Topical Lectures on Machine learning - TMVA tutorials · Topical Lectures on Machine learning TMVA...
Transcript of Topical Lectures on Machine learning - TMVA tutorials · Topical Lectures on Machine learning TMVA...
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Topical Lectures on Machine learning
TMVA tutorials
Rabah Abdul Khalek
April 6, 2018
Nikhef
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MAGIC Telescope
Detect and study primarily photons coming from:
• Growing black holes in active galactic nuclei.
• Supernova remnants, due to their interest as sources of cosmic rays.
• Other galactic sources such as pulsar wind nebulae or X-ray binaries.
• Unidentified EGRET or Fermi sources.
• Gamma ray bursts.
• Annihilation of dark matter.
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MAGIC data
The data are MC generated to simulate registration of high energy
gamma particles in a ground-based atmospheric Cherenkov gamma
telescope using the imaging technique.
index variable description
1 fLength major axis of ellipse
2 fWidth minor axis of ellipse
3 fSize 10-log of sum of content of all pixels
4 fConc ratio of sum of two highest pixels over fSize
5 fConc1 ratio of highest pixel over fSize
6 fAsym distance from highest pixel to center
7 fM3Long 3rd root of third moment along major axis
8 fM3Trans 3rd root of third moment along minor axis
9 fAlpha angle of major axis with vector to origin
10 fDist distance from origin to center of ellipse
class 1 for gamma (signal), 0 for hadron (background)
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MAGIC data
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Summary of the steps
The main source of background are the hadronic showers initiated by
cosmic rays in the upper atmosphere.
1. Partitioning data.
Total set = 19020 events.
Training set (60%) = 8632(s)+4681(b) = 11099.
Testing set (30%) = 2467(s)+1339(b) = 6020.
Validation set (10%) = 1901.
2. Build the NN/MLP Classifier, train it and test it.
3. Apply the trained classifier on the validation set.
4. Compare the performance of different algorithms.
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Hands-on coding
Start by cloning the repo:
git clone
https://github.com/rabah-khalek/TMVA_tutorials.git
cd TMVA_tutorials
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Hands-on coding
Start by cloning the repo:
git clone
https://github.com/rabah-khalek/TMVA_tutorials.git
cd TMVA_tutorials
Step1: Load the shower data into TTrees:
git checkout step1
You should be able to create three files in data/: signal.root,
background.root and validation.root.
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Hands-on coding
Start by cloning the repo:
git clone
https://github.com/rabah-khalek/TMVA_tutorials.git
cd TMVA_tutorials
Step1: Load the shower data into TTrees:
git checkout step1
Step1: Check the solution:
git checkout step1_solution
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Hands-on coding
Step2: Build the Classifier, train it and test it.
git checkout step2
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Hands-on coding
Step2: Build the Classifier, train it and test it.
git checkout step2
Step2: Check the solution
git checkout step2_solution
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classifier output on testing set
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Cuts on the classifier
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Hands-on coding
Step3: Apply the trained classifier on the validation set.
git checkout step3
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Hands-on coding
Step3: Apply the trained classifier on the validation set.
git checkout step3
Step3: Check the solution
git checkout step3_solution
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Results - Application on validation set
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Hands-on coding
Step4: Compare the performance of different algorithms.
git checkout full_version
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ROC curve - the classifier performance
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Thank you
Thank you!
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