Data Processing Group activities: status and plans. F. Prino, C. Zampolli

Transcription

1 Data Processing Group activities: status and plans F. Prino, C. Zampolli DPG Plenary Meeting, February 21 st 2017

2 Outline DPG organization DPG material New twiki pages with documentation on data sample reconstruction, analysis tools Data Processing: status and plans Good run lists Reconstruction of p-pb data and guidelines for the analysis Monte Carlo productions: status and plans Summary of QM campaign Productions anchored to the p-pb sample(s) QA tools and Analysis Objects and Tools Towards 2017 data taking Alignment and Calibration plans for 2017 New HLT cluster finder Conclusions 02/02/17 F. Prino, C. Zampolli 2

3 detectors detectors DPG Coordination DPG Coordination Chiara Zampolli Francesco Prino PROC Processing Roberto Preghenella Catalin-Lucian Ristea (*) Chiara Zampolli detectors The DPG in alphabetical order: QAT QA and Tools Marie Germain, E. Botta Jacek Otwinowski (*) PWGs AOT Analysis Objects and Tools Catalin-Lucian Ristea (*), David Dobrigkeit Chinellato, Andrea Dainese & Andrea Rossi (*) Recently joined DPG coordination, welcome! PWGs 02/02/17 F. Prino, C. Zampolli 3

4 New twiki page for the Run-2 data samples with completed data processing, summarizing: Main characteristics Reconstruction passes Links to the RCT, QA, run lists MC productions DPG Twiki: news eriodssummary 02/02/17 F. Prino, C. Zampolli 4

5 DPG Twiki: news Twiki page with guidelines for the analysis of Run-2 data samples How to merge together different LHC15o reconstruction passes (with w/o pidfix, cookdedx) NEW: how to merge the three reconstructions of the p-pb sample (CENT_wSDD, CENT_woSDD, FAST) 02/02/17 F. Prino, C. Zampolli 5

6 DPG Twiki: news New twiki page on pileup in Run-2 samples summarising: basic information about same-bunch and out-of-bunch pileup impact on the data analysis tools to remove/cleanup the samples (at the event and track selection level) 02/02/17 F. Prino, C. Zampolli 6

7 Data reconstruction campaign in PbPb, 5 TeV (LHC15o) 2015 pp, 5 TeV (LHC15n) 2016 pp, 13 TeV (LHC16k, l) 2016 ppb, 5 TeV (LHC16q) 2016 ppb, 8 TeV (LHC16r) 2016 Pbp, 8 TeV (LHC16s) 2016 ppb, 5 TeV (LHC16t) From: 02/02/17 F. Prino, C. Zampolli 7

8 Fast cluster in p-pb data sets The 2016 p-pb data collected with the FAST cluster active Difference between MB-FAST and MB- CENT is SDD (slowest detector in Alice) When SDD is BUSY the event is collected without SDD MB-FAST contains all MB triggers both with and without SDD MB-CENT are the events with SDD (subsample of FAST) About 50% of the events are without SDD for LHC16q and LHC16t For LHC16r and LHC16s the FAST cluster was activated only in few runs Three reconstructions preformed: pass1_cent_wsdd: all events in the CENT cluster, i.e. with SDD in the readout, reconstructed including SDD in the tracking pass1_cent_wosdd: all events in the CENT cluster, i.e. with SDD in the readout, reconstructed excluding SDD from the tracking pass1_fast: all events that are only in the in the FAST cluster (and not in the CENT), i.e. without SDD in the readout 02/02/17 F. Prino, C. Zampolli 8

