ALICE Physics Week, Muenster, Andrea Dainese 1 Andrea Dainese, INFN Legnaro work with Andrea Rossi, Padova University Preparation for D 0  K  analysis

ALICE Physics Week, Muenster, Andrea Dainese 2 Invariant mass analysis and significance maximization (in bins of p t, y,   RP …) Analysis Scheme Event reconstruction (RAW  ESD  AOD) Charm “production” (ESD/AOD  AOD for D’s) D 0 signal selection (using PID and geom/kinem cuts) Correction for efficiencies, acceptance, BR Cross section normalisation

ALICE Physics Week, Muenster, Andrea Dainese 3 MC Sample for Tools Development 5×10 6 pp events at 14 TeV (PYTHIA “PhysicsRun” cocktail): 85% pp min. bias w/o heavy quarks ~14% pp (MSEL=1) with charm, p t hard -binned ~1% pp (MSEL=1) with beauty, p t hard -binned ALICE baseline: cross sections from NLO pQCD with “best guess” set of parameters Fast production: TPC param + full ITS, no TOF, no TRD Generated in Legnaro, Torino, CNAF in April mb mb 0.007

ALICE Physics Week, Muenster, Andrea Dainese 4 Invariant mass analysis and significance maximization (in bins of p t, y,   RP …) Analysis Scheme Event reconstruction (RAW  ESD  AOD) Charm “production” (ESD/AOD  AOD for D’s) D 0 signal selection (using PID and geom/kinem cuts) Correction for efficiencies, acceptance, BR Cross section normalisation

ALICE Physics Week, Muenster, Andrea Dainese 5 Event reconstruction (RAW  ESD  AOD) Reconstruction in the barrel track parameters with and without primary vertex constraint Alignment correction performed ITS: expected residual misalignment per module (talk by A.Jacholkowski)  SPD: 10  m (r, r  ), 20  m (z) + rotations/tilts  SDD: 20  m (r, r , z) + rotations/tilts  SSD: 13  m (r, r  ), 10  m (z) + rotations/tilts different alignment parameters to be used in MC for syst. error evaluation Primary vertex reco (crucial for heavy flavour analyses!) Standard AOD (talk by M.Oldenburg), extracted from ESD AliAODEvent - Part of the event Meta Data - Global event information charged tracks vertices (V 0,...)

ALICE Physics Week, Muenster, Andrea Dainese 6 Primary vertex from tracks warning: 90% of tracks from D decay used to find primary vertex (talk on Friday) primary K,  -decay D-decayB-decay

ALICE Physics Week, Muenster, Andrea Dainese 7 Track impact parameter measurement in pp collisions Track impact parameter resolution:  d0 =  vtx   track Vertex reconstructed from tracks Bias (underestimate of d 0 ) if the considered track is used for vertex fit:  of d 0 distribution for primaries  m  bias!  of d 0 /error(d 0 ) distr. for primaries ➔ primary vertex has to be reconstructed for each D candidate excluding its daughters: time-consuming!

ALICE Physics Week, Muenster, Andrea Dainese 8 Effect of ITS misalignment on d 0 resolution Impact parameter resol:  track = a scatter /p t  b meas  c misalign Use ITS toy model no geometry just gaussian smearings Determine the c misalign parameter for different misalign. scenarios Estimate being repeated with full AliRoot simu/reco chain Effect on D 0 significance being studied

ALICE Physics Week, Muenster, Andrea Dainese 9 Invariant mass analysis and significance maximization (in bins of p t, y,   RP …) Analysis Scheme Event reconstruction (RAW  ESD  AOD) Charm “production” (ESD/AOD  AOD for D’s) D 0 signal selection (using PID and geom/kinem cuts) Correction for efficiencies, acceptance, BR Cross section normalisation

ALICE Physics Week, Muenster, Andrea Dainese 10 Production of D 0  K -  + candidates Analysis with opposite-charge track pairs all tracks with 6 ITS points (possibly) two selection steps: single-track cuts, to reduce combinatorics (CPU time & disk space) --- secondary vertex reconstruction (talk on Friday) --- candidate cuts implemented in AliD0toKpi and AliD0toKpiAnalysis classes being adapted to AOD / AliAnalysisTask scheme 

ALICE Physics Week, Muenster, Andrea Dainese 11 CharmAOD: Classes Layout AliAODVirtualParticle AliAODRecoDecay AliAODRecoDecayHF AliAODv0 AliAODRecoDecayHF2Prong AliAODRecoDecayHF3Prong (already exists; PWG2 agreed to adapt it to this scheme) done to be done

