Bill Atwood, Nov. 2002GLAST 1 Classification PSF Analysis A New Analysis Tool: Insightful Miner...
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Transcript of Bill Atwood, Nov. 2002GLAST 1 Classification PSF Analysis A New Analysis Tool: Insightful Miner...
Bill Atwood, Nov. 2002 GLASTGLAST1
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
Bill Atwood, Nov. 2002 GLASTGLAST2
A Data Mining Tool
An MinerAnalysisProgram!
Bill Atwood, Nov. 2002 GLASTGLAST3
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
INPUT OUTPUT
A Properties Browser to set parameters
A Traditional “CUT”
Bill Atwood, Nov. 2002 GLASTGLAST4
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 whichIndependent varible to use:
Entropy = )log( ikik ppk
Where k is over catagories and i is the ith Node
(There are other criteria)
Continue process – treating each branch asa new “root.” Terminate according to statistics in last node and/or change in Entropy
Example: Classification Tree from Miner
Bill Atwood, Nov. 2002 GLASTGLAST5
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
Bill Atwood, Nov. 2002 GLASTGLAST6
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)
Bill Atwood, Nov. 2002 GLASTGLAST7
The VTX Classification Tree
Relative amounts of Catagories
Relative amount of Data
Bill Atwood, Nov. 2002 GLASTGLAST8
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
Bill Atwood, Nov. 2002 GLASTGLAST9
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 solutionsAre tried again as 1-Track events
Predictor created by Classification Tree
Rename probability column
From “Thin”
Split
Bill Atwood, Nov. 2002 GLASTGLAST10
Bin the PSF
Continuous Variable Catagroical Variable
Target Class: Class #1 – MS PSF Limited Bin
Bill Atwood, Nov. 2002 GLASTGLAST11
2 Track Classification Tree
Bill Atwood, Nov. 2002 GLASTGLAST12
1 Track Classification Tree
Bill Atwood, Nov. 2002 GLASTGLAST13
Combining Results
Bill Atwood, Nov. 2002 GLASTGLAST14
Example PSF’s At FoM Max
100 MeVPSF-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
Bill Atwood, Nov. 2002 GLASTGLAST15
Before and After Trees
PSF: 2.1o
95%/68% :2.34
Aeff: 1387 cm2
Using Classification Trees
Bill Atwood, Nov. 2002 GLASTGLAST16
Before and After Trees
0.0 0.1 0.2 0.3 0.4 0.5 0.6
VTX Angle
1.5
2.0
2.5
3.0
3.5
PSF6
8
1.0
1.5
2.0
2.5
PSF9
5/PS
F68
0.0 0.1 0.2 0.3 0.4 0.5 0.6
Vtx.Angle
1.5
2.0
2.5
3.0
3.5
4.0
4.5
PSF68
0
500
1000
1500
2000
2500
Aeff
95/68 Ratio
Aeff
Best results obtained using the “cuts” to achieve a good PSF
PSF: 2.1o
95%/68% :2.34
Aeff: 1387 cm2
Using Classification Trees