9 Fast cluster in p-pb data sets Tracking and PID performance with and without SDD (details in backup slides): Events without SDD cannot be used in analyses which use ITS PID Special attention to events without SDD needed in analyses using ITS standalone tracks Slightly (5%) lower values of TPC-ITS track matching efficiency for events without SDD Same resolution on the track impact parameter in the transverse plane (DCAxy) Less uniform azimuthal angle distribution of tracks matched to ITS for events without SDD Worse resolution on the lambda and K0s invariant mass peaks for events without SDD Suggestions on how to use the three samples in the analysis: Analyses that do not need the full statistics should use CENT_wSDD (better performance) Analyses that need to use the full statistics: CENT_wSDD and CENT_woSDD are two reconstructions of the same events, should not be summed together To have a sample with best possible tracking performance: use CENT_wSDD and FAST NOTE: cross check for systematics related to mixing events with and without SDD. To have a sample with homogeneous usage of SDD: use CENT_woSDD and FAST NOTE: not necessarily this minimizes the systematics 02/02/17 F. Prino, C. Zampolli 9

10 QA and Run Lists Lists of good runs in AliDPG twiki based on QA output: five lists created requiring: QA good for detector + full acceptance (i.e. RCT flag 1) Tracklets: (ITS + V0 + ZDC) Central Barrel tracking (ITS+V0+TPC + ZDC) Central Barrel Tracking hadron PID: (ITS+V0+TPC +TOF+T0+ ZDC) Central Barrel Tracking electron PID: (ITS+V0+TPC +TOF+TRD ZDC) Central Barrel Tracking calo: (ITS+V0+TPC +EMC+ ZDC) Available lists of good runs: Recent news: Additional list provided for the Central barrel tracking case Including runs for which the TPC is good but with incomplete acceptance (RCT flags 1 19) Run lists created for the 4 p-pb samples: LHC16q, LHC15r, LHC16s and LHC16t New version (v2) with 2 additional good runs for LHC16k 02/02/17 F. Prino, C. Zampolli 10

11 LHC16k, LHC16l Statistics LHC16k (pp 13 TeV) IR ~120 khz (but a few runs lower) Phys Sel Eff in [85%, 98%] LHC16l (pp 13 TeV) IR ~120 khz (but a few runs lower) Phys Sel Eff in [90%, 97%] N.B.: 2 runs still in QA 02/02/17 F. Prino, C. Zampolli 11

12 ppb and Pbp samples: statistics LHC16q (ppb 5 TeV) IR ~30 khz (but 3 first khz) Phys Sel Eff ~98.5% LHC16r (ppb 8 TeV) IR varying in [100, 400] khz (but a few khz) Phys Sel Eff in [85%, 98%] depending on IR LHC16s (Pb p8 TeV) IR varying in [150, 250] khz (but a few khz) Phys Sel Eff in [82%, 98%] depending on IR LHC16t (ppb 5 TeV) IR ~30 khz Phys Sel Eff ~98.7% 02/02/17 F. Prino, C. Zampolli 12

13 Next data reconstruction plan Next data processing: Recover backlog in reconstruction of pp data samples of 2015 and 2016 Features of different periods collected in: xlsx?dl=0 Prioritization of the different pp periods based on the feedback of the PWGs and discussion in the PB Current tentative plan: Start with LHC16f (B=0.2 T) Then the other 2016 pp periods Move to 2015 giving higher priority to LHC15l (closer to 5 TeV, DCal energy calibration available) Current status: Final validation of AliDPG package which will be used instead of macros + scripts from alien 02/02/17 F. Prino, C. Zampolli 13

14 Next data processing Estimated CPU times to reconstruct the 2015 and 2016 pp data samples For each period: 4 days for QA and manual calibration after cpass1 4 days for QA after ppass 2015 CPass0+CPass1 (10K CPUs, d) Total PPass (10K CPUs, d) LHC15g LHC15h LHC15i LHC15j LHC15k LHC15l TOTAL CPass0+CPass1 (10K CPUs, d) Total PPass (10K CPUs, d) LHC16d LHC16e LHC16f LHC16g LHC16h LHC16i LHC16j LHC16m LHC16o LHC16p TOTAL /02/17 F. Prino, C. Zampolli 14

15 Next data processing Tentative plan Current estimate for completion of all periods is August 2017 Not yet considered the overlap with the processing of the new 2017 data 02/02/17 F. Prino, C. Zampolli 15

16 Monte Carlo campaign for QM All done but 1 production, postponed (AMPT for PWG-CF, code was not ready) 02/02/17 F. Prino, C. Zampolli 16

