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:– Quartz Blade

• PID:– Hadron Absorber and Iron

wall

2zs

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

Fig

From position to track• That is the job for …

reconstruction1. Chose start and

finish point

2. Try to fit track to targets

3. Add middle points

4. Check that all the groups have points in the track


Reconstructed event


Experimental Particle Physics

• Chose a particle and a particular decay channel.(PDG)

• From that it will depend what is more important for you in terms of detector, and tracks

• For this presentation you’re going to see:

0sK


http://pdg.lbl.gov/2007/listings/contents_listings.html

Choice of good events

• You need to make sure that all the detectors you depend for your study were working correctly at the data taking time.


First mass spectrum


Cuts This is 90% of the Work…


Track distance

Δz

IV PCA

What these cuts are:

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

• The particles have decay lengths of 2.7 cm (becomes 72 cm in thelaboratory frame)

They make sense because:

After the Cuts


Background Subtraction

Combinatorial

The idea:

• Take a “particle” that 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 ones

• Take enough of them

• Subtract their mass to your histogram

Fit

This is the other 90% of the Work…


Acceptances

• The result you “see” has been biased by the detector and by the analysis steps.

• Now you must “unbias” so that you can publish a result comparable with other results.

• This is again… 90% of the work

• But after this you are done… You just have to write the thesis/article


Pause for questions

13 Feb. 2008 Pedro Parracho - MEFT@CERN 2008 12

Multivariate analysis

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

• Some Classification Methods :– Fisher Linear Discriminant– Gaussian Discriminant– Random Grid Search– Naïve Bayes (Likelihood Discriminant)– Kernel Density Estimation– Support Vector Machines– Genetic Algorithms– Binary Decision Trees– Neural Networks


Decision TreesNode

Leaf

MiniBoone, Byron Roe

A decision tree isa sequence of cuts.

Choose cuts that partition the data into bins of increasingpurity.

Key idea: do sorecursively.


13 Feb. 2008 Pedro Parracho - MEFT@CERN 2008

TMVA, what is it?

• Toolkit for Multivariate Analysis– software framework implementing several

MVA techniques– common processing of input data

(decorrelation, cuts,...)– training, testing and evaluation (plots, log-file)– reusable output of obtained models (C++

codelets, text files)

15


Implemented methods• Rectangular cut optimisation• Likelihood estimator• Multi-dimensional likelihood estimator and k-

nearest neighbor (kNN)• Fisher discriminant and H-Matrix• Artificial Neural Network (3 different

implementations)• Boosted/bagged Decision Trees• Rule ensemble• Support Vector Machine (SVM)• Function Discriminant Analysis (FDA)

16


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– common evaluation of all classifiers in an objective way– plugin as many classifiers as possible

• a GUI provides a set of performance plots• 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), which can be inserted into any application• it’s easy to use and flexible• easy to implement the chosen classifier in user

applications


Logical Flow


Correlation Plots


Comparison of all the methods• In this plot we can

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

20


Methods output

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


Where to cut

• The TMVA produces this kind of plots, which are very useful to help deciding how pure the selected signal can be


Eye Candy


Eye Candy II

End


Backup


PID in NA60

This is the “muon part of NA60”:

After the hadron absorber, only muons survive, and are tracked in the MWPCS

13 Feb. 2008 27Pedro Parracho - MEFT@CERN 2008back

Decision Trees

Geometrically, a decision tree is an n-dimensional histogram whose bins are constructed recursively

Each bin is associated with some value of the desired function f(x)

MiniBoone, Byron Roe

00 0.4

200

Energy (GeV)

PM

T H

its

100

B = 10S = 9

B = 37S = 4

B = 1S = 39

f(x) = 0 f(x) = 1

f(x) = 0


Decision Trees

For each variable find the best cut:

Decrease in impurity= Impurity(parent) - Impurity(leftChild)-Impurity(rightChild)

and partition using the best of the best

00 0.4

200

Energy (GeV)

PM

T H

its

100

B = 10S = 9

B = 37S = 4

B = 1S = 39

f(x) = 0 f(x) = 1

f(x) = 0


Decision Trees

A common impurity measure is (Gini):

Impurity = N * p*(1-p)

where

p = S / (S+B)

N = S + B0

0 0.4

200

Energy (GeV)

PM

T H

its

100

B = 10S = 9

B = 37S = 4

B = 1S = 39

f(x) = 0 f(x) = 1

f(x) = 0



How to use TMVA


Train the methods1. Book a “factory”

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

”<options>”)

2. Add Trees to the factoryfactory->AddSignalTree(sigTree, sigWeight);factory->AddBackgroundTree(bkgTreeA, bkgWeightA);

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

4. Book the methods to usefactory->BookMethod(TMVA::Types::<method enum>,

“<MethodName>", “<options>")

5. Train, test and evaluate the methodsfactory->TrainAllMethods();factory->TestAllMethods();factory->EvaluateAllMethods();


Apply the methods

1. Book a “reader”TMVA::Reader *reader = new TMVA::Reader()

2. Add the variablesreader->AddVariable(“<YourVar1>", &localVar1);

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

3. Book Classifiersreader->BookMVA( “<YourClassifierName>",

”<WheightFile.weights.txt>” );

4. Get the Classifier outputreader->EvaluateMVA(“<YourClassifierName>")

reader->EvaluateMVA("Cuts",signalEfficiency)

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Experimental Particle Physics

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