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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 a faster analysis between Training data vs. Verification data.
  • With the NN trained with MC data, Significance maps were done with data of 2015 and 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
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: r5 - 18 Sep 2017, TomasCap

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