17 Next Monte Carlo productions General purpose p-pb simulations started Two cycles with two different event generators (EPOS-LHC, DPMJET) Three productions per cycle, matching the 3 data reconstructions (CENT_wSDD, CENT_woSDD, FAST) Productions anchored to 5 TeV data samples (16q) are presently in 10% QA 02/02/17 F. Prino, C. Zampolli 17

18 QA tools GOAL: develop common set of tools for online and offline QA and for software release validation Meetings: Wednesday afternoon at 2.30 pm Big progresses in the last months Details in the next talks by Jacek and Markus 02/02/17 F. Prino, C. Zampolli 18

19 AOT: Analysis Objects and Tools AOT-Event: event characterization and selection Event properties: event plane, centrality (talk by Alberica later this morning) Event selection: plans Revise the event selection for Pb-Pb and pp tuned for QM Room to improve? Special selections for high-multiplicity pp? Pileup removal/mitigation (in common with AOT-track selection) Study event selection for the p-pb samples Add them to OADB/AliEventCuts AOT-Tracks: track properties and selection Main activities: QA/documentation of track reconstruction performance Track selection for Run2 data sets Book-keeping, clean-up, maintenance of track filter bits in AODs Details later this morning in the talk by Andrea Rossi 02/02/17 F. Prino, C. Zampolli 19

20 Towards 2017 data taking: calibration needs TPC calibration For every field polarity we need low and high intensity runs before going to standard CPass Each run with at least 2 M tracks (~30 mins for pp) Detector alignment Main goal: try to remove the bias seen in the impact parameter Statistics: cosmics can be used, but also beam data are needed Central Barrel: With full ITS, TPC, TRD and TOF in the readout» Cosmics: ~50M of back-to-back triggers (C0OB3) for B+,B- and B0 each (the trigger rate w/o ITS in the trigger ~90Hz -> 1 week of 100% eff per polarity)» pp data: ~10M pp triggers at IR<20kHz for B+ and B- each. B0 is preferable but not obligatory. MUON: Cosmics with MTR trigger The alignment procedure takes ~1 month for data filtering and analysis The data collected until the new alignment is available will need a new ppass 02/02/17 F. Prino, C. Zampolli 20

21 Towards 2017 data taking: improved HLT Cluster Finder Improvements in the TPC cluster finding and data compression in the HLT for the 2017 data taking New cluster finder implemented in software FPGA implementation ongoing Tested on 2015 low-intensity data New features Improved rejection of noise clusters heavily seen in 2016, maintaining the current physics performance Performance could indeed slightly improve, because noise clusters could disturb tracking Improved compression algorithm using track model compression, data format improvement, arithmetic encoding Ongoing Additional benefits: Split cluster flag available, to be used for improved de/dx calculation HLT tracks can be used as seeds for offline, reducing memory footprint and computing time 02/02/17 F. Prino, C. Zampolli 21

22 Towards 2017 data taking: improved HLT Cluster Finder Noise cluster rejection 35% reduction in number of clusters compared to old HLT Cluster Finder Overall data size reduced by 20-25% Performance No significant difference in Nclusters/track; c 2 ; de/dx resolution and PID separation Slight improvement in TPC-ITS matching at sector edge, might be able to improve further 02/02/17 F. Prino, C. Zampolli 22

23 Summary New tools provided to the analyzers to understand and deal with the data New Twiki s summarizing the features of the reconstructed periods New Twiki on pileup under construction Guidelines for pa reconstructions included in the Twiki on data samples Data reconstruction will focus on old data samples till 2017 data taking starts QA activities will be very high need responsive and well-organized QA experts Monte Carlo is now focusing on remaining requests from last year and ppb simualtions QA tools and AOT activities cover several topics Manpower always welcome! Preparation of 2017 data taking ongoing New HLT cluster finder validation planned in detail Calibration & Alignment data Quark Matter is not the end 02/02/17 F. Prino, C. Zampolli 23

