • Prompt reconstruction at Tier-0:
      • Reconstruction at 40 Hz (over 150 Hz) using CMSSW
      • Application of calibration constants from offline DB
      • Generation of Reco, AOD, and AlCaReco datasets
      • Splitting of an HLT-tagged sample into 10 streams
    • Distribution of all AOD & some FEVT to all participating Tier-1s
    • Calibration jobs on AlCaReco datasets at some Tier-1s and CAF
    • Re-reconstruction performed at Tier-1s
    • Skim jobs at some Tier-1s with data propagated to Tier-2s
    • Physics jobs at Tier-2s and Tier-1s on AOD and Reco

Italian contribution

slide4

Official Timeline

– June 1 June 14: First Version of Detector and Physics reconstruction SW for CSA06

– June 1: Computing systems ready for Service Challenge SC4

– June 15: physics simulation validation complete

– July 1: start MC production

– Aug.15: Calibration, alignment, HLT (and first version L1 simulation),

reconstruction, and analysis tools ready

– Aug.30: 50 Mevt produced, 5M with HLT pre-processing

– Sep. 1: Computing systems ready for CSA

– Sep 15: Start CSA06

– Oct 1: Smooth operation for CSA06

– Oct 30: End smooth operation for CSA06

– Nov 15: Finish CSA06

slide5

Success metrics

  • Most of performance metrics of the CSA06 are:
  • Number of participating Tier-1 - Goal: 7 - Threshold: 5;
  • Number of participating Tier-2 - Goal: 20 - Threshold 15;
  • Weeks of running at sustained rate - Goal: 4 - Threshold: 2;
  • Tier-0 Efficiency - Goal: 80% - Threshold: 30%, measured as unattended uptime fraction over 2 best weeks of the running period;
  • Running grid jobs (Tier-1+Tier-2) per day (2h jobs typ.) - Goal: 50K - Threshold: 30K;
  • Grid job efficiency - Goal: 90% - Threshold: 70%;
  • Data serving capability at each participating site from the disk storage to CPU: Goal 1MB/s/execution slot - Threshold : 400 MB/s (Tier-1) or 100 MB/sec (Tier-2)
  • Data transfer Tier-0 to Tier-1 to tape - Individual goals (threshold at 50% of goal); for CNAF it was: 25 MB/s;
  • Data transfer Tier-1 to Tier-2 - Goal: 20 MB/s into each Tier-2 - Threshold: 5 MB/s;
  • Overall "success" is to have 50% of participant at or above goal and 90% above threshold.
slide6

Computing resources

  • Tier-0 (CERN):
    • 1.4M SI2K (~1400 CPUs at CERN)
    • 240 TB
  • Tier-1 (7 sites):
    • 2500 CPUs in total
    • 70 TB disk + tape as minimum to participate
  • Tier-2 (25 sites):
    • 2400 CPUs in total
    • Average 10 TB disk at participating Tier-2
slide8

MC production

software and tools

  • ProdAgent tool used to automatise theproduction:
  • consists of many agents running in parallel: JobCreator, JobSubmitter,JobTracking, MergeSensor….
  • ouput files are registered in Data bookeeping service (DBS); blocks of files are registered in Data Location System (DLS) which takescare of mapping of file blocks and storage elements where they exist
  • Files are merged for optimum size before transfer to CERN
  • CMS software (CMSSW) installed via grid tools or directly bysite admins in remote sites. A local catalogue used to map LFNs tolocal PFNs via a set of rules
  • Storage technologies deployed: CASTOR, dCache, DPM
slide9

MC pre-production

  • 4 production teams active:
    • 1 for OSG with contact person:
    • -- Ajit Mohapatra – Wisconsin
    • (taking care of 7 OSG CMS Tier2)
    • 3 for LCG:
    • -- LCG(1) with contact person
    • Jose Hernandez – Madrid (Spain,
    • France, Belgium, CERN)
    • -- LCG(2) with contact person
    • Carsten Hof– Aachen (Germany,
    • Estonia, Taiwan, Russia,
    • Switzerland, FNAL)
    • -- LCG(3) with contact person Nicola
    • De Filippis – Bari (Italy, UK, Hungary)
    • Large partecipation of CMS T1s and T2s involved
slide10

