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-- TomasCap - 25 Aug 2017 This is my logbook


Week 1

  • Check in at MSU office.
  • Get the Spartan card. It helps me to access to building and office.
  • AERIE installation (in my personal computer) was solved.
  • First presentation: What things I did and the future work.

Week 2

  • A network with 15 inputs was trained for distinguishing from gamma to hadrons.
  • Choose the best variables and use them as input (variable importance analysis).
  • Create a module that computes the network's output.

Goals from August, 14th to August 25th (Week 3 & 4)

  1. Create a Foswiki
  2. Made an analysis with the most important variable (ranking).
  3. Choose the most significant variables and use them as inputs to the network.
  4. Make a Crab maps with the networks trained
  5. Train a network using real data as Background and MC as Signal. Compare the results

Week 3

Week 4

  • A network was trained with all variables of HAWC data stream in order to obtain the ranking of these variables (MC were used).
  • The best variables were chosen and fed as inputs to the networks.
  • Create a Crab Map with the new networks trained.
  • Obtain the ranking (variable importance) using real data.

Goal from August 28th to September 1st

  1. Work on a new version of disMax.
  2. Read information about Boosted Decision Tree (BDT).
  3. Train a BDT with 15 features.
  4. Check why don't have good results when real data are used.

Week 5

  • I've read the Chapter 11 "Decision Tree" in order to understand how to work it.
  • A BDT was trained with Real data as BKG and MC as Signal. But It doesn't answer that I've expected.
  • A BDT was trained with only MC as BKG and Signal. It has a good performance in the bins 4,5,6.
  • Presentation of the MSU meeting (Tuesday, September 5th,2017): The first result using BDT.


Goal from September 5th to 8th

  • Make the plot of Energy Vs Q factor of BDT and NN.
  • Repeat the analysis of variable importance but now with the BDT.
  • Train other BDT.

Week 1

  • Explain how to training and verification
  • Make an analysis between Training data vs. Verification data.
  • Create a code that can make Crab map using different months of the year.
  • Using the NN that was trained with MC data, Significance maps were done with data of 2015 and 2017.

Goal from September 11th to 15th

  • Compare the variable between Training data set vs. Testing data set in oder to look for which variable use as input of the machine learning.
  • Train a NN and BDT, using MC as signal and real data as Bkg.


  • Crab maps in the Bin 0 using NN (with 15 inputs) that was trained with MC data.
  • A NN and simple BDT was trained with 10 inputs: Compactness, rec.PINCness, rec.planeChi2, rec.SFCFChi2, rec.logNPE, rec.CxPE40SPTime, rec.LDFAge, rec.LDFAmp, rec.LDFChi2 and rec.disMax
  • Make some Crab maps to check if the NN or BDT has a good performance.
  • In the comparation of Training and verification data. On one hand we compara Bkg vs. Bkg, we need to exclude the data around the crab nebula, and the other hand, the data around the crab nebula are used to compare with signal (MC)

Goal from September 18 to 22

  • Doing an analysis in order to choose which variables will be used to G/H sep
  • Train a NN and BDT.

Week 3

  • Slide of the compare Training and verification data. Result of an NN and BDT trained: 09/19/2017
  • The rectangle cut (RC) was applied using Compactness and PINCness, in order to check if the phases to train is correct.
  • A comparison is done using RC, NN, and SC.
  • There are three sets: Training, Verification, and Testing.
    • Training set is used to fit the model.
    • Validation set is used to estimate prediction error for model selection.
    • Test set is used for assessment of the generalization error of the final chosen model.

Goal from September 25 to 29

  • Use mc.delAngle in the training, verification and testing stage.
  • Compare my results with John's cut and Bredran's cut.
  • Choose a potencial parameter.

Week 4

  • Slide of the compare NN, RC and SC 09/26/2017
  • mc.delAngle is used for training a NN and looking for an optimal Rectangle cut.
  • A comparation of using delAngle and do not use it is made.
  • Comparation of John's cut adn Breand's cut vs. my NN is started but it does not complete.


Goal from October 2 to 6

  • Compare my results with John's cut and Bredran's cut.
  • Choose a potencial parameter.
  • Use these parameter as input of NN.

Week 1

  • Slide of the compare using mc.delAngle and does not use it 10/03/2017
Topic attachments
I Attachment Action Size Date Who Comment
20170818_ChooseFeatures.pdfpdf 20170818_ChooseFeatures.pdf manage 2570.3 K 25 Aug 2017 - 22:06 TomasCap  
20170905BDT.pdfpdf 20170905BDT.pdf manage 3085.9 K 05 Sep 2017 - 15:50 TomasCap The first results using Boost Decision Tree
20170912_MapsBin0And1.pdfpdf 20170912_MapsBin0And1.pdf manage 1811.7 K 18 Sep 2017 - 21:08 TomasCap Maps of the Crab nebula in Bin 0 and 1. Compare the maps of Standartd cut vs NN that was trained using MC data
20170919_compareTAndV.pdfpdf 20170919_compareTAndV.pdf manage 5040.5 K 19 Sep 2017 - 15:05 TomasCap Presentation of Sep 19th, 2017
BringingToTheTable.pdfpdf BringingToTheTable.pdf manage 1100.3 K 25 Aug 2017 - 21:31 TomasCap What things I did about gamma/hadron separation using neural network
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Topic revision: r9 - 05 Oct 2017, TomasCap

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