24 BACKUP 02/02/17 F. Prino, C. Zampolli 24

25 Problems spotted during data reconstruction The pidfix issue Bug in the PID hypothesis used during the last tracking step (TPCrefit) Usage of old (old gas) Bethe-Bloch parameterization + 5s cut on the TPC de/dx Effect: momentum bias and reduced matching efficiency for low-p T protons (<0.4 GeV/c) Due to wrong E-loss correction for protons tracked with wrong mass hypothesis Fixed code+ocdb used for: last group of Pb-Pb runs (LHC15o pass1_pidfix); lowir PbPb (LHC15o pass3_lowir_pidfix), pp 5 TeV (LHC15n, pass3), LHC16l and LHC16k pass1 The cookdedx issue Bug in the PID hypothesis used during the first tracking step (TPCin) The de/dx values were not passed to AliTPCseed::CookdEdx and the pion mass hypothesis was used in the first prolongation of tracks from TPC to ITS Effect: reduced matching efficiency for low-p T protons (<0.8 GeV/c) and kaons (<0.4 GeV/c) The final kinematics was instead OK because the "correct" mass was used in the refit step Fixed code used for: lowir PbPb (LHC15o pass4_lowir_pidfix_cookdedx), pp 5 TeV (LHC15n, pass3), LHC16l and LHC16k pass1 In PbPb 2015 data: Runs without ZDC (no_zdc) or miscalibrated ZDC (fix_zdc) QA for these runs done manually after updates in the physics selection code. They appear separately in the good runs lists. 02/02/17 F. Prino, C. Zampolli 25

26 DPG Material: TWiki /02/17 F. Prino, C. Zampolli 26

27 DPG Material: TWiki /02/17 F. Prino, C. Zampolli 27

28 DPG Material: TWiki /02/17 F. Prino, C. Zampolli 28

29 DPG Material: TWiki o_ pdf Any feedback on the pages is more than welcome! Please, refer to these pages, JIRA, reports for any question, and also, of course, to the experts 02/02/17 F. Prino, C. Zampolli 29

30 Packages AliDPG repository: MC part of AliDPG is stable, fully integrated in central production workflow and currently used for all the productions for QM Data processing macros and scripts added recently, currently under test Note: macros and scripts used so far in data processing are taken from alien directories QA and AOD train macros 02/02/17 F. Prino, C. Zampolli 30

31 AliDPG: Monte Carlo 02/02/17 F. Prino, C. Zampolli 31

32 AliDPG: Data processing Recently added to the package, currently under test Same macros and scripts for pp and Pb-Pb data processing 02/02/17 F. Prino, C. Zampolli 32

33 QA tools Current status Different solutions for DQM, HLT and offline QA Offline QA carried out by detector experts, each using different tools Software release validation Performance benchmarks on data and MC simulations Validation (not automatic) with reference data and MC GOAL: develop common set of tools for online and offline QA, to be used both by non experts, and by experts Easy comparison data vs MC Now in exploratory phase; 3 lines of developments currently ongoing: ROOT tree based DB (TPC QA generalization) recently complemented with Jupyter notebook (visualization) Elasticsearch (nosql database) + Kibana/Kibi (visualisation) Overwatch (EMCAL QA generalization, python+root based) with ZODB (database) + JSROOT (visualization) Extensive report in the ALICE Offline Week on November 2 ( 02/02/17 F. Prino, C. Zampolli 33

34 AOT: track characterization Book-keeping of track filter bit definition per AOD set (ongoing) TWiki already existing (maintained as service task in the past) Task developed to monitor the AOD tracks per filter bit Different option under evaluation for storing and retrieving the information about how the AOD set was created Revision, clean-up, maintenance of AOD filter bits (to be started) Need input from PWG and BTG More organized way to implement new filter bits Better testing, validation, propagation of information Track selection for Run2 data sets (started) Test and validate new cuts to remove bad tracks due to TPC SP distortions QA/documentation of track reconstruction performance (ongoing) Tasks developed for ESD and AOD tracks, will be added to central QA Systematic uncertainty on track reconstruction efficiency Track impact parameter resolution and bias Common interface for ESD and AOD track selection 02/02/17 F. Prino, C. Zampolli 34

