particle identification by energy loss measurement in the na61 (shine) experiment

Particle identification by Particle identification by energy loss measurement in energy loss measurement in

the the NA61 (SHINE)NA61 (SHINE)experimentexperiment

Magdalena PosiadalaMagdalena Posiadala

University of WarsawUniversity of Warsaw

"Neutrino physics at accelerators", JINR, Dubna 27-30.01.2009 2

Outline:Outline:► Energy loss measurements in NA61 experiment at Energy loss measurements in NA61 experiment at

CERNCERN► Energy loss parametrisationEnergy loss parametrisation► Methods of Particle Identification in NA61 in the Methods of Particle Identification in NA61 in the

region of interest for the T2K.region of interest for the T2K.► Energy loss measurement in the region below Energy loss measurement in the region below

1GeV/c1GeV/c Two methods of approach to analysis:Two methods of approach to analysis: Method 1 – fits done using TMinuit packageMethod 1 – fits done using TMinuit package Method 2- using probability functionsMethod 2- using probability functions


NA61 (SHINE) NA61 (SHINE) detectordetector

TPCs are filled with mixtures of Ar+ COAr+ CO22 (90:10)(90:10) for VTPCs and (95:5)(95:5) for MTPCs

Max 72 points Max 90 points


Energy loss Energy loss measurementmeasurement

► The optimization of the parameters required for the The optimization of the parameters required for the determination of energy loss in the TPCs was determination of energy loss in the TPCs was performed using the method developed by NA49performed using the method developed by NA49. (For . (For details see Eur. Phys. J , C45 (2006) 343)details see Eur. Phys. J , C45 (2006) 343)

► Corrections for the following effects were Corrections for the following effects were applied during theapplied during the calibration of the NA61 data:calibration of the NA61 data: signal loss due to threshold cuts; corrections were obtained signal loss due to threshold cuts; corrections were obtained

using Monte Carlo calculations for gas mixture used in NA61,using Monte Carlo calculations for gas mixture used in NA61, time dependence of the TPC gas pressure,time dependence of the TPC gas pressure, residual time dependence of the measurements (day/night),residual time dependence of the measurements (day/night), charge absorption during the drift,charge absorption during the drift, differences in the TPC sector gain factors,differences in the TPC sector gain factors, differences in the amplification of the preamplifiers and edge differences in the amplification of the preamplifiers and edge

effects at sectors boundaries.effects at sectors boundaries.



Preliminary distributions of corrected dE/dx values for positively (white) and negatively (yellow) charged particles with momentum chosen around 5 and 15 GeV/c.


Energy loss Energy loss parametrisationparametrisation


dE/dx dE/dx vs number of vs number of measured points on the measured points on the

tracktrack

1 island-> vtpc1 island-> vtpc

2 i2 islandsland –> –> mtpc+vtpcmtpc+vtpc

Estimated resolution of dE/dx measurements of 4-5% can be achieved for particles passing through both the vertex and the main TPC chambers.


Total momentum Total momentum

N>30N>10

N>40 N>50


dE/dxdE/dx for total for total momentum p< 2 GeV/cmomentum p< 2 GeV/c

tracks tracks with with high high dE/dx dE/dx errors errors are are rejectedrejected


Methods of Particle Identification in Methods of Particle Identification in NA61 in the region of interest for NA61 in the region of interest for

the T2Kthe T2K

► In NA61 (SHINE) experiment particle identification is In NA61 (SHINE) experiment particle identification is possible using possible using ionization measurementsionization measurements in in active volume of the TPCs and active volume of the TPCs and time-of-flight time-of-flight measurementsmeasurements in ToF counters. in ToF counters.

► For ToF-F measurements see next presentation of For ToF-F measurements see next presentation of Sebastien Murphy.Sebastien Murphy.


► JNUBEAMJNUBEAM

NA61, 2C data, N>30

JNUBEAM –simulation of the T2K beam

Comparison of

NA61 data with T2K simulation


Tracks with and without Tracks with and without TOF-F N>30TOF-F N>30

dE/dx info


in the region below in the region below 1GeV/c1GeV/c


Statistics below Statistics below 1GeV/c1GeV/c – NA61 – NA61

DataData

in %

In T2K bins

ptot: 200MeV/bin x 50 bins till10GeV/c

:

20 rad/bin x 20bins

till 0.4 rad


Statistics for Statistics for

N>30, p<1GeV/cN>30, p<1GeV/c

p (GeV/c)


Statistics below Statistics below 1GeV/c1GeV/c -2C Data -2C Data

in %


dE/dx region below dE/dx region below 1GeV/c1GeV/c

Preliminary plot ofPreliminary plot of the reconstructed the reconstructed dE/dx values versus dE/dx values versus momentamomenta (p<1.2 (p<1.2 GeV)GeV) for positively for positively charged particles, charged particles, together with the together with the Bethe-Bloch curves Bethe-Bloch curves for for positronspositrons,, pionspions, , kaonskaons, , protonsprotons and and deuteronsdeuterons..

of dE/dx vs dE/dxof dE/dx vs dE/dx


Smearing with Gaussian theoretical Smearing with Gaussian theoretical distributiondistributionss of energy loss of energy loss

► For whole tracks we can calculate theoretical energy loss For whole tracks we can calculate theoretical energy loss dE/dxdE/dxteor teor using track total momentum and applied using track total momentum and applied parametrisation functions.parametrisation functions.