Monitoring of MinBias (1)

Maximum rate per day: 1.15 M

slide11

Monitoring of MinBias (2)

T1 -CNAF

Pisa

LNL

Bari

Most of the failures at CNAF were related to stageout and stagein problems with CASTOR2

slide12

Dataset statistics

Total: ~ 66 M eventsTotal FEVT: O(150) TB

  • 1. Minimum bias (40M)
  • 2. Zµµ (2M)
  • 3. T-Tbar (6M)
    • All decays
  • 4. We (4M)
    • events selected in narrow range to illuminate 2 SMs
  • 5. Electroweak soup (5M)
    • Wl nu + Drell-Yan (m>15 GeV) + WW +HWW
  • 6. HLT soup (5M): 10 effective MC HLT triggers (no taus pass)
    • W (leptons) + Drell-Yan (leptons) + t-tbar (all modes) + dijets
  • 7. Jet calibration soup (1M)
    • dijet + Z+jet, various pt-hat ranges
  • 8. Soft Muon Soup (2M)
    • Inclusive muons in minbias + J/Psi production
  • 9. Exotics Soup (1M)
    • LM1 SUSY, Z’ (700 GeV), and excited quark (2000 GeV) [all decays]

12 M of events produced by the LCG(3) team

slide13

Efficiency and problems

Efficiency:

  • Overall efficiency: 88%
    • Probability for a job to end successfully once it is submitted
  • Grid efficiency:95%
    • Aborted jobs: jobs not submitted because requirements not met (merge jobs) or jobs once submitted fail due to Grid infrastructure reason
  • Problems:
  • stage out was the main cause of job failures. More robust checking were implemented, more attempts to stage, a fallback strategy etc..
  • merge jobs caused tipically an overload of the storage system because of the high rate of read access; CASTOR2 at CNAF was tuned to cope with the needs of the production (D. Bonacorsi and CNAF admins)
  • site validation: storage, software tag, software mount points, matching of CE
  • consistency between fileblock/files in DBS/DLS and the reality at sites.

Support of Italian Tier-1 and Tier-2 very effective also in August

slide15

Tier-0tasks in CSA06

  • Reconstruction with CMSSW_1_0_x (x6)
    • All main reconstruction components included
      • Detector-specific local reconstruction and clustering
      • Tracking (only 1 algo used), vertexing, standalone , jets
      • Global  (with tracker), electrons, photons, b&tau tagging
    • Reconstruction time small (no p/u!): 4.5s/ev MB, 20s/ev TTbar
      • Computing model assumes 25 s/ev
  • Calibration/Alignment
    • Ability to pull in constants from Offline DB included for ECAL, Tracker, and Muon reconstruction
    • Direct access to Oracle or via Frontier cache
slide16
Processing for CSA officially launched October 2

First week mostly minbias (with some EWK) using CMSSW102 while bugs fixed to improve robustness on signal samples

Second week processing included signal samples at rates generally matched to T1 bandwidth metrics and using CMSSW103

After having run for about 23 days, 120M events at 100% uptime, decided to increase scale for last days

Reprocessed all signal samples in ~5 days using CMSSW106 and maximum CPU usage

Useful to re-do some samples (FEVT, Reco, AOD, AlCaReco) because of some problems/mistakes in earlier generation (missing files, missing muon objects)

Performance:

160 Hz processing rate, peaking at 300 Hz

signals, minbias, and HLT split samples

1250 CPUs for prompt reconstruction

150 CPUs for AOD and AlCaReco production (separate step)

All constants pulled from Frontier

i.e. full complexity of CSA exercise

4 weeks uptime (goal), 207M events processed

Tier-0operations

slide17

Calibration/Alignment exercise at Tier-0 CAF

  • Calibration/alignment tasks:
    • Specialized tasks to align/calibrate subsystems using start-up miscalibrated samples, e.g.
      • Align a portion of Tracker with HIP algorithmby using Z →mmsample on the central analysis facility (CAF) for prompt calibration/alignment
      • Intercalibrate ECAL crystals by phi symmetry in minbias events, 0/, or by isolated electrons from W/Z
    • Specialized reduced RECO data format (AlCaReco) to be used for calibration/alignment stream from Tier-0
    • Mechanism to write constants back into offline DB to be used
    • Re-reconstruction at Tier-1 required to test new constants
    • Propose that miscalibration is applied at RECO
  • Datasets for alignment exercise: Zµµ
slide18

