MesonEx Chain. From Detector to Publication... Perspective of one CLAS12 experiment. Derek Glazier, University of Glasgow

Transcription

1 MesonEx Chain From Detector to Publication... Perspective of one CLAS12 experiment Derek Glazier, University of Glasgow With HASPECT collaboration : Edinburgh, JPAC, Giessen, Glasgow, Genova, Mainz, Ohio, Torino, Roma,...

2 MesonEx with CLAS12 Primary Physics Goal for CLAS12 e beam Nucleon Structure But high potential for meson spectroscopy Strategy : High Intensity electron Beam Tag quasi real photons Large Acceptance Magnetic Spectrometer Many final states Linearly polarised photons Amplitude analysis sensitive to small contributions Close interplay of exp theory

3 HASPECT Overview Statistical Methods Experimental Data Particle Reconstruction Event Selection Simulated Data Forward Tagger Simulation Event Generator Physics Analysis IU AmpTools JPAC HASPECT Activity Goal Well defined optimal techniques Not ad hoc solutions

4 CLAS12 - Forward Tagger Detect electrons at small angle to perform quasi-real photo-production experiments. Calorimeter Calorimeter: electron energy/momentum Photon energy (ν=e-e') Polarization ε ν2/2ee PbWO4 crystals with APD/SiPM readout Tracker Scintillation Hodoscope: veto for photons Scintillator tiles with WLS readout Tracker: electron angles, polarization plane MicroMegas detectors e Forward Tagger gv e N CAD implementation CLAS12 Scintillation Hodoscope Tracker FEE

5 Benchmark Reaction 11 GeV e scattering in 5cm lh2 target Luminosity ~ 1035cm 2s 1 e' detected in forward tagger γ energy ( GeV) and polarisation σe= GeV 3π detected in CLAS12 σp=0.5 %, σθ=1 mrad, σφ=3 mrad Resolution allows good discrimination from other final states (simulation) Expected number of reconstructed events from initial low luminosity data (20 days) Full field Half field Neutron reconstructed by missing mass 80 day experiment with full luminosity 80 X more events or 106/10MeV

6 Simulated Amplitude Analysis a2 to ρπ S wave a1 to ρπ S wave a1 to ρπ D wave π2 to ρπ P wave π2 to ρπ F wave π2 to f2π S wave π2 to f2π D wave π1 to ρπ P wave Total Search for π1(1600) exotic in 3π final state Detector response capable of reconstructing signals <1% Developing our amplitude analysis techniques to better interpret such small signals

7 Analysis Strategy 1) Beam Asymmetry for Pseudoscalers 2) Cross Sections Probe Production Mechanisms 3) Spin Density Matrix Elements for Vectors 4) Amplitude Analysis of 2 Meson final states 5) Amplitude Analysis of other accessible final states Extract Mesonic States

8 Route to Publication MesonEx CLAS12 + gemc Data Taking Raw Data Detector coatjava response Calib Track Recon coatjava 4Vectors calib +... Final State Model Paper Event Select Physics Analysis Publish Event Filter Physics Result Write/ Review Every Process needs to have a well defined procedure Pre reviewed by collaboration Every Product needs to be checked and validated Before final review possible

9 Route to Publication MesonEx CLAS12 + gemc Data Taking Raw Data Detector coatjava response Calib Track Recon coatjava 4Vectors calib Month 12Month 3Month Typical ~ 6 years Final State Model Paper Event Select Physics Analysis Publish Event Filter Physics Result Write/ Review 18Month 24Month 18Month * For a general, non fast tracked analysis

10 Route to Publication MesonEx CLAS12 + gemc Final State Model Paper Event Select Physics Analysis Publish Event Filter Physics Result Write/ Review 6 Month 12Month 3Month Typical ~ 6 years 18Month 24Month 18Month 6 Month 6Month 3Month Possible ~ 3.5 years 3Month 12Month 6Month Data Taking Raw Data Detector coatjava response Calib Track Recon coatjava 4Vectors calib +... * For a general, non fast tracked analysis

