Hi Gyorgy, Krisztian and Adam, (Roberto, your help might be needed for point 2) below) I try to summarise what we just discussed via EVO and then will try to detail out several steps as good as I can. Then we will have a next chat tomorrow (Wed) at 17.30 h to discuss arising problems: Title: MC Tk Alignment Chat Community: CMS Password: mc Meeting Access Information: - Meeting URL http://evo.caltech.edu/evoNext/koala.jnlp?meeting=M9MIMi2D2iD2Ds9t9uDD99 - Password: mc - Phone Bridge ID: 421 3929 Password: 5317 Task: Finding an Alignment (including sensor bow parameters) on MC that performes close to the current new data alignment concerning - momentum biases as seen with Z-mass validation - local precision as seen with track based validation (DMR etc.) This means three sub-tasks: 1) Running an alignment procedure on MC ALCARECO similar (concerning free parameters, data set compositions [samples and pt/eta/phi distributions] ) to the data procedure 2) Validate the result using the Z-mass vs eta/phi/... (comparing to data etc.) 3) Track based validation to see DMedianR/normalised DRR (comparing to data etc.) Now more details: 1) Alignment ============= At .../ALCA_TRACKERALIGN/MP/MPproduction/mp0895 I have setup a MC alignment starting from the current MC scenario plus sensor bow parameters from data (i.e. 'mis-bow'). If you have run this succesfully, a second option to explore is to start from ideal alignment plus sensor bow parameters from data. (Starting geometry defined in file startgeometry.txt - see below -, the tag would be 'TrackerIdealGeometry210_mc' and connect string 'frontier://FrontierProd/CMS_COND_31X_FROM21X') To run the setup, create a new mps-directory and - copy these files to it: alignables.txt empty.txt peakLA.txt startgeometry.txt alignment_cosmics.py alignment_isolated_mu.py alignment_minbias.py alignment_z.py setup_align.pl - do cd <...>/ALCA_TRACKERALIGN/MP/MPproduction/CMSSW_4_2_4_patch1_TkAl3 cmsenv (in fact, my tests used ...CMSSW_4_2_4_patch1_TkAl1, but ..3 gives better diagnoses of the bows) - take care that setup_align.pl is executable (chmod ...) - execute it, best is to store its output for reference: ./setup_align.pl > dump - send jobs using mps_fire.pl, check status with mps_stat.pl/mps_fetch.pl and finally send the ped job mps_fire.pl -m [or use mps_auto.pl 300, see https://twiki.cern.ch/twiki/bin/view/CMSPublic/SWGuideMillepedeIIProductionEnvironment] The pede job might not run out-of-the-box due to memory issues (mps_fetch.pl should tell you once the merge job is DONE). Please let me know the output to diagnose and suggest further actions. Input samples are currently - isolated muons from W (16.3 M tracks) - isolated muons from QCD-mu enriched events (2.3 M tracks, some [!] lower pt) - minbias tracks (450 k events - about 4.5 M tracks??) - Zmumu events using mass constraint (390 k events used in pede) - peak mode cosmics (1.8 M used tracks) - deco mode cosmics (1.7 M used tracks) This breakdown is experimental: The aim is to have similar overall statistics and pt/eta/phi distributions as Joerg has used in data (see his presentation last Thursday). You find some basic track distribution in the millePedeMonitor*root files that you find in the jobData/jobLMN directories (after the pede job is run you find it in only in EOS storage where the binaries are as well). A subtask is to get 'similar' distributions, by either changing the number of input events per job for a specific sample (e.g. search for maxEvents in alignment_minbias.py), or by weighting the samples in the pede fit. This can be done by weighting adding, e.g. for a weight of 0.8, "-- 0.8" behind the the .dat files in the config of the merge job, e.g. see .../mp0860/jobm_it2/alignment_merge.py. For this you have to find out which binary number belongs to which sample - the dump from setting up the aignment can help to diagnose this (note that the order can change due to the 'confhash' map used in perl...!) But before experimenting with/fine tuning of the input, do the validation - then we might have to iterate. 2) Z validation =============== The Z-mass peak position as a function of eta and phi of the positive/negative muon, maybe also something like vs phi for eta > 0.9 or so. Either write you own simple analyser (as Joerg did), otherwise I hope that Roberto Castello (in CC) can help. It would be good to overlay - ideal alignment - the current MC alignment (i.e. the start geometry in the current setup) - the data (- maybe Joerg's first shot) 3) Track based validation ========================= We need the DMR and normlaised DRR plots as youhave produced them in the past already. The tricky things are a) to get similar statistics and pt/eta distributions for the sample testing the new MC alignment with MC events and the data alignment with data b) not to forget to load the correct TrackerSurfaceDeformationRcd as determined in your alignment To achieve a) I suggest to validate in data on the longest of our periods (B-I), but do a pt cut of e.g. 20 GeV and then validate MC on a fraction of similar size from /WJetsToLNu_TuneZ2_7TeV-madgraph-tauola/Summer11-TkAlMuonIsolated-PU_S4_START42_V12-v1/ALCARECO =============================== So far for today - I guess many details are left out, but of course you have some experience with alignment and validation already, so I hope you can fill some holes. Now I just remind that validating is probably taking more time than actully doing the alignment, so I suggest to get used to the validation already testing the existing alignments (ideal, current misalignment scenarios). Cheers Gero -- ----------------------------------------------------------------------- Gero Flucke - Analysis Centre, Helmholtz Alliance "Physics at the Terascale" * Statistics Tools - CMS: Tracker Alignment Convenor DESY/CMS, Notkestr. 85, D-22607 Hamburg, Germany Bldg. 1e, Rm. 02.501 phone: +49 (0)40 8998 3525 fax: +49 (0)40 8998 3092