MiniBooNE Event Reconstruction and Particle Identification

Transcription

1 MnBooNE Event Recontructon and Partcle Identfcaton Ha-Jun Yang Unverty of Mchgan, Ann Arbor (for the MnBooNE Collaboraton) DNP06, Nahvlle, TN October 25-28, 2006

2 Outlne Phyc Motvaton MnBooNE Event Type Event Recontructon Partcle Identfcaton Summary 10/26/2006 DNP06, H.J.Yang, MnBooNE PID 2

3 Phyc Motvaton LSND oberved a potve gnal, but not confrmed. L m P( e ) n ( )n ( Δ νμ ν = 2 2θ 2 ) = ( ± ± )% E The MnBooNE degned to confrm or refute LSND ocllaton reult at Δm 2 ~ 1.0 ev 2. 10/26/2006 DNP06, H.J.Yang, MnBooNE PID 3

4 MnBooNE Flux 8 GeV proton on Be target gve: p + Be π +, K +, K 0 L ν μ from: π + μ + ν μ K + μ + ν μ K 0 L π - μ + ν μ Intrnc ν e from: μ + e + ν e ν μ K + π 0 e + ν e K 0 L π - e + ν e The ntrnc ν e ~0.5% of the neutrno Flux, t one of major background for ν μ ν e earch. L(m), E(MeV), Δm 2 (ev 2 ) 10/26/2006 DNP06, H.J.Yang, MnBooNE PID 4

5 Event Topology 10/26/2006 DNP06, H.J.Yang, MnBooNE PID 5

6 Event Recontructon To recontruct event poton, drecton, tme, energy and nvarant ma etc. Cerenkov lght prompt, drectonal Scntllaton lght delayed, otropc Ung tme lkelhood and charge lkelhood method to determne the optmal event parameter. Two parallel recontructon package S-Ftter baed on a mple, pont-lke lght ource model; P-Ftter dffer from S-Ftter by ung more 0 th approxmaton tre, addng e/μ track wth longtudnally varyng lght ource term, wavelength-dependent lght propagaton and detecton, non-pont-lke PMT and photon catterng, fluorecence and reflecton. 10/26/2006 DNP06, H.J.Yang, MnBooNE PID 6

7 Recontructon Performance Mchel Electron 10/26/2006 DNP06, H.J.Yang, MnBooNE PID 7

8 Partcle Identfcaton Two complementary and parallel method: Log-lkelhood technque: mple to undertand, wdely ued n HEP data analy but le entve Booted Decon Tree: Non-lnear combnaton of nput varable Great performance for large number of nput varable (about two hundred varable) Powerful and table by combnng many decon tree to make a majorty vote 10/26/2006 DNP06, H.J.Yang, MnBooNE PID 8

9 Booted Decon Tree How to buld a decon tree? For each node, try to fnd the bet varable and plttng pont whch gve the bet eparaton baed on Gn ndex. Gn_node = Weght_total*P*(1-P), P weghted purty Crteron = Gn_father Gn_left_on Gn_rght_on Varable elected a pltter by maxmzng the crteron. How to boot the decon tree? Weght of mclafed event n current tree are ncreaed, the next tree bult ung the ame event but wth new weght, Typcally, one may buld few hundred to thouand tree. How to calculate the event core? For a gven event, f t land on the gnal leaf n one tree, t gven a core of 1, otherwe, -1. The um (probably weghted) of core from all tree the fnal core of the event. 10/26/2006 DNP06, H.J.Yang, MnBooNE PID 9

10 Performance v Number of Tree The advantage of ung booted decon tree that t combne many decon tree, weak clafer, to make a powerful clafer. The performance of booted decon tree table after a few hundred tree teraton. Booted decon tree focu on the mclafed event whch uually have hgh weght after hundred of tree teraton. An ndvdual tree ha a very weak dcrmnatng power; the weghted mclafed event rate err m about Ref1: H.J.Yang,, B.P. Roe, J. Zhu, Stude of Booted Decon Tree for MnBooNE Partcle Identfcaton, Phyc/ , Nucl. Intum.. & Meth.. A 555(2005) Ref2: B.P. Roe, H.J. Yang, J. Zhu, Y. Lu, I. Stancu,, G. McGregor, Booted decon tree a an alternatve to artfcal neural network for partcle dentfcaton,, phyc/ , NIMA 543 (2005) /26/2006 DNP06, H.J.Yang, MnBooNE PID 10

11 Output of Booted Decon Tree Oc ν e CCQE v All Background MC v ν μ Data 10/26/2006 DNP06, H.J.Yang, MnBooNE PID 11

12 Summary MnBooNE Event Recontructon Poton reoluton ~ 23 cm Drecton reoluton ~ 6 o Energy reoluton ~ 15% Recontructed π 0 ma reoluton ~ 20 MeV/c 2 MnBooNE Partcle Identfcaton For 0.1% μ eff., ~ 90% electron eff. For 1% π 0 eff., ~ 70% electron eff. For 0.5% all background eff., ~ 80% electron eff. MnBooNE Reult are comng oon 10/26/2006 DNP06, H.J.Yang, MnBooNE PID 12

13 Backup Slde 10/26/2006 DNP06, H.J.Yang, MnBooNE PID 13

14 Lght Model Cerenkov lght - drectonal CER r λcer μ = ρε F(co ϑ, E) f (co η) exp( / ) 2 r Scntllaton lght - otopc SCI r λc = f (co ) exp( / ) 2 r Predcted charge μ ϕε η μ = μ + μ CER SCI 1. Cerenkov angular dtrbuton - F(coϑ) 2. PMT angular repone f(coη) 3. Cerenkov attenuaton length λcer 4. Scntllaton attenuaton length - λc 5. Relatve quantum effcency - ε 6. Cerenkov lght trength - ρ Iotropc Scntlaton lght φ Drectonal Cherenkov lght ρ θc (x y z t) (ux uy uz) r η Pont-lke lght ource model f(coη) Scntllaton lght trength - ϕ /26/2006 DNP06, H.J.Yang, MnBooNE PID 14 coη

15 1. Corrected tme () t = t t corr 2. Cerenkov lght t corr () dtrbuton Lght Model 0 r c n T cer ( t ) corr = 1 E E t t E corr c ( c, ) exp 1 ( c, ) [ 2 (, )] μ πσ μ σ μ 2 3. Scntllaton lght t corr () dtrbuton T c ( t ) corr 2 1 E tcorr t E (, E) exp σ ( μ, ) (, ) 0 μ = 2 2τμ 2τ ( μ, E) τμ (, E) σμ (, E) tcorr t (, E) 0 μ exp Erfc 2τμ (, E) 2τμ (, E) 4. Input: Cerenkov lght t 0 cer,σ cer Scntllaton lght t 0 c,σ c,τ c Ht/1 n 6 10 Monte Carlo mulaton Data 5. Total negatve log tme lkelhood μc μ Lt ( () corr ) = log( Tcer ( t () corr, μc ) + Tc ( t () corr, μ )) μ + μ μ + μ c c Corrected Tme (n)