1. Background Rejection Activities in Italy
Gamma-ray Large Area Space Telescope
Background Rejection Activities in Italy
Francesco Longo
University and INFN, Trieste, Italy
On behalf of the “North-East” INFN group
thanks in particular to R.Rando, O.Tibolla, Y.Lei, G.Busetto and P.Azzi (University and INFN Padova)

2. Bkg Rejection activity
Starting from “simple cuts”
Collection of Bkg Rejection documentation
Understanding IM cuts (DC1 variables and cuts)
Classification Trees in R
New recent developments
Ready for new data
More Info:

3. “By hands” cuts First iteration using already suggested cuts
Look into Merit Tuple to find efficiency of rejection and gamma acceptance
Reference docs (Atwood):
“Instrument response studies”
“Post rome background rejection”
Datasets:
DC1 prep background ntuples
DC1 prep gamma Merit nutple
Divide events in particle type: gamma(signal), gamma(bkg), electron+positron, protons

4. Calorimeter categories
Definitions:
No cal: CalEnergySum< || CalTotRLn≤2
Low Cal: CalEnergySum> && CalTotRLn>2
Med Cal: CalEnergySum> && CalTotRLn>2
High Cal: CalEnergySum<3500. && CalTotRLn>2
“Good Energy” events:
“good_energy” = (EvtEnergySumOpt-MCEnergy)/MCEnergy
|”good_energy”| ≤ 35%

5. Signal events split g(all) g (good) ALL Total No Low Med High 722594
314226
(43.4%)
122081
(17%)
114224
(15.8%)
172063
(23.8%)
g (good)
81139
(66.4%)
94869
(83%)
142082

6. Background events split
ALL
Total No
Low
Med
High e
746232
649293
(87%)
93933
(13%)
1139
(0.2%)
1867
(0.3%) p
608568
(52%)
284425
(24%)
178084
(15%)
94200
(8%) g
288491
276358
(96%)
10066
(3.5%)
1227
(0.4%)
840
GOOD
Low
Med
High e
60093 (64%)
854 (75%)
1365 (73%) p
35407 (12%)
10914 (5.7%)
(22%) g
5185 (51.5%)
797 (65%)
459 (55%)

7. Gamma
Low Cal
GOOD
BAD
ALL
Good ene
High Cal
ALL
GOOD
BAD
MCEnergy

8. “Tree” cuts Using IM xml file in classification
Develop a “Node” structure parsing the xml IM output
Check of cuts

9. CT approach ID
predicate ID
0/1
predicate

10. Signal events split All Total No Lo Med Hi g(all) 722,594 314,226
122,081
114,224
172,063
g(good)
64,110
(20.4%)
86,190
(70.6%)
90,201
(79.0%)
138,793
(80.7%)

11. Good Cal E

12. Starting with Classification Trees
Use of R program – rpart (recursive partitioning)
Searching to optimize “goodCal”
For each step rpart reports the cost-complexity of the tree, the number of splits, the relative error and finally the error that it obtains from a process of cross validation, with the corresponding sigma.

13. Classification Trees with rpart

14. Classification Trees with rpart

15. Classification Trees with rpart

16. Classification Trees with rpart

17. One Tree per E bin

18. One Tree per E bin

19. One Tree per E bin

20. Classification Trees with R

21. rpart Classification

22. rpart Classification

23. Error costs

24. Error Costs

25. Error Costs

26. Random Forest

27. Random Forests

28. Random Forests

29. Random Forests

30. rForest package

31. Conclusions Work is progressing… Work on new variables started
More results will come…