Hit and TrackingData set used: Pythia p+pHijing b=10, dca < 1 cmFit Points >= 24, dca < 1cm
Fit Points, Low MultiplicityCuts:
|dca=10
Fit Points, NowCuts:Central HijingGlobal dca=10Integral normalized to 1
Efficiency vs Multiplicity, last reviewHere, efficiency is:
All Matched TracksAll MC Tracks
(even MC tracksNot in acceptance)
So, absolute scaleMuch worse than True efficiency. Current Tracker Integrated Tracker
Efficiency vs Multiplicity, PionsHere, efficiency is:
Matched TracksThrown MC Tracks
(even MC tracksNot in acceptance)
So, reallyEffic*accept.Hijing, AuAub
Efficiency vs Multiplicity, KaonsHere, efficiency is:
Matched TracksThrown MC Tracks
(even MC tracksNot in acceptance)
So, reallyEffic*accept.
Efficiency vs Multiplicity, ProtonsHere, efficiency is:
Matched TracksThrown MC Tracks
(even MC tracksNot in acceptance)
So, reallyEffic*accept.
Efficiency vs pT, last reviewHere, efficiency is:
Found & MatchedMC Accepted
i.e. as in all spectraanalyses Current Tracker Integrated Tracker
Efficiency vs pT, Low Mult, loose cutsHere, efficiency is:
Found & MatchedMC Accepted
i.e. as in all spectraanalysesAt low multiplicityThings look OK
Efficiency vs pT, High Mult, loose cutsHere, efficiency is:
Found & MatchedMC Accepted
i.e. as in all spectraanalyses
Efficiency vs pT, High Mult, tighter dcaHere, efficiency is:
Found & MatchedMC Accepted
i.e. as in all spectraanalyses
and tighter fit pointsHere, efficiency is:
Found & MatchedMC Accepted
i.e. as in all spectraanalyses
Cuts like thoseused in identifiedspectra papers
Efficiency vs eta, tight cutsHere, efficiency is:
Found & MatchedMC Accepted
i.e. as in all spectraanalyses
Cuts like thoseused in identifiedspectra papers
Data Comparison, ITTF/TPT yieldsHere, Zhangbu used:
Fit Points >= 15For the highestmultiplicity,Sti finds ~80% of the tracks foundby the old tracker.
Data Comparison, ITTF/TPT yieldsHere, Zhangbu used:
Fit Points >= 15The ~80%improves ash approaches 1(but then decreases)
Snapshot and Areas to improveShape of distributions are similar to current trackerMean Fit Points shows similar trends with multiplicity, pt and etaShape at low fit points shows no bump from the large etaEfficiency is still low comparted to current trackerNew tracker shows stronger multiplicity dependenceLarge eta tracking needs tuning (see also Andrews talk)
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