35 TPC-ITS matching efficiency A. Barbano, C. Terrevoli TPC-ITS matching efficiency systematics Systematic uncertainty on tracking efficiency estimated from the difference in TPC-ITS matching efficiency between data and MC Affected also by different amount of secondaries in data and MC Procedure (similar to the one used in the 5 TeV spectra and RAA analyses, Extract from MC the fractions and the matching efficiencies of primary and secondary particles Extract the fractions of primary and secondary particles in data via template fits to impact parameter distributions Re-weight the MC efficiencies with the fractions of primary and secondary particles from data 02/02/17 F. Prino, C. Zampolli Data MC primaries MC secondaries MC out of the box MC reweighted p T (GeV/c) 35

36 Impact parameter resolution A. Festanti Same impact parameter resolution in highir and lowir runs Slightly worse resolution in data than MC Slightly larger data-mc discrepancy w.r.t /02/17 F. Prino, C. Zampolli 36

37 Impact parameter bias A. Festanti Impact parameter distribution not centered at 0 Larger displacement w.r.t data Data-MC discrepancy up to μm for the mean 02/02/17 F. Prino, C. Zampolli 37

38 Impact parameter bias: checks A. Festanti LHC15o lowir TPC+ITS tracks LHC15o lowir ITSsa tracks LHC15f TPC+ITS tracks 02/02/17 F. Prino, C. Zampolli Bias present in different eta/phi regions Bias present (although slightly smaller) also for ITS standalone tracks Bias present (although reduced) when excluding tracks crossing ITS modules not realigned in 2015 Smaller bias in LHC15f Detector alignment changed? Difference between pp and Pb-Pb? Check LHC15n PWGs should evaluate the effect of this bias in their analyses 38

39 PID issues during the tracking pidfix: Problem with dedx parameterization used to assign the PID in the tracking + number of sigma used to cut on the signal pion mass hypothesis used for higher mass particles effects are relevant for protons with pt<0.4 GeV/c, kaons with pt<0.3 GeV/c and nuclei Affected datasets: 2/3 HI LHC15o ( pass1 ), LHC15n, pass2 LI LHC15o See Talk at the Physics Forum on 5.10 cookdedx: Missing identification during the TPCin early step of the reconstruction (due to missing TPC clusters) pion mass hypothesis always used it affect kaons with pt<0.4 GeV/c and protons with pt<0.8 GeV/c Affected datasets: HI LHC15o ( pass1, and pass1_pidfix ), LHC15n, pass3 LI LHC15o 02/02/17 F. Prino, C. Zampolli 39

40 Matching rate cookdedx: data vs MC R. Shahoyan Data MC 02/02/17 F. Prino, C. Zampolli 40

41 Matching rate cookdedx: data vs MC R. Shahoyan Data MC Compatible behaviour in data and MC Absolute difference in match eff between data and MC comes from the pile-up in LHC16l and higher secondaries contamination in data More studies started using the low IR sample from LHC15o (reconstructed both with and without the fix, with corresponding MC) 02/02/17 F. Prino, C. Zampolli 41

42 Large extrapolation error at low p T R. Shahoyan Symptom: small ITS-TPC matching efficiency at low pt , pass2_lowir , pass3_lowir_pidfix p T p T Difference: in pass3 fluctuations are accounted by extra errors added to clusters and additional covariance matrix accounting for errors correlation At low p T the inconsistency in correlated error matrix building wrt original formula leads to too large extrapolated error of the track and its rejection by a fiducial cut in ITS 02/02/17 F. Prino, C. Zampolli 42

43 Large extrapolation error at low p T R. Shahoyan Symptom: small ITS-TPC matching efficiency at low pt 0.8 Match eff vs phi , pass2_lowir , pass3_lowir_pidfix 0.55 p T p T , pass2_lowir , pass3_lowir_pidfix Difference: in pass3 fluctuations are accounted by extra errors added to clusters and additional covariance matrix accounting for errors correlation Effect is particularly bad in run due to strongly overestimated distortions fluctuations error 02/02/17 F. Prino, C. Zampolli 43

