Analysis Meeting 31Oct 05T. Burnett1 Classification, etc. status Implement pre-filters Energy estimation
Analysis Meeting 31Oct 05T. Burnett2 Prefilters All of the Atwood background rejection trees require prefilters Simplifies trees Needed to implement IM trees Reject if true: AcdActiveDist > -199 | AcdRibbonActDist > | CalTrackDoca > 40 | CalTrackAngle >.5 | CalXtalRatio >.85 One of eight: the track/medcal case apply the classification tree only for events passing the filter
Analysis Meeting 31Oct 05T. Burnett3 Implemented by a simple file filter.txt has this text (each statement must be true) this is a tree, and is converted to same applied in training, testing, and final analysis AcdActiveDist < AcdRibbonActDist < CalTrackDoca < 40 CalTrackAngle <.5 CalXtalRatio <.85
Analysis Meeting 31Oct 05T. Burnett4 The gamma pre-filter cuts Gamma classification categoryprefilter: remove if true vertex-high AcdActiveDist > -10 | CalTrackAngle >.5 | CalTrackDoca > 40 vertex-med AcdActiveDist > -199 | AcdRibbonActDist > |CalTrackDoca > 200 vertex-thin AcdActiveDist > -199 | AcdRibbonActDist > vertex-thick AcdUpperTileCount > 0 | AcdLowerTileCount > 1 |AcdRibbonActDist > track-high CalTrackDoca > 30 | CalTrackAngle >.3 track-med AcdActiveDist > -199 | AcdRibbonActDist > | CalTrackDoca > 40 | CalTrackAngle >.5 | CalXtalRatio >.85 track-thin AcdActiveDist > -199 | AcdRibbonActDist > | CalTrackDoca > 200 | EvtECalTransRms <.8 track-thick AcdActiveDist > -199 | AcdRibbonActDist > | AcdDoca 200 | EvtECalTransRms > 2.5 | CalMaxXtalRatio >.8 | Tkr1FirstChisq > 2.5 | Tkr1ToTTrAve > 2
Analysis Meeting 31Oct 05T. Burnett5 New energy trees Need to implement this sequence, described in detail previously by Bill last August. need 4 trees, each to evaluate the likelihood that each of the energy estimation methods is best resulting probability is the best, energy corresponds to best
Analysis Meeting 31Oct 05T. Burnett6 New folder setup Each tree is described by three files in each folder: –dtree.txt – ascii file with a list of weighted trees and nodes: tree: specify the weight to assign to the tree branch: variable index, cut value leaf: purity –variables.txt – list of the corresponding tuple variables –filter.txt – optional filter Evaluation is by passing a vector of floats, ordered according to the variable list. new
Analysis Meeting 31Oct 05T. Burnett7 Preliminary look at UW energy analysis trees Single trees, same variables, good criterion as Bill
Analysis Meeting 31Oct 05T. Burnett8 Dispersion study, 0.25 cut
Analysis Meeting 31Oct 05T. Burnett9 Dispersion study, 0.50 cut
Analysis Meeting 31Oct 05T. Burnett10 Dispersion, 0.75 cut
Analysis Meeting 31Oct 05T. Burnett11 Conclude Best cut is probably energy-dependent