1. Bill Atwood, Nov. 2002GLAST 1 Classification PSF Analysis A New Analysis Tool: Insightful Miner Classification Trees From Cuts Classification Trees: Recasting of the GLAST PSF Analysis Energy Dependencies Present status of GLAST PSFs

2. Bill Atwood, Nov. 2002GLAST 2 A Data Mining Tool An Miner Analysis Program!

3. Bill Atwood, Nov. 2002GLAST 3 Miner Details What is a Data Miner? o A graphical user programming environment o An ensemble of Data Manipulation Tools o A Set of Data Modelling Tools o A “widget” scripting language o An interface to data bases Why use a Data Miner? o Fast and Easy prototyping of Analysis o Encourages “exploration” o Allows a more “Global” View of Analysis INPUTOUTPUT A Properties Browser to set parameters A Traditional “CUT”

4. Bill Atwood, Nov. 2002GLAST 4 Classification Trees Root Branch 1 Branch 2 Given a “catagorical varible” split the data into two pieces using “best” independent continuous varible Example: VTX.Type = 1 if “vertex” direction is best 2 if “best-track” direction is best Use “Entropy” to deside which Independent varible to use: Entropy = Where k is over catagories and i is the i th Node (There are other criteria) Continue process – treating each branch as a new “root.” Terminate according to statistics in last node and/or change in Entropy Example: Classification Tree from Miner

5. Bill Atwood, Nov. 2002GLAST 5 Classification Trees Why use Classification Trees? 1. Simplicity of method – recursive application of a decision making rule 2. Easily captures non-linear behavior in predictors as well As interactions amoung them 3. Not limited to just 2 catagories There are numerous text on this subject…… In the following analysis Classification Trees will be used to:  Separate out the good “vertex” events  Predict how “good” and event really is

6. Bill Atwood, Nov. 2002GLAST 6 GLAST PSF Analysis This portion of the code  Reads in the data  Culls out bad data  Adds new columns for analysis  Makes Global Cuts  Splits the data into 2 pieces  Thin Radiators  Thick Radiators (TKR.1.z0 > 250) ( ACD.DOCA > 350 & Energy >.5*MC.Energy)

7. Bill Atwood, Nov. 2002GLAST 7 The VTX Classification Tree Relative amounts of Catagories Relative amount of Data

8. Bill Atwood, Nov. 2002GLAST 8 CPA: To Vertex or not to Vertex? Probability is not continuous – its essentially binned by the finite number of leaves (ending nodes) There is a “gap” at.5 - Use that to determine which solution to use

9. Bill Atwood, Nov. 2002GLAST 9 Do the Vertex Split! Use 2-Track Solution Use 1-Track Solution The data are now divided into 2 subsets according to the Probability that the 2-Track (“vertex”) solution is best. No data have been eliminated – Failed Vertexed solutions Are tried again as 1-Track events Predictor created by Classification Tree Rename probability column From “Thin” Split

10. Bill Atwood, Nov. 2002GLAST 10 Bin the PSF Continuous Variable Catagroical Variable Target Class: Class #1 – MS PSF Limited Bin

11. Bill Atwood, Nov. 2002GLAST 11 2 Track Classification Tree

12. Bill Atwood, Nov. 2002GLAST 12 1 Track Classification Tree

13. Bill Atwood, Nov. 2002GLAST 13 Combining Results

14. Bill Atwood, Nov. 2002GLAST 14 Example PSF’s At FoM Max 100 MeV PSF-68 =2.7 o 95/68 = 2.65 1000 MeV: PSF-68 =.35 o 95/68 = 2.3 10000 MeV : PSF-68 =.1 o 95/68 = 2.9

15. Bill Atwood, Nov. 2002GLAST 15 Before and After Trees PSF: 2.1 o 95%/68% :2.34 A eff : 1387 cm 2 Using Classification Trees

16. Bill Atwood, Nov. 2002GLAST 16 Before and After Trees 95/68 Ratio A eff Best results obtained using the “cuts” to achieve a good PSF PSF: 2.1 o 95%/68% :2.34 A eff : 1387 cm 2 Using Classification Trees