ALICE Physics Week, Muenster, Andrea Dainese 12 CharmAOD scheme (with F.Prino) single-track cuts on p t and d 0 (p t ) need common cuts for all analyses (to be studied) build all (+,-) pairs and compute secondary vtx create AliAODRecoDecayHF2Prong apply D 0 reco cuts on d 0  d 0, cos  pointing and mass store loop on all tracks (+ & -): 1) build triplets, create AliAODRecoDecayHF3Prong, apply reco cuts (common for the 3 particles?) 2) if pair has D 0 mass, attach track and create a D *+ candidate loop on all tracks (+ & -) … create AliAODRecoDecayHF … store for D +,D s +,  c +, D *+ for D 0  K  for D 0  K 

ALICE Physics Week, Muenster, Andrea Dainese 13 Invariant mass analysis and significance maximization (in bins of p t, y,   RP …) Analysis Scheme Event reconstruction (RAW  ESD  AOD) Charm “production” (ESD/AOD  AOD for D’s) D 0 signal selection (using PID and geom/kinem cuts) Correction for efficiencies, acceptance, BR Cross section normalisation

ALICE Physics Week, Muenster, Andrea Dainese 14 Selection Variables & PID Variables: (p t of decay tracks) DCA: distance of closest approach between the two tracks cos  *: cosine of decay angle d 0 xd 0 : product of the two tracks r  impact parameters cos  pointing : cosine of pointing angle 3 PID “modes” considered: no PID K id required ( ,  ), (K,K) pairs rejected

ALICE Physics Week, Muenster, Andrea Dainese 15 Extraction of signal raw yield: Invariant mass analysis Here, 5 million pp events (1/200 of 1-year’s statistics) p t > 0 “PPR cuts”

ALICE Physics Week, Muenster, Andrea Dainese 16 Extraction of signal raw yield: Invariant mass analysis Beware of reflections: Reflections Combinatorial no PID RS/TS~0.30 RS/TS~0.25 RS/TS~0.20RS/TS~0.15 work by C.Ivan PID: ( ,  ), (K,K) rejected

ALICE Physics Week, Muenster, Andrea Dainese 17 Extraction of signal raw yield: Invariant mass analysis Two possible approaches: direct fit w/o bkg subtraction (only if large S/B, > 5-10%?) background subtraction with event mixing + fit Direct fit tried: exponential + gaussian fit side-bands with expo: initial expo parameters fit everything with expo + gauss with constraint on total integral From Fit Total S integral (+- 3  ) = S/B (+- 3  ) = 85% SGNC as S/  S (+- 3  ) = 4.3 (58 for 10 9 evts) SGNC as S/  S+B (+- 3  ) = From Simulation Total S integral (+- 3  ) = 39.5 S/B (+- 3  ) = 90% SGNC as S/  S+B (+- 3  ) = 4.3 (59 for 10 9 evts)

ALICE Physics Week, Muenster, Andrea Dainese 18 0<p t <1 GeV/c Significance maximization Optimize cut values in p t bins: maximize significance in cut- variables space (e.g. 4D space) Example (work by A.Rossi): 2<p t <3 GeV/c3<p t <5 GeV/c5<p t <8 GeV/c

ALICE Physics Week, Muenster, Andrea Dainese 19 Results (scaled to 10 9 events) Checked performance w/o PID: not much worsening, but have to tighten cuts (larger systematic errors?) PPR  S /S=1/SGNC

ALICE Physics Week, Muenster, Andrea Dainese 20 Invariant mass analysis and significance maximization (in bins of p t, y,   RP …) Analysis Scheme Event reconstruction (RAW  ESD  AOD) Charm “production” (ESD/AOD  AOD for D’s) D 0 signal selection (using PID and geom/kinem cuts) Correction for efficiencies, acceptance, BR Cross section normalisation

ALICE Physics Week, Muenster, Andrea Dainese 21 Corrections & Errors: Beauty feed-down (~10%) HOW? 1.Monte Carlo with state-of-the-art pQCD input 2.use measurements from ALICE (from single , then e) SYSTEMATIC ERROR: uncertainty on b-bbar cross section from theory (1) or data (2). For (1), the syst. error estimated to be ~8-10% according MNR; should be smaller according FONLL

ALICE Physics Week, Muenster, Andrea Dainese 22 Corrections & Errors: Acceptance, Reco & Selection Efficiency HOW? Embedding of MC signal in real events, and calculate all corrections in one go (selected  in-acceptance). Average correction in {p t, y} grid NEEDED: tuned MC (good descr. of tracking effs and resols). d 0 resol. is crucial, must be evaluated from data vs p t, , PID, N ITSclusters SYSTEMATIC ERROR: - compare weights in different runs, and with the two field orientations (+z and -z) - check stability of extracted yield VS variation of cuts - repeat weights calculations (MC) with different sets for alignment corrections