44 Large extrapolation error at low p T R. Shahoyan Symptom: small ITS-TPC matching efficiency at low pt , pass2_lowir , pass3_lowir_pidfix Reason of inconsistency: the method used Kalman fit matrix instead of LLS matrix used in derivation: their difference is too large at low low p T Fixed in aliroot since last week Since many samples have been reconstructed with this feature, important to verify data vs MC comparison Effect becomes significant at top IR (~7.5 khz) around distortion hot spots, where the distortion fluctuation error becomes very large (up to 1 cm) 02/02/17 F. Prino, C. Zampolli 44

45 R. Shahoyan Large extrapolation error at low p T : data vs MC Few percent difference data vs MC as known from secondaries The assigned TPC track errors are well reproduced in the MC, hence the effect of the Invariant cut on matching rate is also reproduced. 02/02/17 F. Prino, C. Zampolli 45

46 Large extrapolation error at low p T : impact on data LHC15n (pp, 5 TeV): no bad run found LHC15o: one bad run found: (low IR, 12K kint7 triggers) In total we have 4.7M kint7 in the low IR sample Other (~25) runs with drop by 5-6% in match eff at low p T on the C- side, not marked as bad since this is reproduced in MC (see before) LHC16l: 8 bad runs found: , , , , , (SSD C-side off, 3.2M total events, ~1.7M kint7), , (2 SSD DDLs not readout, 2.8M total events, 477K kint7)» In total we have: 323M total events, 54M kint7 LHC16k: no bad run found 02/02/17 F. Prino, C. Zampolli 46

47 LHC15o, PbPb, 5 TeV Statistics of golden runs (CentralBarrel) Full ITS, TPC, V0, ZDC pass N. Run (tot) N. Ev (tot) (M) N. Ev with TPC (tot) (M) Golden runs (corresponding total without selection - figures in parenthesis) N. kint7 (tot) (M) Anchored Gen Purp MC pass2_lowir 12 (12) 9 (9) 8 (8) 5 (5) LHC16h8(a, b) pass3_lowir_pi dfix pass4_lowir_pi dfix_cookdedx 11 (12) 9 (9) 8 (8) 5 (5) LHC16g1(a, b, c) 11 (12) 9 (9) 8 (8) 5 (5) LHC16j7(a, b) pass1 78 (108) 299 (406) 111 (154) 71 (100) LHC16g1(a, b, c) pass1_pidfix 6 (42) 15 (140) 6 (55) 5 (44) LHC16g1(a, b, c) Golden runs, no ZDC (corresponding total without selection - figures in parenthesis) lowir (all passes) 1 (1) 0.1 (0.1) 0.1 (0.1) 0.1 (0.1) LHC16g1(a, b, c), LHC16j7(a,b) pass1 4 (8) 17 (27) 6 (9) 4 (7) LHC16g1(a, b, c) pass1_pidfix 1 (1) 0.2 (0.2) 0.2 (0.2) 0.2 (0.2) LHC16g1(a, b, c) Golden runs, fix ZDC (corresponding total without selection - figures in parenthesis) pass1 3 (9) 13 (34) 6 (14) 5 (12) LHC16g1(a, b, c) 02/02/17 F. Prino, C. Zampolli 47

48 LHC15o, PbPb, 5 TeV Statistics of runs w/o full TPC acceptance (CentralBarrel) pass N. Run (tot) N. Ev (tot) (M) N. Ev with TPC (tot) (M) N. kint7 (tot) (M) Not full TPC acceptance (corresponding total without selection - figures in parenthesis) Anchored Gen Purp MC pass1 6 (108) 14 (406) 6 (154) 5 (100) LHC16g1(a, b, c) pass1_pidfix 33 (42) 120 (140) 46 (55) 38 (44) LHC16g1(a, b, c) Not Full TPC acceptance, no ZDC (corresponding total without selection - figures in parenthesis) Not Full TPC acceptance, fix ZDC (corresponding total without selection - figures in parenthesis) pass1 6 (9) 21 (34) 8 (14) 6 (12) LHC16g1(a, b, c) N.B.: 1. The runs with Not Full TPC Acceptance are excluded by definition in the Golden runs lists 2. The Golden run lists are defined asking for full ITS, V0, ZDC, TPC 3. Only runs with full ITS, TPC, TRD in data taking are considered (in tot numbers) 02/02/17 F. Prino, C. Zampolli 48