Tracker Alignment exercise

  • CSA06 misalignment scenario:TIB dets and TOB rods misaligned by applying:
  • random shifts, drawing from a flat distribution of witdth +/-100 mm in (x,y,z) for the double sided modules and in x (sensitive coordinate) for the single sided ones
  • random rotations, drawing from a flat distribution of witdth +/-10 mrad, in (alpha,beta,gamma) for all the modules

TIB double sided dets positions

  • Alignment exercise:
  • to read the object in the DB, to apply the initial misalignment;
  • to run the iterative HIP algorithm and to determine alignment constants;
  • 1M events used and 10 iterations.
  • jobs running in parallel on 20 CPUs on a dedicated queue at Tier-0;
  • new costants inserted into the DB
slide19
Tomcat and squids (caching servers) in place

and tested before CSA

DB populated with some sets of constants

No miscalib., start-up miscalib. (4%), etc…

But multiple failures on first tests

Crashes (needed CORAL patch)

Logging of 28K queries/job kills servers (disabled)

Successfully in CSA by ~Oct.24

Access to DB via Frontier

Good Tests

In CSA

Failed tests

slide20

Transfer Tier-0/Tier-1s

  • All 7 Tier-1 centers participated in the challenge performing very well
    • some storage element software or hardware problems at individual sites
    • but all have recovered and rapidly cleared any accumulated backlogs
      • The longest down time at any site has been about 18 hours
  • Files are injected into the CMS data transfer system PhEDEx and transferred using FTS
    • One central service failures
      • Recovery has been rapid
    • Highest rate from CERN was 550MB/s
slide21

Transfer Tier-0/Tier-1s

…..after the prompt reconstruction at Tier-0:

Transfer to Tier1 CNAF overall successfull

slide22

Skimming data at Tier-1s

  • To fit data at T2, and to reduce primary datasets to manageable sizes, it was needed to run skim jobs at T1s to select events according to the analyses
  • Skim configuration files prepared according to the RECOand AOD format (also including some “MC truth” information)
  • Organized skim jobs ran with ProdAgent
  • Different skim procedures prepared by the users for running on the same dataset were unified in a single skim job producing different streams
  • 10 filters prepared by the Italian people to cope with the analyses prepared
  • 4 teams for running skim jobs at tier-1s
    • N. De Filippis: Electroweak soup (RAL, CNAF, ASGC, IN2P3)
    • D. Mason:Jets (FNAL)
    • C. Hof:TTbar ( FZK and FNAL)
    • J. Hernandez:Zmumu (PIC and CNAF)
  • Skim job output files shipped to Tier-2s for end-user analyses
  • 9 Oct. – T1 Skim jobs started
slide23

RECO/AOD data formats

  • First RECO/AOD definition completed for CSA06 production
  • RECO Content:
    • Tracker Clusters
      • Rec-hits skipped for disk space reasons
      • Can be recomputed from clusters
    • Ecal/HCal/Muon RecHits
    • Track “core” plus “extra” + attached RecHits
      • Refitting is straightforward from “attached” hits
    • Vertices, Ecal Clusters, Calo Towers
    • “High Level” Objects
      • Photons, Electrons (links with tracks missing…), Muons,Jets, Met (from Calo Towers and Generator)
      • Tau tagging
    • HLT output summary
      • Trigger bits + links to High Level Objects (as candidates…)
    • HepMC Generator
    • Geant 4 Tracks/Vertices
  • AOD Content: a proper subset of RECO
    • Clusters, Hits are dropped
    • Track “core” only saved
      • Can’t refit a track on AOD
      • Only muon tracks have RecHits attached in AOD
    • Vertices, Ecal Clusters, Calo Towers
    • “High Level” Objects, HepMC Generator
slide24