► In this way we get theoretical distribution of energy loss in In this way we get theoretical distribution of energy loss in momentum bins (see example plot on the right).momentum bins (see example plot on the right).

► Smearing with Gaussian functionSmearing with Gaussian function

dE/dx data

Example ->


Smearing with Gaussian theoretical Smearing with Gaussian theoretical distributiondistributionss of energy loss of energy loss (2) (2)

The theoretical distributions of energy loss are smeared with Gaussian function following this rules:

Mean = dE/dxtheor calculated for each track

Sigma = dE/dx for the each track; from NA61 data

After Gaussian smearing


Methods of analysisMethods of analysis

► Having theoretical energy loss distributions Having theoretical energy loss distributions smeared for each of the particle hypotheses I start smeared for each of the particle hypotheses I start to extractto extract raw raw particle yields using 2 methods: particle yields using 2 methods:

► Method 1-Method 1-> Making fits using TMinuit Package in > Making fits using TMinuit Package in RootRoot

► Method 2Method 2 ->Calculating probability functions for 4 ->Calculating probability functions for 4 particles hypothesesparticles hypotheses

► For more details look at the example in the next For more details look at the example in the next few slides.few slides.


Momentum range (0.16-Momentum range (0.16-0.3) GeV/c0.3) GeV/c

► Preliminary plot of Preliminary plot of the reconstructed the reconstructed dE/dx values versus dE/dx values versus momentum (p<1.2 momentum (p<1.2 GeV) for positively GeV) for positively charged particles, charged particles, together with the together with the Bethe-Bloch curves Bethe-Bloch curves for for positronspositrons, , pionspions, , and only and only kaons kaons in in selectedselected momentum range.momentum range.



► We have theoretical smeared with Gaussian energy We have theoretical smeared with Gaussian energy loss distributions (right) and NA61 data (left)loss distributions (right) and NA61 data (left)



►Method 1 -> uses Method 1 -> uses 2 2 function for function for minimizationminimization

► Theoretical distribution of energy loss is Theoretical distribution of energy loss is fitted to datafitted to data

► Calculation of the fit parameters Calculation of the fit parameters ,,,,,,

2

__2)(

__

data

Ktheortheordata

N

NNNNNetheorptheor



►Method 1Method 1►TMinuit TMinuit

fitsfits►ResultsResults


Momentum range (0.16-0.3) GeV/cMomentum range (0.16-0.3) GeV/c

► Method 2->Probability functions; for each of the bin i Method 2->Probability functions; for each of the bin i assuming particle hypotheses I calculate probability assuming particle hypotheses I calculate probability function defined as:function defined as:

ip

ieii

Ki

i

NNNN

Nprob


Momentum range (0.16-0.3) GeV/cMomentum range (0.16-0.3) GeV/c

► Method 2-> Method 2-> Multiply probability Multiply probability functions by functions by distribution of the distribution of the energy loss for the energy loss for the data in selected data in selected momentum slice to momentum slice to obtain colourobtain colourfulful plots plots at the bottomat the bottom picturepicture;;correspondicorresponding to particle ng to particle species.species.


Momentum range (0.3-0.4) Momentum range (0.3-0.4) GeV/cGeV/c




fitsfits



►Method 2Method 2►Probability Probability

functionsfunctions



protons are seen

(yellow line)




fitsfits




functionsfunctions

Almost the Almost the same number same number of positrons of positrons and kaons, and kaons, which iswhich isof course not of course not true.true.




fitsfits




functionsfunctions

Method 2 at this stage of studies works well Method 2 at this stage of studies works well only for momentum slices where theoretical only for momentum slices where theoretical lines do not cross completely !!!lines do not cross completely !!!Needs further improvements (Needs further improvements (/K ratio /K ratio parameterised by function)parameterised by function)


SummarySummary

► The resolution The resolution dE/dx dE/dx strongly depends on number strongly depends on number of points per track. For tracks passing through all of points per track. For tracks passing through all TPCs the resolution at the level of 4 - 5% .TPCs the resolution at the level of 4 - 5% .

► NA61 data cover acceptance in p, NA61 data cover acceptance in p, variables for variables for the T2K experiment.the T2K experiment.

► Important dE/dx analysis below 1 GeV/c region.Important dE/dx analysis below 1 GeV/c region.► Presented two methods Presented two methods towardstowards raw particle raw particle

yields yields ► Method 1 Method 1 –>making fits using TMinuit package–>making fits using TMinuit package► Method Method 2 2 with probability functions still needs with probability functions still needs

improvement->approaching towards max log-likelihood.improvement->approaching towards max log-likelihood.


Further stepsFurther steps

► On the right relative error vs energy loss distribution is presented.On the right relative error vs energy loss distribution is presented.► Next step is to smear theoretical lines with Gaussians that have Next step is to smear theoretical lines with Gaussians that have

error equal to relative error of dedx and see how it influences the error equal to relative error of dedx and see how it influences the presented resultspresented results

particle identification by energy loss measurement in the na61 (shine) experiment

Documents

energy loss measurements

rejectedneutrino physics

pointsneutrino physics

gevcneutrino physics

determination of energy

t2k simulationneutrino

signal loss

na61 experiment