49 Statistics of golden runs (CentralBarrel) pass N. Run (tot) N. Ev (tot) (M) N. Ev with TPC (tot) (M) Golden runs (total in parenthesis) N. kint7 (tot) (M) Anchored Gen Purp MC pass2 27 (27) 187 (187) 138 (138) 13 (13) n/a LHC15n, pp, 5 TeV 02/02/17 F. Prino, C. Zampolli 49

50 Match eff vs phi Match eff vs pt Reco with vs without SDD (CENT events) Ad hoc reco done, output at /alice/cern.ch/user/p/pwg_pp/lhc16q/ /pass1 ITS refit ITS refit + SPD any Eta > 0 (A side) Black vs blue: ~5% difference Black vs blue: ~2% difference withsdd w/osdd Strong dependence on ITS status Black vs blue: ~2% difference Black vs blue: ~0% difference 17/11/16 F. Prino, C. Zampolli 50

51 Match eff vs phi Match eff vs pt Reco with vs without SDD (CENT events) Ad hoc reco done, output at /alice/cern.ch/user/p/pwg_pp/lhc16q/ /pass1 ITS refit ITS refit + SPD any Eta < 0 (C side) Black vs blue: ~5% difference Black vs blue: ~2% difference withsdd w/osdd Strong dependence on ITS status Black vs blue: ~2% difference Black vs blue: ~0% difference 17/11/16 51

52 Reco with vs without SDD (CENT events) Ad hoc reco done, output at /alice/cern.ch/user/p/pwg_pp/lhc16q/ /pass1 Eta > 0 (A side) Eta < 0 (C side) ITS refit, no TOF bc ITS refit, TOF bc SPD any, no TOF bc 17/11/16 52 SPD any, TOF bc

53 Without SDD With SDD Reco with vs without SDD (CENT events) Ad hoc reco done, output at /alice/cern.ch/user/p/pwg_pp/lhc16q/ /pass1 Λ, all pt antiλ, all pt No selection on the position of the Λ decay vertex Mean = Sigma = 1.38 Mean = Sigma = 1.37 Mean = Sigma = 1.57 Mean = Sigma = /11/16 53

54 Reco with vs without SDD (CENT events) Ad hoc reco done, output at /alice/cern.ch/user/p/pwg_pp/lhc16q/ /pass1 Λ, all pt - With SDD - Intervals of radius of Λ decay vertex Sigma = 1.38 Sigma = 1.37 Sigma = 1.32 Sigma = /11/16 54

55 Reco with vs without SDD (CENT events) Ad hoc reco done, output at /alice/cern.ch/user/p/pwg_pp/lhc16q/ /pass1 Λ, all pt - Without SDD - Intervals of radius of Λ decay vertex Sigma = 1.43 Sigma = 1.51 Sigma = 1.69 Sigma = /11/16 55

56 Reco with vs without SDD (CENT events) Ad hoc reco done, output at /alice/cern.ch/user/p/pwg_pp/lhc16q/ /pass1 Eta > 0 (A side) Eta < 0 (C side) 3pi/4 < phi < 5pi/4 with SDD without SDD 56

57 Physics Selection in LHC16k Efficiency Interaction Rate 02/02/17 F. Prino, C. Zampolli 57

58 Physics Selection in LHC16r Efficiency Interaction Rate 02/02/17 F. Prino, C. Zampolli 58

59 Physics Selection in LHC16q Efficiency Interaction Rate 02/02/17 F. Prino, C. Zampolli 59