Monitoring of skim

jobs at Tier-1s

slide25

Transfer of skim outputs from Tier-1s to Tier-2s

  • Problems related to:
  • wrong config. of Tier-2 sites
  • wrong setup of download agents with FTS
  • CNAF related problems (FTS server, CASTOR)
slide26

Total transfer Tier-0 to

Tier-1s and Tier-2s

Exceeded 1PB in 1 month!

slide28

Analyses at Tier-2s (2)

  • All INFN Tier2s took part to the last step of the CSA06: the physics analyses starting from the output of skim procedures

Legnaro/ Padua

(Wmn selection)

Pisa

(tau validation)

(Study of minimum bias/underlying event)

Rome

(electron reco)

Bari

(tracker misalignment)

slide29

Analysis at Rome

Three analyses with goal:

to study of the electron reconstruction in Z  ee events (Meridiani)

to measure the W mass in W  en events (Tabarelli De Fatis, Malberti, CMS NOTE 2006-061)

to run a simple calibration with W  en events (Govoni)

  • Electron and Z mass reconstruction using the hybrid supercluster
    • energy (barrel only):

Eff vs h

mZ

Eff vs pT

slide30

Analysis at Pisa (1)

The general idea is to simulate a "early data taking" activity of the t group:

the goal is to study the tau tag efficiency from the Z ttevents (like described in CMS/AN 2006/074)

the goal is to study the misidentification with the recoiling jet with Z+jet, Z mm events

In addition: runt validation package on skimmed events

3) The t validation package has been run on pure di-tau sample and on skimmed ttbar sample(S. Gennai, G. Bagliesi).

pT of the jet

Isolation efficiency vs Isolation Cone :

slide31

Analysis at Pisa (2)

Study of minimum bias/underlying event (Fanò, Ambroglini, Bartalini):

  • Monte Carlo tuning for LHC
  • Pileup undestanding
  • UE contribution measurements in MB events

UE

MinBias

slide32

Analysis at Legnaro

Goal: to study the W mn preselection with different Monte Carlo data samples

Two data samples were considered (Torassa, Margoni, Gasparini):

(1) the electroweak soup (3.4 M evts, 50% Wmn and 50% DY)

(2) the soft muons (1.8 M evts, 50% minimum bias and 50% J/y, pTm > 4 GeV)

EWK soup

The transverse momentum, the efficiency vs h and vs pT as obtained with the GlobalMuon reconstructor (to be compared with standalone…)

slide33

Analysis at Bari

  • Goals: to study the effect of trackermisalignment on track reconstruction performances (De Filippis):
  • with the perfect tracker geometry;
  • in the short term and in the long term misalignment scenario by reading misalignment position and errors via frontier/squid from the offline database ORCAOFF.
  • by using the tracker module position and errors as obtained by the output of the alignment process that will be run at CERN T0.
  • Data samples used: Z→mm and TTbar (the second for computing the fake rate)
slide34

Analysis jobs at Bari

  • CRAB_1_4_0 used to submit 1.8 k jobs
  • grid efficiency = 99 %, appl. eff = 94 %
  • Bunch of 150 jobs run in different time slots
  • max 45 jobs run in parallel
  • the configuration of squid tuned to ensure that the alignment data were read by the local cache of squid via the frontier client rather than from CERN (blue histo).

 frontier/squid works as expected at tier-2 Bari when accessing alignment data

slide35

The last step of CSA06:

Re-reconstruction at Tier-1s

Goals:

to demonstrate re-reconstruction from some RAW data at Tier-1s as part of the calibration exercise

Status:

  • access of Offline database via frontier working
  • re-reconstruction demonstrated at ASGC, FNAL, IN2P3, PIC and CNAF
  • Running at RAL and further tests at CNAF

PIC

slide36

What should have

to work better (1)