11 CALIB See CalCom/Software Group Detector response Validate : Standard detector calibration monitors Calib coatjava Process : CLAS12 offline analysis calib Already well defined Requirements : (for each detector) Responsible Person Technical report Calibrator for each data set Vailidation Spectra

12 Track Reconstruction See CalCom/Software Group Process : coatjava Well defined reconstruction software Track Recon Validate : 4Vectors Tracks, PID, Normalisation CLAS12 detector and gemc data +... Requirement before any analysis note Requirements: Validated calibrations Cooker Well defined Spectra, e.g. KinFit Pulls, miss mass Run Group, Offline Tech. WG... to Validate ROOT output

13 ROOT Output Options? Coatjava provides direct ROOT output Coatjava provides evio output, converted to ROOT Structure? Filter on final state Physics : 4 Vector, Vertex, timings, PID... Banks : All detector informations Hybrid : Physics + Selected Banks info Requires consultation between experiments and software group

14 Event Selection Typically time consuming... Validate : Final e.g. Signal and Background simulation State extract true signal Requirement before any analysis note Event Process : Recon Standard procedures, prereviewed and accepted by collaboration Event Perhaps software made available Select e.g Probability Weighting: Qfactor Cuts: Boosted Decision Trees, KinFit, Subtractions: Sideband, splots Requirements : (for each technique) Technical Note Review for benchmark analysis Validation procedure for each analysis Data Analysis Commitee?

15 Physics Analysis I Model independent data Process : extract Acceptance Corrected Yield >Cross Section Obs. Asymmetries Fit Spin density matrix elements Physics Events? Analysis Validation : e.g. Signal and Background simulation extract true observable Compare previous data Requirements : Standard Tools? Data Analysis Commitee? Or internal experiment review(e.g. MesonEx) Physics Result

16 Physics Analysis II In association with JPAC/... Model Process : Complex Amplitude Analysis Use of standard tools : AmpTools, PyPWA and models (JPAC) Physics Analysis Physics Result Validation : Consultation with JPAC... Requirements: JPAC approved amplitudes JPAC member associated to each analysis Feedback JPAC and CLAS12 analysers

17 Publish Paper...To the journal Publish Write/ Review Process : Write final analysis note Reviewed Write paper Reviewed Requirements: Previous validation stages should be complete Review should be quick checking this Analysis Note Template

18 Summary We are preparing to analyse data for MesonEx Software being developed (not shown here) for event generator, event selection, amp. analysis Many obervables and final states accessible Requires efficient and reliable analysis chain Well defined procedures and validation for : calibration, track reconstruction, event selection, physics Final Analysis note should check these are done Publication speed should be optimised Consulation within collaboration required other experiments, software group

19 Data Paths Take either path : MC Phase Space Toy Toy Fits(Validation) Fits Real Fits(Physics) EdGen Real Fits Gsim GEMC... CLAS CLAS12... user_ana CLAS12R... user_ana CLAS12R... = LorentzVectors AmpTools Generator HASPECT (ROOT) = HASPECT (ROOT) Reconstruct phase space AmpTools Configuration Model Fold in model AmpTools Fitter AmpTools Format Lorentz Vectors Other (user defined)

20 HASPECT Event Reconstruction Provide code to handle routine tasks allowing procedures to become standardised Input/Output/Interfacing Histogramming Particle/reaction identification Event weighting Maintain normal ROOT flexability for users Users shift to physics and systematic studies Promote full potential of ROOT Based on TSelector Tree analysis class Use of TEntryList class to prevent duplicating data ROOT system takes care of compilation and configuration Parallel ROOT Facility (PROOF) Statistical Analysis Packages (RooFit/Stats)

21 Event Reconstruction : sweights M. Pivk,F.R. Le Diberder,Nucl.Inst.Meth.A 555, , 2005 Given discriminatory PDF for signal and background calculates weight : Part of RooStats(used here) Can include multiple signal and background species Ns = Number of species fk = PDF for species k Nk = Yield for species k V = covariance matrix Can fit multidimensional discriminatory PDF Only as good as fit model... Pentaquark paper Can use directly in likelihood fits

