Update of the Fiducial calibration study in 2km WC detector 2km pre-meeting 7 July, 2006 G. Mitsuka T2K-2km working group

Contents Motivation Fiducial PMT geometry Criteria to identify the vertex in/out FV Fiducial PMT simulation in 2KM Water Cherenkov detector Summary (From now on, Fiducial PMT is shortened to FVPMT)

Motivation  Need to implement new fiducial calibration system In the WC detector, fiducial volume is expected as one of the large systematic errors (difficult to calibrate fiducial volume)  Need to implement new fiducial calibration system An idea of FV calibration Set the array of PMTs(FVPMT) around FV boundary Vertex is identified as in/out by FVPMT signals In order to check whether FV calibration will works well or not Performance test of the candidate PMT for FVPMT(2inch PMT : Hamamatsu R9869) is carried out in the 1kton detector  R9869 fulfills requirements Estimate the efficiency of FV calibration and error by using 2km simulation with FVPMTs Update

How to set the array of FVPMT Requirements Avoid the effect of shadow of FVPMT Identify the FV boundary with high resolution Solutions 2inch PMTs are set with a distance of 50cm FVPMT is set on the FV boundary R9869 with water proof

FVPMT geometry Z direction R direction (Top,Bottom) IFPMT 50cm OFPMT FV boundary IFPMT 50cm 50cm OFPMT Fiducial volume Inward-Facing PMT = IFPMT Outward-Facing PMT = OFPMT Front & End-cap 104PMTs + Barrel 360PMTs = Total 464PMTs

Criteria for in/out identification(1) FVout event L Vertex is identified as FVin/out by FVPMT hits  FVout requires OFPMT hits  FVin requires IFPMT hits Hit due to scattering or reflection is cut by PMT-facing direction and m direction OFPMT Hit FVin event OFPMT No-hit FV boundary IFPMT Hit

Estimation of efficiency and error Estimate the FV calibration efficiency and error by using 2km WC simulation with FVPMTs Neutrino events are simulated with taking account of the neutrino flux at 2km position These events are independently reconstructed of FVPMT by 2km official reconstruction software Sample events are 1-ring m-like event (~170000)

Event reconstruction with FVPMT Reconstruction performance seems to be good even with FVPMT

FV identification efficiency (Z dir) ID as FVin(ID) ID as FVout(ID) X,Y,Z is fitted position Events in R(=sqrt(X2+Y2)) < 150cm are selected  except for less efficient region (FV barrel edge) Efficiency curve shows sharp shift at FV boundary

FV identification efficiency (R dir) Events in -300<Z<100cm are selected Efficiency curve shows sharp shift at FV boundary ID as FVin(ID) ID as FVout(ID) Efficiency summary FVin(fit) FVout(fit) FVin(ID) 83.2% 16.8% FVout(ID) 17.6% 82.4%

Additional study FV calibration efficiency and error with 464’s FVPMT were estimated  predict that FV calibration works well For the cost reason, the number of FVPMTs is required to be much small  search for the alternative configuration Assuming the half number of FVPMTs, check the efficiency and error

How to decrease FVPMT FVPMTs set on the endcap and frontcap are very important to calibrate the FV edge, because m of CCQE interaction are almost forward scattered Try to remove barrel FVPMTs ( next page) zbs and ntuple files are tentatively same as full FVPMT version, skip barrel FVPMT when reading ntuple

What FVPMT is skipped Type 1 Type 2 Full464 – skip240 = 224 PMTs used used PMT skipped PMT Type 1 Type 2 Y(cm) Y(cm) Z(cm) Z(cm) Full464 – skip240 = 224 PMTs used 464 – 210 = 254 PMTs used

Calibration efficiency (Zdir:(x2+y2)1/2<150cm) FVin(ID) original / modified FVout(ID) original / modified Type 1 Type 2

Calibration efficiency (Rdir:-300<z<100cm) FVin(ID) original / modified FVout(ID) original / modified Type 1 Type 2 Worse efficiency in/out FV Same level inside FV, worse outside FV

Efficiency as a function of En FV out (full) FV in (full) FV out (type2) FV in (type2) Efficiency 80% of all events En(MeV)

Efficiency summary Black : Full FVPMT Blue : type1 Red : type2 FVin (id) FVout (id) FVin (true) 83.2 / 77.5 / 80.9 16.8 / 22.6 / 19.1 FVout (true) 17.7 / 17.8 / 18.8 82.4 / 82.2 / 81.2 (unit is %) type2 keeps 81% efficiency inside FV, although type1 is 78% According to the efficiency curve, type2 could be realistic

Shadow effects of FVPMT (1) (w/o FVPMT is normalized by the number of events in FV) Ring-counting PID w/o FVPMT w/ FVPMT w/o FVPMT w/ FVPMT Events Events Excess appeared, but not affect Single-ring Likelihood Likelihood According to the comparison of Ring-counting and PID, FVPMT doesn’t seem to affect 1-ring m-like events