  • Problems with CMSSW:
  • the "reasonability" of the code was not too much taken into account. Operations were driven by computing, and the feeing was: "whatever you run we do not care. It is enough it is not crashing".
  • as it often happens in this case, the release schedule was crazy. Also the initial milestones were somehow crazy, and it meant a really hard work to cope with them.
  • CSA06 meant blocking developments for some time, to make sure we were maintaining the backward-compatibility. But it also meant a lot of code had to live either in the head, or in pre releases for some time. It would be better to have specifically two releases ongoing at a time: a production one, and a development one.
  • - Framework proved to be usable for T0 reconstruction. HLT was not attempted at CSA06 and so no conclusions on that.
slide37

What should have

to work better (2)

  • Storage system:
  • CASTOR and DPM support (in general rfio access ) for CMS application had a lot of problems ( libdpm patched, > 2 GB files required a patch)
  • CASTOR updates too much critical for the operation during the CSA06 operations: that caused a lot of problems and an emergency status for CNAF
  • Integration issues:
  • all the pieces of the CSA06 worked (example: CMSSW releases, PA, skim jobs, DBS/DLS interactions) but
  • a lot of effort of operation teams to make them integrated each other;
  • PA: tool that required a lot of “distributed” expertise, dedicated hw/sw setup (at least three machines), real–time monitoring
  • the CMS SW installation in remote sites was problematic
  • LCG/OSG performances very good
slide38

Conclusions/suggestions

  • CSA06 was successful at INFN (all the steps were executed) but thanks to the 100 % work of few experts and to the coordinated effort of many people at Tier-1 and Tier-2 sites.
  • CSA06 was supposed to be a challenge to commission the computing/software/analysis system but in some cases it required also development/deployment of the tools
  • CSA06 analysis exercises could be as the ramp-up for the physics program/organization in Italy
  • A new CSA would be the best for 2007 with simulated and real data; focus on start-up operations (calibration and alignment) and analysis preparation
slide40

Production setup at Bari

pccms27

Apache 2.0

php 4.3.2

MySQL 5.0.22

pccms29

PhEDEx server

ProdAgent UI II

pccms28

Apache 2.0

php 4.3.2

MySQL 5.0.22

pccms6

DB mirror

ProdAgent UI I

PA_035, PA_041

PA_045, PA_047

various productions monitored

Managed By different PA versions

pccms30

Test and backup setup

+ PhEDEx injection

ProdAgent UI

slide41

Monitoring of production

via web interface

First prototype of monitoring was developed by Bari team:

slide42

Event content: RECO/AOD

CTF tracks

Stand-Alone muons

pix.electrons

Kt jets

It. cone, 5

cone-iso

strip.electrons

trk count.

Global muons

RS tracks

m.p. cone, 5

m.p. cone, 7

photons

muons

GSF tracks

MET

b/tau tags

island basic custers

pixel tracks

calo-tower cand’s

Particle Candidates

island super clusters

trigger res.

e/gamma tracks

hybrid super clusters

calo-towers

S-Alone extra

S-Alone tr. hits

HLT

corr. island s.c.

primary vertices

corr. hybrid s.c.

Global extra

Global tr. hits

AOD

CTF extra

CTF tr. hits

HB/HE hits

Track extensions

RS extra

RS tr. hits

HF hits

GSF extra

GSF tr. hits

DT 1D hits

DT 2D segm.

HO hits

pixel extra

pixel tr. hits

DT 4D segm.

ecal hits

strip clus.

pixel clus.

CSC 2D hits.

CSC segm..

preshower hits

RECO

RPC hits

Muons

Tracker/Vertices

E/Gamma

HCal/Jets/Met

slide43

Skimming filters

  • Overwhelming response from CSA analysis demonstrations
    • About 25 filters producing ~37 (and 21 jet) datasets !
    • Variety of outputs and sizes: FEVT, RECOSim, AlCaReco
slide44

