Parameterization of EMC response for low energy p m e Marianna Testa for CP + charged kaon WG KLOE general meeting 24-05-2003

Introduction PID with EMC (TOF, energy deposit) important for several analyses KL->pln, K->pln, KL->p+p-, KS->pe(m?)n…. calorimeter response for p m (e) not reliable in MC need for parameterization from data clustering efficiency energy related variable (see later) TOF (should be OK with new MC)

data sample Kl3, KL-> p+ p- p0 (both with KS-> p+p-) using 2002 data selection: kinematics and TOF cuts K-> p  p0 , K -> m  n selection: easy kinematics cuts

Clustering efficiency Defined as  = N extrap: # particles whose track has been extrapolated on the EMC N cluster: # particles extrapolated with a cluster in the EMC associated Extrapolation well reproduced by MC N extrap =the normalization sample The clustering efficiency for e, p, m is parameterized with respect to the momentum of the particle and the impact angle on the EMC

Cuts used for the PID KL->pen: kinematics cuts on the value of the variable ‘lepton mass’ calculated in the hypothesis of positive or negative track in a decay with a pion and a massless particle - KL->p+ e- n KL->p- e+ n Contamination<1%

difficult kinematics cuts on the variable ‘lepton mass’ KL->pmn: difficult kinematics cuts on the variable ‘lepton mass’ need for TOF identification of the associated p (TOF>-0.5 ns) contamination  5.5% (<1% if TOF from m is used: calo. response) TOF distribution KL->p-m+ n background - KL->p+ m- n KL->p- m+ n Dt(ns)

KL->p+p-p0: kinematics cuts on the value of. the missing mass (Mm) KL->p+p-p0: kinematics cuts on the value of the missing mass (Mm) |Mm-mp0|< 4 MeV contamination  0.8% missing mass distribution

Clustering efficiency Electrons: Efficiency dependence most on the impact angle Muons: Stronger correlation between momentum and impact angle

Pions from KL->pen and KL->p+p-p0 Different e for p- and p+ at low energies e versus momentum e m p p- p+

Data MC comparison m - MC sample  7pb-1 MC response edata/emc vs momentum edata/emc vs momentum m - e- edata/emc vs momentum MC sample  7pb-1 MC response not well reproduced at low energies Wait for new MC p-

PID with calorimeter full calorimeter response parameterization almost impossible Use few significant variables, keep correlations Total energy (E), 3d cluster centroid (xbar) Parameterization in 10 MeV bins in Momentum (P) Use Neural net both for parameterization and PID

NN basic Multi-Layer Perceptrons: Input quantities are processed through successive layers of "neurons" IN I1 I2 N input 1st layer N1 "neurons" X1= C01+S Ci1 Ii; Y11= 1/(1+e-x1) nth layer O1= F01+S Fi1 Yni

Results: electrons at [140, 150] MeV 2 - 15 - 2 – 1 MLP Input # neu 1st 2nd layer output Integrated 1d distribution 2d distribution of the xbar and the ratio P/E P/E distribution xbar distribution xbar P/E

Results: p+ at [140, 150] MeV P/E distribution xbar distribution xbar

Results: m+ at [140, 150] MeV P/E distribution xbar distribution xbar

Xbar (cm) p+ vs e [90-230] MeV P/E

Xbar (cm) m+ vs p+ [100-250] MeV P/E

Results: p + e+ separation at 200 MeV 2 - 5 - 10 – 1 MLP Input # neu 1st 2nd layer output e purity p Efficiency NN output

Results: p + m+ separation at 200 MeV purity p Efficiency NN output

Conclusions Parameterization of EMC response for low energy p m e well advanced Clustering Efficiency: data almost done MC need more MC statistics and new MC PID: parameterization done (to be included in KCP-KPM lib) good results for p/e and p/m separation