charged kaon lifetime : preliminary result

Paolo Massarotti Charged Kaon Meeting 6 dicember 2005

Charged kaon lifetime: preliminary result

P. Massarotti


Summary:

Events selection

Reconstruction efficiency

Resolution function

measurement

Conclusions


measurement at KLOE

large statistics

good resolutions

kaon decays on flight

measurements of and


The Particle Data Group measures are not in agreement

: experimental picture

= (12.385±0.024)ns


K tag

K

Kvtx

Li

Signal selectionSignal selection

• Self triggering muon tag• K track on the signal side• Decay vertex

Signal K extrapolated to the IP. dE/dx

correction applied along the path.

Li = step length

Vertex efficiency and resolution functions needed Vertex efficiency and resolution functions needed wrt the proper time of the Kaonwrt the proper time of the Kaon

Strategy

T


Tag effTrg eff

Why self-triggering tag?Trg and Tag (without self trg request) efficiency as a function of the charged kaon kine proper time

Both the efficiencies have a slope


Tag self trg

Why self-triggering tag?Self Trg tag efficiency as a function of the charged kaon kine proper time

Tag self trg

The self trg tag efficiency is constant in the region between 10 and 35 ns


Events selection and

analysis background

Proper time distribution

Background analisys

Resolution functions for the

background families

Background rejection

Proper time distribution after the cut


Proper time distribution

We evaluate on MonteCarlo this effect

The main distortion of the slope comes from the badly reconstructed Kaon vertices


Background analysisFive families:

“golden”: good vertex (~ 69.1%)

kaon hits associated to daughter track (~ 20.6%)

daughter hits associated to kaon track (~ 5.2%)

early pion decay (~ 1.4%)

K± broken track(~3.7%)


Families proper time resolutions kaon hits associated

to daughter track

early pion decay

K± broken track

good track All (but one) the dist are centered within 200 ps

Mean: -7.6 ns

daughter hits associated to kaon track

Cut at 100 MeV

Daughter P*with kaon mass

hypothesis

MeV/c

Kaon broken tracks


Definition of signal sample:

• Cut at 100 MeV/c on P* with 94% efficiency

• 73% golden and 27% other families ( worse resolution)

Broken tracks:less than 1%

Measurement region


Reconstruction Efficiency: Neutral vertex technique

Considering only kaon decays with a

X X

We look for the vertex asking

clusters on time: (t - r/c) = (t – r/c)

invariant mass

agreement between kaon flight time and clusters time

0

E,t,x

±

E,x,t

K tagt

pK

pKt0

lK

xK

E,t,x


Efficiency measurements

GTr KTr secV kaon

chargeddaughter1

2

•

Nvn = Neutral vertex sample

Nvn&vc = neutral and charged vertex sample

Nvn&TrK = tracked K given neutral vertex sample

NTrK&nv = neutral vertex given tracked K sample


Reconstruction efficiency

Global vertex efficiency

Tracking efficiency

Vertex efficiency given the kaon track

Methods comparison


Global efficency comparisonNormalization sample: tagged events with a neutral vertex in

the signal emisphere with the cut:

MC recoMC kine

(40 < <150) cm , | z | < 150 cm


Global efficency comparisonMonteCarlo kine vs MonteCarlo reco large fit window

aG = (94.3 ) x10-2

bG = (-.13 ) x10-3

aG = (94.4 ) x10-2

bG = (-.07 ) x10-3

between 12 and 45 ns between 15 and 35 ns


Global efficency comparison:

normalization sample X for kine efficiency

aG = (99.8 ) x10-2

bG = (.37) x10-3

If we normalize the kine efficiency using only the

events X

also absolute normalization is in perfect agreement.

