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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. Classification PSF Analysis. CPA. A Data Mining Tool. An Miner Analysis Program!.

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Classification PSF Analysis

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  1. 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 Classification PSF Analysis CPA

  2. A Data Mining Tool An Miner Analysis Program!

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

  4. Branch 1 Root Branch 2 Classification Trees Given a “catagorical varible” split the data into two pieces using “best” independent continuous varible • 1 if “vertex” direction is best • 2 if “best-track” direction is best Example: VTX.Type = 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 Use “Entropy” to deside which Independent varible to use: Entropy = Where k is over catagories and i is the ith Node (There are other criteria)

  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. 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 ( ACD.DOCA > 350 & Energy > .5*MC.Energy) (TKR.1.z0 > 250)

  7. The VTX Classification Tree Relative amounts of Catagories Relative amount of Data

  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. Do the Vertex Split! Predictor created by Classification Tree Use 2-Track Solution From “Thin” Split Use 1-Track Solution Rename probability column 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

  10. Bin the PSF Continuous Variable Catagroical Variable Target Class: Class #1 – MS PSF Limited Bin

  11. 2 Track Classification Tree

  12. 1 Track Classification Tree

  13. Combining Results

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

  15. Using Classification Trees PSF: 2.1o 95%/68% :2.34 Aeff: 1387 cm2 Before and After Trees

  16. Best results obtained using the “cuts” to achieve a good PSF Aeff 95/68 Ratio Before and After Trees

  17. Using Classification Trees PSF: 2.1o 95%/68% :2.34 Aeff: 1387 cm2 After Trees

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