ALICE Physics Week, Muenster, Andrea Dainese 23 Evaluation of d 0 res. from data Evaluation of d 0 resolution: d 0 distribution is dominated by primary particles for |d 0 |<d 0 MAX Fit in this range provides the resolution, to be then compared with that in AliRoot

ALICE Physics Week, Muenster, Andrea Dainese 24 A.Rossi p t  0.4 GeV/c primaries all

ALICE Physics Week, Muenster, Andrea Dainese 25 A.Rossi p t  1.1 GeV/c primaries all

ALICE Physics Week, Muenster, Andrea Dainese 26 A.Rossi p t  4.5 GeV/c primaries all

ALICE Physics Week, Muenster, Andrea Dainese 27 A.Rossi limited statistics! d 0 MAX /  Fit range for evaluation of resolution Conclusions: a fit of all tracks’ d 0 distr. in the range ~ (-3 , +3  ) allows to extract the d 0 resolution resolution is a convolution of track position resolution, primary vertex resolution, misalignment effects Open points: devise method to separate track and vertex contributions study other methods (e.g. using cosmic  crossing all detector)

ALICE Physics Week, Muenster, Andrea Dainese 28 Summary Performed realistic D 0 analysis on 5 million pp events (PDC06-like sample) First version of invariant mass fit Developed multi-dimensional SGNC maximization method Results (signal SGNC) compatible with PPR ones To be repeated asap on PDC06 events Long TO-DO list port analysis to AOD/AliAnalysisTask framework effect of misalignment procedure for corrections...

ALICE Physics Week, Muenster, Andrea Dainese 29 EXTRA SLIDES

ALICE Physics Week, Muenster, Andrea Dainese 30 First pp data: machine scenario  * ~ 10 m (will be 0.5 m for Pb-Pb) Bunch spread: transverse  bunch (  *) ~ 70  m, long.  bunch ~ 7.5 cm Vertex spread: transverse  vertex =  bunch /  2~50  m, long.  vertex = 5.3 cm Luminosity: (  1/   vertex ) ~10 30 cm -2 s -1 (70 kHz for  pp = 70 mb) Revol’s scenario: 20x10h ~5-10x10 7 min. bias events Subsystems directly used for D 0 analysis: ITS, TPC, TOF Assumption: TPC, full ITS, half TOF (-0.9<  <0.9,  =  ) First pp data: detector scenario

ALICE Physics Week, Muenster, Andrea Dainese 31 Candidate cuts (1)

ALICE Physics Week, Muenster, Andrea Dainese 32 Corrections & Errors: Acceptance, Reco & Selection Efficiency Selection efficiency: from selected to reconstructed signal Reconstruction eff.: from reconstructed to in-acceptance Acceptance: from in-acceptance to dN/dy at y=0 central Pb-Pb

ALICE Physics Week, Muenster, Andrea Dainese 33 Beauty feed-down Feed-down can be up to 15% after selection Can use upper cut on |d 0 | to control it To subtract it: 1)use simulation with state- of-the-art pQCD c and b predictions for LHC 2)later, use b cross section measured at LHC 3)later, use d 0 of D 0 to estimate feed-down

ALICE Physics Week, Muenster, Andrea Dainese 34 Simulation & Reconstruction Detectors: full transport in ITS, track references at TPC R in Primary Vertex:  * = 10 m   x =  y = 50  m, as expected for the 2007 run Reconstruction: TPC tracking response parametrized (old parametrization from 2002  conservative efficiencies and resolutions) Slow Points in ITS z of vertex from SPD Kalman in ITS with AliITStrackerV2 full vertex reco with tracks NOT INCLUDED (to be done before analysis)

ALICE Physics Week, Muenster, Andrea Dainese 35 TPC parametrization full tracking TPC param full tracking TPC param full tracking TPC param TPC param / full tracking

ALICE Physics Week, Muenster, Andrea Dainese 36 Reco & Sele TO-DOs TO DO for single-track cuts (p t and d 0 ): - estimate CPU time, signal and background efficiency vs cut values - estimate cut values compatible with “final” candidate cuts TO DO for secondary vertex: implement vertex fitting with errors and  2 (as done for primary) TO DO for inv. mass analysis: prepare fit procedure/code; take into account reflections in fit; background subtraction IN PROGRESS IN PROGRESS, talk on Friday

ALICE Physics Week, Muenster, Andrea Dainese 37 Corrections & Errors TO-DOs TO DO for beauty feed-down: - define procedure to generate MC with B -> D 0, and reweight results according to a given pQCD d  b /dp t - define procedure to use ALICE single  measurement to infer B production in the barrel TO DO for systematic errors: - define procedure for embedding (how many {p t,y} bins? needed MC stats? which real events? vs dN ch /dy? vs z vertex ?) - define procedure to evaluate d 0 resol. from data IN PROGRESS