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Experimental Particle Physics Experimental Particle Physics - - PowerPoint PPT Presentation

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Experimental Particle Physics Experimental Particle Physics Detector by function Position: Beam Tracker Vertex Telescope Multi-wire Proportional Chambers (MWPCs) Energy: Zero Degree Calorimeter (ZDC) Charge:

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SLIDE 1

Experimental Particle Physics Experimental Particle Physics

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SLIDE 2

Detector by function

  • Position:

– Beam Tracker – Vertex Telescope – Multi-wire Proportional Chambers (MWPCs)

  • Energy:

– Zero Degree Calorimeter (ZDC)

  • Charge:

– Quartz Blade

  • PID:

∑

∝

2

z s

PID:

– Hadron Absorber and Iron wall

∑ z

s

13 Feb. 2008 2 Pedro Parracho - MEFT@CERN 2008

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SLIDE 3

From position o track

  • That is the job for …

reconstruction

1. Chose start and finish point 2. Try to fit track to targets targets 3. Add middle points 4. Check that all the groups have groups have points in the track

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SLIDE 4

Reconstructed event

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SLIDE 5

Experimental Particle Physics

  • Chose a particle and a particular decay

channel channel.(PDG)

  • From that it will depend what is more

i t t f i t f d t t d important for you in terms of detector, and tracks

  • For this presentation you’re going to see:

+ − +

→ π π

s

K

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SLIDE 6

Choice of good events

  • You need to make sure that all the

detectors you depend for your study were detectors you depend for your study were working correctly at the data taking time.

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SLIDE 7

First mass spectrum

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SLIDE 8

Cuts

This is 90% of the Work…

What these cuts are:

Track distance IV PCA Δz

  • Daughter particles of a V0 decay
  • riginate at the same point in space

They make sense because:

  • riginate at the same point in space
  • The particles have decay lengths of

2.7 cm (becomes 72 cm in the

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laboratory frame)

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SLIDE 9

After the Cuts

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SLIDE 10

Background Subtraction

Combinatorial Fit

This is the other 90% of the Work…

The idea:

  • Take a “particle” that

could be real but that you could be real, but that you are sure it is not.

– Each track is from a different collision – The ditracks characteristics are according to the real

  • nes
  • Take enough of them
  • Subtract their mass to
  • Subtract their mass to

your histogram

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SLIDE 11

Acceptances

  • The result you “see” has been biased by

the detector and by the analysis steps the detector and by the analysis steps.

  • Now you must “unbias” so that you can

bli h lt bl ith th publish a result comparable with other results.

  • This is again… 90% of the work
  • But after this you are done… You just

y j have to write the thesis/article ☺

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SLIDE 12

Pause for questions

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SLIDE 13

Multivariate analysis

  • Multivariate statistical analysis is a collection of

procedures which involve observation and procedures which involve observation and analysis of more than one statistical variable at a time.

  • Some Classification Methods :

– Fisher Linear Discriminant Gaussian Discriminant – Gaussian Discriminant – Random Grid Search – Naïve Bayes (Likelihood Discriminant) – Kernel Density Estimation e e e s y s a o – Support Vector Machines – Genetic Algorithms – Binary Decision Trees – Neural Networks

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SLIDE 14

Decision Trees

Node

A decision tree is f a sequence of cuts. h h Choose cuts that partition the data i bi f i i into bins of increasing purity.

Leaf

Key idea: do so i l

MiniBoone, Byron Roe

recursively.

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SLIDE 15

TMVA, what is it?

  • Toolkit for Multivariate Analysis

ft f k i l ti l – software framework implementing several MVA techniques common processing of input data – common processing of input data (decorrelation, cuts,...) training testing and evaluation (plots log file) – training, testing and evaluation (plots, log-file) – reusable output of obtained models (C++ codelets text files) codelets, text files)

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SLIDE 16

Implemented methods

  • Rectangular cut optimisation
  • Likelihood estimator

Likelihood estimator

  • Multi-dimensional likelihood estimator and k-

nearest neighbor (kNN) g ( )

  • Fisher discriminant and H-Matrix
  • Artificial Neural Network (3 different

implementations)

  • Boosted/bagged Decision Trees

R l bl

  • Rule ensemble
  • Support Vector Machine (SVM)

F ti Di i i t A l i (FDA)

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  • Function Discriminant Analysis (FDA)

16

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SLIDE 17

Advantages of TMVA

  • Distributed with ROOT
  • several methods under one 'roof‘

– easy to systematically compare many classifiers, – and find the best one for the problem at hand – common input/output interfaces l ti f ll l ifi i bj ti – common evaluation of all classifiers in an objective way – plugin as many classifiers as possible

  • a GUI provides a set of performance plots

th fi l d l( ) d i l t t fil d

  • the final model(s) are saved as simple text files and

reusable through a reader class

  • also,

the models may be saved as C++ classes (package independent) hich can be inserted into an (package independent), which can be inserted into any application

  • it’s easy to use and flexible

t i l t th h l ifi i

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  • easy

to implement the chosen classifier in user applications

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SLIDE 18

Logical Flow

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SLIDE 19

Plots

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SLIDE 20

Correlation Plots

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SLIDE 21

Comparison of all the methods Comparison of all the methods

  • In this plot we can

see how good see how good each of the methods is for our problem.

