A preliminary Muon Identification

result

You ZhengyunSchool of Physics , PKU

2005.11.23

Outline

Muon ID algorithm;

Muon ID result;

Muon ID variables;

Next work;

MuonID Samples

Train Samples :

Mu (True) & Pi (False) Reco. Tracks.Use Perfect Reco tracks only (all associated mc hits found)

5000 tracks at each Discrete Momentum ( @ 0.5, 0.75 and 1.0 GeV/c ) ,

MuonID Variables

Input variables : 1. Depth of track in Iron ; 2. Max hits a layer contains;

1.0GeV/c

MuonID Variables

Input variables : 1. Depth of track in Iron; 2. Max hits a layer contains;

0.75GeV/c

MuonID Variables

Input variables : 1. Depth of track in Iron; 2. Max hits a layer contains;

0.5GeV/c

MuID Alogrithm

Project n-dim variables space to 1-dim line. g : average of input variable vector;S : covariance of input variables;

T=S1+S2 ω=T-1(g1-g2) Y = ωt * x;

Use two samples mu and pi to get a Transform Vector ω, Which could separate two samples best, and determine a YCut;

Test :For an unknown sample to be tested, Use input variable x to get a y, compare it with YCut to determine whether it belongs to mu or pi;

MuonID @ 1.0GeV/c

YCut = 3.2

MuonID @ 1.0GeV/c Log

YCut = 3.2

MuonID @ 0.75GeV/c

YCut = 2.0

MuonID @ 0.75GeV/c Log

YCut = 2.0

MuonID @ 0.5GeV/c

YCut = 0.5

MuonID Efficiency

GeV/c

%

0.5 0.75 1.0

Mu Efficiency

87.64 93.42 91.18

Pi

Fake rate18.96 13.04 6.92

Mu Efficiency

85.00 91.22 89.42

Pi

Fake rate18.92 12.78 6.24

MuID Alg

Global :MucRec + MuID

For all tracks with|Cos θ| < 0.9

Mis-identified muon includes : 1. Lost hits by Acceptance; 2. Lost hits by muc reconstruction;3. All hits found, but mis-identified as pion by MuonID Algorithm;

YCut could be adjusted

Global :MucRec + MuID

For all tracks with|Cos θ| < 0.9

0

10

20

30

40

50

60

70

80

90

100

1 1. 2 1. 4 1. 6 1. 8 2 2. 2 2. 4 2. 6 2. 8 3

mupi

YCut

e.x @ 0.75GeV/c, YCut = 2.0 is the best when mu:pi = 1:1

YCut = 2.0

%

Next Work

Consider more variables;

More detailed MC samples to get a set of parameters and ycut;

Other identification algorithm;

Thank you !

图 6 进入 μ子鉴别器之前 , π介子衰变成μ子的比率随动量的分布

Efficiency

1.0 GeV cosθ(-0.9~0.9)

mu 10,000 single track0 hits : 236Perfect Reco Tracks (Lost 0) : 9609 5000

as mu: 5000-441=91.18%, no hits 109Other Tracks (Lost >0) :155

pi 30,000 single track0 hits : 16974Perfect Reco Tracks (Lost 0) : 28382 5000

as mu: 346 = 6.92%, no hits 2827Other Tracks (Lost >0) : 1618

yCut = 3.2; GlobalTest mu 5000 : 4471 = 89.42%Test pi 5000: 312 = 6.24%

Efficiency

0.75 GeV cosθ(-0.9~0.9)

mu 10,000 single track0 hits : 220Perfect Reco Tracks (Lost 0) : 9443 5000

as mu: 5000-329=93.42%, no hits 100Other Tracks (Lost >0) :557

pi 48,000 single track0 hits : 31645Perfect Reco Tracks (Lost 0) : 47409 5000

as mu: 652 = 13.04%, no hits 3193Other Tracks (Lost >0) : 591

yCut = 2.0; GlobalTest mu 5000 : 4561 = 91.22%Test pi 5000: 639 = 12.78%

Efficiency

0.5 GeV cosθ(-0.9~0.9)

mu 10,000 single track0 hits : 1218Perfect Reco Tracks (Lost 0) : 9468 5000

as mu: 5000-618=87.64%, no hits 618Other Tracks (Lost >0) :155

pi 90,000 single track0 hits : 79393Perfect Reco Tracks (Lost 0) : 95835 5000

as mu: 948 = 18.96%, no hits 4052 Other Tracks (Lost >0) : 1618

yCut = 0.5; GlobalTest mu 5000 : 4250 = 85%Test pi 5000: 946 =18.92 %