22 Event Reconstruction : Simulated Models Signal shapes are not always smearing well described by parameteric functions Simulated PDFs systematic uncertainty in α shape accounted for via morphing with additional nuisance parameters i.e Profile Likelihood Construct new RooFit PDF Supply simulated events Sequential 1D histograms Smoothed and interpolated Adding greater additional smearing with morphing parameter α Additional offset parameter (Also RooFit HistFactory...) scale offset

23 Event Reconstruction : π+π-p Just Phase Space g11 dataset, detect π and p Model from simulated π+π p and π+π π0p events Signal BG RooFit Extended Maximum likelihood fit RooStats sweight calculation For Disentangle distributions Cross section + For amp analysis ρ Not in ω simulated model Note 2 fits required. First fixes alpha and off. Second, only Yields free => Covariance matrix

24 THSSimFit* RF=new THSSimFit( SF ); //Manager class RF >LoadVariable("Mmiss[ 0.1,0.15]");//should be same name as variable in tree RF >LoadAuxVars("Eg[3,4]");//should be same name as variable in tree RF >LoadAuxVars("fgID[0,1E12]");//should be same name as variable in tree RF >LoadAuxVars("t[0,0.4]");//should be same name as variable in tree /////////////////////////////Make Model Signal RF >Factory("THSEventsPDF::Signal(Mmiss,alpha[0.02,0,0.04],off[0, 0.005,0.005],10)"); TChain chainmcl("hsparticles","mcsignal"); chainmcl.addfile("mc_ppip_cor.root"); //add mc data to make model ((THSMorphPDF*)RF >GetWorkSpace() >pdf("signal"))\\ >AddSmearedModel(&chainmcL,RF >GetAuxVars()); //////////////////////////////Make BG model (same code again)... RF >LoadSpeciesPDF("Signal"); RF >LoadSpeciesPDF("BG"); Only really need to RF >TotalPDF();//Total PDF ///////////////////////////Load Data configure variable and TChain chain("hsparticles"); file names chain.addfile("twopi_ppip_pmiss.root"); RF >LoadDataSet(&chain);//import to RooFit //////////////////////////Fit Model to data RF >FitWeights(10); RF >DrawTreeVar("MPipm",200,0,2); RF >DrawTreeVar("MmissP",200,0,2); *Developed with Dominik Werthmueller, Simon Gardener (Glasgow)

25 Useful Class THSBins Can split input data into N dimensional bins and peform fit for each bin RF >LoadBinVars( Eg,10,3,4); Double_t tbins[]={0.1,0.2,0.3,1,2,5}; RF >LoadBinVars( t,5,tbins); Bin number stored as a branch in an associated tree. This tree is used to filter events for individual fits Can be used to create persistant RooFit Workspace, containing data and PDF for each bin. RF >PrepareForFarm(); Workspaces can be sent to farm for faster processing **Good to split data to reduce correlations Need better method for smooth behaviour between bins

26 Useful Class THSWeights Container for sweights (or any other weight) Requires global event ID to synchronise with events (Passed from THSRooFit) Stored in TTree with branch names = species (e.g. Signal, Background ) THSWeights* wts=new THSWeights("TotalWeights"); //If fits done on farm merge all bins wts >Merge("WeightsEg","WeightsAll.root"); //Loop over data get current event ID wts >GetEntryBinarySearch(ID);//Get Weight for ID Weight = wts >GetWeight("Signal");

27 Additional Tricks Perform multiple fits to find best parameters Can do binned chi2 for speed Can limit range and merge background for sweight Fit Fit 1 Fit3 Winner! Fit 2 sweight Fit Shrink Range =>less BG to subtract Merge 3pi and pi0

28 Crystal Ball : pi0 Photon Asymmetry Analysis Simon Gardner (Glasgow) paper ready First fit photon time Use weight Then Miss mass W=1246,Theta=90 W=1421,Theta=114

29 Example Analysis Each step uses new selector Reconstru data Filter final state Make THSParticles Code automatically generated for each step. Users fill in details Calc. Var.s Explore data histograms Calc. Var.s Filter New tree Qvalue New tree Use Weights Histograms sweights New Tree Merge Weights With particle tree Physics