  • The best method

seems to be the BDT (boosted decision trees) that is basically a method that expands the usual cut method to more dimensions

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SLIDE 22

Methods output Methods output

All the methods output a number (the output classifier) that represents All the methods output a number (the output classifier) that represents how well the given event matches the background. Here we can see the distributions of this value for two chosen methods (the best: BDT and the worst: Function Discriminant Analysis). This plots can help s to pinpoint the c t al e to chose for o r st d

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us to pinpoint the cut value to chose for our study.

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SLIDE 23

Where to cut Where to cut

Th TMVA d thi ki d f l t hi h f l t h l

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  • The TMVA produces this kind of plots, which are very useful to help

deciding how pure the selected signal can be

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SLIDE 24

Eye Candy

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SLIDE 25

Eye Candy II

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SLIDE 26

End

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SLIDE 27

Backup

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SLIDE 28

PID in NA60

This is the “muon part of NA60”: After the hadron absorber only muons survive and are tracked in the MWPCS After the hadron absorber, only muons survive, and are tracked in the MWPCS

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SLIDE 29

Decision Trees

Geometrically, a decision

200 f(x) = 0

f(x) = 1 y, tree is an n- dimensional histogram whose bins B = 10 B = 1 histogram whose bins are constructed recursively

100

B = 10 S = 9 B = 1 S = 39 f(x) = 0 y Each bin is associated

MT Hits

B 37 f(x) = 0 with some value of the desired function f(x)

E (G V) PM

B = 37 S = 4

MiniBoone, Byron Roe

0.4 Energy (GeV)

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SLIDE 30

Decision Trees

For each variable find

200 f(x) = 0

f(x) = 1

For each variable find the best cut:

B = 10 B = 1

Decrease in impurity = Impurity(parent)

100

B = 10 S = 9 B = 1 S = 39 f(x) = 0

= Impurity(parent)

  • Impurity(leftChild)

I it ( i htChild)

MT Hits

B 37 f(x) = 0

  • Impurity(rightChild)

and partition sing the

E (G V) PM

B = 37 S = 4

and partition using the best of the best

0.4 Energy (GeV)

13 Feb. 2008 30 Pedro Parracho - MEFT@CERN 2008

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SLIDE 31

Decision Trees

A common impurity

200 f(x) = 0

f(x) = 1 A common impurity measure is (Gini): B = 10 B = 1 Impurity = N * p*(1-p)

100

B = 10 S = 9 B = 1 S = 39 f(x) = 0 where p = S / (S+B)

MT Hits

B 37 f(x) = 0 p = S / (S+B) N = S + B

E (G V) PM

B = 37 S = 4 N = S + B

0.4 Energy (GeV)

13 Feb. 2008 31 Pedro Parracho - MEFT@CERN 2008

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SLIDE 32

How to use TMVA

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SLIDE 33

Train the methods

1. Book a “factory”

TMVA::Factory* factory = new TMVA Factory(“<JobName>” targetFile new TMVA::Factory(“<JobName>”, targetFile, ”<options>”)

2. Add Trees to the factory

factory->AddSignalTree(sigTree, sigWeight); y g g g g factory->AddBackgroundTree(bkgTreeA, bkgWeightA);

3. Add Variables

factory->AddVariable(“VarName”, ‘I’) factory->AddVariable(“log(<VarName>)”, ‘F’)

4. Book the methods to use

factory->BookMethod(TMVA::Types::<method enum>, “<MethodName>" “<options>") <MethodName> , <options> )

5. Train, test and evaluate the methods

factory->TrainAllMethods(); factory->TestAllMethods();

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y (); factory->EvaluateAllMethods();

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SLIDE 34

Apply the methods

  • 1. Book a “reader”

TMVA::Reader *reader = new TMVA::Reader() TMVA::Reader reader = new TMVA::Reader()

  • 2. Add the variables

reader->AddVariable(“<YourVar1>", &localVar1); ( , ); reader->AddVariable(“log(<YourVar1>)", &localVar1);

  • 3. Book Classifiers

reader->BookMVA( “<YourClassifierName>", ”<WheightFile.weights.txt>” );

4 Get the Classifier output

  • 4. Get the Classifier output

reader->EvaluateMVA(“<YourClassifierName>") reader->EvaluateMVA("Cuts",signalEfficiency)

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