Shadow effects of FVPMT (2) Hatched type requires identification as FVin Efficiency of QE,Non-QE and NC in 1-ring m-like events Type w/o FVPMT True Full type1 type2 QE 75.6 71.5 72.6 71.1 71.8 Non-QE 22.9 24.1 23.3 24.8 NC 1.5 4.3 4.0 4.1 (unit is %) Efficiency of miss-PID in 1-ring m-like QE events Type w/o FVPMT True Full type1 type2 miss-PID 1.02 0.80 0.68 0.72 0.71 (unit is %)

Error of efficiency In all types, error is smaller than 1% e : Efficiency of FV identification Real data MC Table of errors of MC efficiency Type Full type1 type2 Q(+/-30%), Nhit(+/-10%) 0.1% 0.6% 0.4% Non-QE(+/-20%), NC(+/-30%) 0.2% Ring-counting(+/-5%) <0.1% PID(+/-4%) Energy-scale Total 0.3% 0.7% In all types, error is smaller than 1%

Summary Checked the efficiency and error by using fitted 2km MC  predict FV calibration works well Vertex of 1-ring m-like events are correctly identified with efficiency > 80% Error of identification efficiency is less than 0.5% Searched for alternative FVPMT setup  Even if the half number of FVPMTs, keep the efficiency of 80%

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miss-identified event FVin events miss-identified as FVout Outwrd-going event whose vertex is near from FV boundary  OFPMT hit around exit point LowE event whose vertex is almost center of FV  Neither PMT hits FVout events miss-identified as FVin Inward-going event whose vertex is near from FV boundary  IFPMT hits, but OFPMT no-hit

Slope of Efficiency curve in FV q1 q1 + q2 = p/2 q1 > q2 q2 OFPMT hits due to scattering or reflection  identified as FVout event cos(q2) is generally large  Hit is ignored by angle cut cos(q2) is generally small  Hit is taken into account in the calibration criteria Z

Systematic error of FV DATA MC FV syst. error

Introduction Reconstruction is applied for generated MC~170000events(shown at last meeting) FV identification criteria is improved Estimate the error of FV calibration

Summary Improved criteria is applied for fitted MC 　Events are correctly identified with efficiency > 80% 　Error of identification efficiency is 0.7% In preparation for the commitment of improved routines Studying the configuration using lower number of PMTs as keeping efficiency and error

Summary Checked the efficiency and error by assuming the half number of FVPMTs type2 keeps 81% efficiency in/out FV Error of efficiency is less than 0.6% in all geometries type2 could be realistic geometry

Error of Identification efficiency Systematic error of FV NinID : #events in FV e : Efficiency of FV identification Real data MC Error of identification efficiency 　Calibration system (Q:+/-20%,Nhit:+/-10%)  0.07% 　Neutrino interaction (nonQE:+/-20%,NC: +/-30%)  0.33% 　Reconstruction (PID:+/-4%,RingCounting:+/-5%)  0.33% 　Energy scale (+/-2%)  0.11% Error of efficiency = 0.48%

Error of efficiency NinID : #events in FV e : Efficiency of FV identification Real data MC Table of errors of MC efficiency Type Full type1 type2 Q(+/-30%), Nhit(+/-10%) 0.07% 0.49% 0.32% Non-QE(+/-20%), NC(+/-30%) 0.33% 0.20% 0.11% Ring-counting(+/-5%) <0.01% 0.12% PID(+/-4%) 0.02% 0.01% Energy-scale 0.19% 0.17% Total 0.48% 0.57% 0.40%

Criteria for in/out identification(1) VTX-Cross point = LVC Fitted vertex Cross point FVout event FVout event L OFPMT Hits IFPMT OFPMT Hit L - LVC FVin event IFPMT FVin event Hits OFPMT No-hit FV boundary IFPMT Hit L - LVC If OFPMT hit (L-Lvc < 0)  ID as FVout else if IFPMT hit != 0  ID as FVin else(i.e. No-IFPMT hit)  ID as FVout

Criteria for in/out identification(2) Merit of FVPMT facing both sides Backward extrapolated cross point exists && VTX-Corss(forward) > VTX-Cross(Backward)  Hits around backward cross point are taken into account in order to correct miss-fitted event miss-fitted FVout event OFPMT Hit miss-fitted FVout event True vertex(★) is FVout Fitted vertex(●) is FV in  OFPMT hit can correct FVin to FVout miss-fitted FVin event True vertex(★) is FVin Fitted vertex(●) is FVout  OFPMT hit can correct FVin to FVout miss-fitted FVin event IFPMT Hit

Criteria for in/out identification(3) Hits by scattering or reflection OFPMT hits due to the scattering or reflection  Identified as FVout by these OFPMT hits In order to avoid such miss-ID cosq(PMT dir~particle dir) > 0.8  ignore hit q OFPMT Hit

Ring counting likelihood (check) Full Type1 Type2 Efficiency dlfct