The difference is given by the

characterized by high momentum

secondary tracks


Data and MonteCarlo compared

reco MCData

The difference between Data and datalike MonteCarlo is due to

an imperfect simulation of the correlated background: kaons produce in the first

12 layers of the D.C. adjacent spurious hits along their path,

spoiling the tracking performance


Method 2: tracking efficencyMonteCarlo kine vs MonteCarlo reco large fit window

MC recoMC kineMC recoMC kine

MC recoMC kine

More than 1 meter of track


Method 2: tracking efficencyMonteCarlo kine vs MonteCarlo reco large fit window

aTRK = (100.0 ) x10-2

bTRK = (-.12 ) x10-4 aTRK = (100.2) x10-2

bTRK = (-.15 ) x10-3




reco MCData

The difference between Data and datalike MonteCarlo is due to

an imperfect simulation of the correlated background: kaons produce in the first

12 layers of the D.C. adjacent spurious hits along their path,

spoiling the tracking performance


MonteCarlo kine vs MonteCarlo reco large fit window

Method 2: vertexing efficency

MC recoMC kine

MC recoMC kine


MonteCarlo kine vs MonteCarlo reco larger fit window

Method 2: vertexing efficency

aVTX = (94.2 ) x10-2

bVTX = (-.35 ) x10-3 aVTX = (93.7 ) x10-2

bVTX = (-.48 ) x10-3




reco MCData

reco MCData

Good agreement


Two methods compared:MC

aConf = (100.2 ) x10-

2

bConf = (.1 ) x10-3

global product

aconf = (100. ) x10-2

bconf = (.19) x10-3


global product

aconf = (100.9 ) x10-2

bconf = (.37) x10-3

Two methods compared: Data

aconf = (101. ) x10-2

bconf = (.40) x10-3


Resolution functions vs lifetime fit Resolution function evaluated from the

MonteCarlo simulation as a function of the

charged kaon proper time

Point of Closest Approach (PCA) techinque

Validation of thePCA techinque

After PCA method validation the resolution

function evaluated with closest approach

technique on MonteCarlo and Data


Resolution functions: MCFit to the charged kaon proper time using a convolution

of an exponential function and a resolution function. But also the resolution function is a function of the

proper time

Resolution evaluated with the MonteCarlo

simulation:

Treco – Tkine.

Needed a method

that can be applied to Data



Point of Closest Approach method

We search the point of closest approach between the last hit of the kaon track and the first hit of the charged decay particle track

reso X reso Y

reso Px reso Pz

reso Z

reso Py

ALL centered -> no bias on lifetime slope


Data

PCA proper time resolution: comparison Data-MC

Treco – TPCA

reco MCDatareco MC


Fit procedure

We make the fit in the region between 15 and 35 ns.To fit the proper time distribution we construct an histogram ,

expected histo, between 12 and 45 ns, in a region larger than the actual fit region to take into account border effects.

The number of entries in each bin is given by the integral of the exponential decay function, which depends on one parameter only,

the lifetime, convoluted with the efficiency curve. Then a smearing matrix accounts for the effects of the resolution.

We also take into account the tiny correction to be applied to the efficiency given by the ratio of the MonteCarlo datalike and

MonteCarlo kine efficiencies.

Nexpj = Csmear

ij × i × icorr × Ni

theo i = 1

nbins


MC measurement:

+MC = (12.403 ± 0.079) ns 2 =1.13 P2 =32.6%

Best value between 15 and 28 ns

° MC reco• Fit


MC measurement:

+MC = (12.403 ± 0.079) ns

Fit region between 15 and 28 ns

° MC reco• Fit

The fit reproduce the dist. also well ouside the fit

region !


MC residual evaluation

Cons+MC = -0.03 ± 0.16 P2 = 49.8%


-MC = (12.399 ± 0.079) ns 2 = .93 P2 = 52.2

MC measurement


° MC reco• Fit


-MC = (12.399 ± 0.079) ns

MC measurement


° MC reco• Fit


region !


MC residual evaluation

Cons+MC = 0.02 ± 0.09 P2 = 59.8%


+Data = (12.337 ± 0.066) ns 2 = 1.18 P2 = 28.4%

Data measurement

Fit between 16 and 30 ns

° Data• Fit


+Data = (12.337 ± 0.066) ns

Data measurement


° Data• Fit


region !


Data residual evaluation

Cons+Data = -0.005 ± 0.035 P2 = 56.2%


-Data = (12.388 ± 0.058) ns 2 = 1.1 P2 = 35.2%

Between 17 and 31 ns

Data measurement

° Data• Fit


-Data = (12.388 ± 0.058) ns


Data measurement

° Data• Fit


region !


Data measure

Cons-Data = 0.00 ± 0.18 P2 = 49.1%


Preliminary systematics check

Fit stability as a function of the range used - done

Fit stability as a function of the bin size - done

Fit stability with or without of the efficiency

correction- done

Correction due to a not correct evaluation of the

Beem Pipe and Drift Chamber walls thickness-

done

Systematic on efficiency - missing


Very preliminary systematics check

Source of systematic uncertainties

Systematic uncertainties (ps)

Range stability ± 60

Bin stability ± 20

Efficiency correction ± 10

Beam Pipe wall -10

Drift Chamber wall -15

Systematic uncertainties of the order of 65 ps


= (12.367±0.044stat ±0.065syst) ns

Weighted mean between and

KLOE

0.024 preliminary


Conclusions

We obtained

K± = (12.367±0.044stat ±0.065syst)ns

which is in agreement with the result obtained with the PDG 2004 fit.

We have to complete the systematics check

Work is in progress for the “time” measurement

charged kaon lifetime : preliminary result

Documents

kaon flight time

kine efficiency ag

kaon lifetime

good vertex

neutral vertex samplentrknv

neutral vertex sample

vertex askingclusters

vertex samplenvntrk