Analysis at Bari

  • Goals: to study the effect of trackermisalignment on track reconstruction performances.
  • with the perfect tracker geometry;
  • in the short term and in the long term misalignment scenario by reading misalignment position and errors via frontier/squid from the offline database ORCAOFF. This step requires to refit tracks with misaligned geometry but it can be done at the T2. The effect of alignment position error APE to be checked.
  • by using the tracker module position and errors as obtained by the output of the alignment process that will be run at CERN T0 to verify the efficiency of the alignment procedure on the track reconstruction. Refit of tracks to be done in the T2.
  • Global efficiency of track recostruction, track parameter resolution and fake rate are compared in the a), b) and c) cases.
  • The same analysis was performed in ORCA. Plots and documents at link:
  • http://webcms.ba.infn.it/cms-software/cms-grid/index.php/Main/StudiesOfCMSTrackerMisalignment
  • Data samples needed: Z→mm and TTbar (the second for computing the fake rate)
slide45

The CSA06 chain of the needed data samples

  • Z→mm and TTbar samples produced during CSA06 pre-production with CMSSW_0_8_2.
  • CSA06 events reconstructed at T0 with CMSW_1_0_3 (and Zmm with CMSSW_1_0_5 in transfer)
  • 2 skim cfg files used for skimming Z→mm and TTbar sample . Skim jobs just run at T1 with CMSW_1_0_4 and CMSSW_1_0_5andoutput data in reduced format RECOSIM are produced. RECOSIM includes enough information for misalignment analysis.
    • Z→ mmfilter: to select HepMC muons from Z decay with |h| < 2.55, with pT> 5 GeV/c2 and50 < m (Z→mm) < 130. Filterefficiency between 50 and 60 %.
    • TTbar filter: to select events with two muons with |h| < 2.5 and pT> 15 GeV/c2
  • RECOSIM produced with CMSSW_1_0_4 transferred at T2-Bari and misalignment analysis run over RECOSIM with CMSSW_1_0_6.
  • ¼ of the full statistics already analyzed at T2-Bari ….waiting for all the statistics of the samples.
slide46

Track selection

  • Selection:
  • track seeding, building, ambiguity resolution, smoothing with KF.
  • ctfWithMaterialTracks refit after applying alignment uncertainties
  • track associator by c2 to match simtracks with rectracks
  • Efficiency: number of reco tracks matching simul. tracks / number of simul tracks
  • - Simul. track: pT≥ 0.9 GeV/c, 0<h<2.5 , d0 ≤3 cm, z0 ≤30 cm, nhit>0
  • Reco. track: pT≥ 0.7 GeV/c, 0<h<2.6 , d0 ≤120 cm, z0 ≤170 cm, nhit≥8
  • Fake Rate: number of reco tracks not associated to simul tracks / number of reco tracks
  • - Simul. track: pT≥0.7 GeV/c, 0<h<2.6 , d0≤300 cm, z0 ≤300 cm, nhit>8 not used because Simtrack does not have the number of simihit method → Tracking Particle will have but TP is not compatible with CSA data samples
  • Reco. track: pT ≥ 0.9 GeV/c 0<h<2.5 , d0≤3 cm, z0 ≤30 cm, nhit≥8
  • Track parameters resolution: sigma of Gauss fit to distribution of residuals
slide47

Analysis jobs at Bari

  • CRAB_1_4_0 used to submit 1.8 k jobs
  • grid efficiency = 99 %, appl. eff = 94 %
  • Bunch of 150 jobs run in different time slots
  • max 45 jobs run in parallel
  • the configuration of squid tuned to ensure that the alignment data were read by the local cache of squid via the frontier client rather than from CERN (blue histo).

 frontier/squid works as expected at tier-2 Bari when accessing alignment data

slide48

Eff. / PT resolution

with muons from Z

  • The effect of misalignment affects the global track reconstruction efficiency in the first data taking scenario.
  • The effect of tracker misalignment is enough relevant in track parameters resolution (factor 2-3 of degradation)
slide49

Track param. Resol.:

d0 and z0

  • A factor between 2 and 3 in impact parameters resolution due to misalignment
slide50

Z mass from di-muons

Using CSA06 Z→mm sample

The Z mass resolution is increased by a factor larger than 2 in the first data taking scenario (RMS from 1.3 to 2.8)