 
JuanCastillo  15 Jul 2008
Analysis strategy
These analysis were perfomed with
AliAnalysisTasks
.
Event and track cuts
For accepting an event, it is checked the existance of the next objects:
 AliESDEvent, the full event generated via montecarlo truth (henceforth referred as ESD).
 AliMCEvent, the associated montecarlo truth (henceforth called MC).
 AliESDtrackCuts, responsible of handling of ESD track cuts.
Then the analysis of the event is performed. This is done via the next algorithm.
 A list of tracks passing the cut is obtained from the ESD after the AliESDtrackCuts function GetAcceptedTrack.
 Loop over the accepted tracks. Inside, we require:
 The existance of the AliESDtrack.
 A good AliESDtrack label.
 The existance of an AliMCParticle associated with that label.
 That GetTPCInnerParam is returning from AliESDEvent a good AliExternalTrackParam.
All the above conditions are applied to ESD tracks and MC tracks, since we're doing a detectordependent analysis, and there are more MC tracks associated, for example, with the TRD detector.
If all the checks are passed, we consider it
accepted track, and the track information is processed.
Track processing
Once the track is accepted, additional constrains are applied.

GetKinkIndex(0)<=0
(no kinks condition)

esdTrack>GetStatus()&AliESDtrack::kTPCrefit
(TPC refit condition)
 both above.
From the AliExternalTrackParam in the case of an ESD analysis, from an
AliMCEvent in the case of MC analysis, the charge is obtained.
The track information used in this analysis includes charge, rapidity, transversal momentum and azimuthal angle.
If the
charge is different from zero, the track is processed.
Two different strategies are applied for both ESD and MC track information.
Direct analysis: the Histo strategy
The acceptance is divided in arbitrary symmetric pseudorapidity intervals by the class
MDcorrelations
.
In the analysis code, additional
constrains (
cuts,
intervals) are applied.
The resulting track information per
constrain and pseudorapidity interval is stored in a set of reference histograms:
,
,
for the full interval, the forward region and the backward region, and
and vice versa.
Each group of histograms is managed by a
MDclassQA
object.
The output of one run over the data are, in this way, 512 histograms, that are afterwards processed.
Dispersion analysis: The Tree strategy.
The basic idea is common, to allow to run both analysis strategies at the same time.
The acceptance is divided in arbitrary symmetric pseudorapidity intervals by the class
MDcorrelations
.
In the analysis code, additional
constrains (
cuts,
intervals) are applied.
Once the track is accepted, the additional constrains above (no kinks condition, TPC refit condition)are applied.
Two objects
MDcorrelations
(one for the forward region, one for the backward) that last
only for the event receive the track information.
Depending of a pseudorapidity interval a counter is increased.
Once the event is fully analyzed, the object info is streamed to a TTree and the objects cleaned.
The output of run over the data is a relatively big
TTree
with one entry per event, that afterwards can be processed.
Matching the montecarlo truth
To accept a MonteCarlo event, we require the same conditions that for an ESD event (points 1,2 and 3 in section
Event and track cut ).
A good
AliESDEvent is needed for crosschecks.
We should also apply the cuts contained inside
AliESDtrackCuts, since we're only interested in the measured tracks.
Due to the correspondence between lists, a list of tracks passing the cut is obtained from the ESD after the function GetAcceptedTrack, like in the case of the ESD analysis.We will loop on this list, asking for
AliMCEvent tracks.
Each track is asked:
 To have a AliMCParticle associated.
 To be charged.
One object (MDhandler) that lasts for the full analysis receives the accepted track information. For debugging purposes, more MDhandler objets are filled at intermediate steps, before the track is accepted.
Image: ratio MC value /ESD value (Full rapidity, same statistic)
ESD data analysis
ForwardBackward correlation
Matching histo and tree strategies.
Both strategies are equivalent.
To prove that, ratios histograms were created.
Image: MD and ratios of both strategies, full rapidity
Rapidity intervals
To see the dependencies of the rapidity gap, the pseudorapidity coordinate was divided first in forward and
backward regions, then in intervals of 0.2.
NOTE: Zero is EXCLUDED in both regions due to the existence of the central electrode.
For intervals 00 and 01, we consider the next pseudorapidity ranges:
 forward: [1.5,1.3) (f_00) [1.3,1.1) (f_01)
 backward: [1.5,(1.3)) (b_00) [1.3,1.1) (b_01)
A parenthesis indicates the number is outside the range.
A square bracket indicates the number is inside.
Image: MD, dispersion for rapidity intervals 00 and 01
Forwardbackward correlations
We defined the center of a pseudorapidity interval like the center of the pseudorapidity range covered by it.
The pseudorapidity gap (
gap) is taken as the distance between the center of the forward rapidity interval and the backward one.
The multiplicity per event of a certain forward pseudorapidity interval was plotted against its corresponding backward one.
Image: FB correlation clouds
The corresponding correlation clouds were
profiled and the resulting profile, fitted in a range chosen after density criterias.
Image: FB profiles fits
Correlation strength for the ESD
Correlation stregth b, zero and high rapidity points removed, full statistics:
Comments
This numbers correspond to an energy of 10 TeV.
A. Kumar used PYTHIA data for 14 TeV.
value 
Kumar 
mine 
chi2/ndf 
110.2/7 
369.4/5 
constant 
0.8214 
0.8647 
lambda 
23.35 
20.29 
Forwardbackward and forwardforward dispersion
We define:
 backwardforward dispersion :
 forwardforward dispersion :
In the next plot, it it shown the evolution of these coefficients with the pseudorapidity gap, depending on the size of the sample under consideration. This has been done to estimate the minimum sample size for a significative tendency.
Image: forwardforward dispersion sample size dependency
Image: backwardforward dispersion sample size dependency
Comments
A first glance to a Pythia equivalent analysis:
Forwardforward and backwardforward dispersion with Pythia:
Multiplicity Scaling
Preliminary results: old "nonrobust" fitting.
Image: ESDcut clan fit Full rapidity
Image: ESDcut clan fit Central rapidity
Image: ESDcut clan fit Unit rapidity
Image: ESDcut clan fit Half rapidity
It is shown a clear centrality dependency of the semihard contribution to the multiplicity distribution.
Cosmics analysis
Forwardbackward correlations
The software for analyzing ESD FB correlation was run over cosmic data.
Image: forwardbackward correlations for cosmics
Multiplicity
 5000 cosmic events:
Track analysis
From left column to right column: full rapidity, central rapidity and unit of rapidity.
Up: angular distribution on phi for full, central and unit of rapidity.
Center: track rapidity distribution for full, central and unit of rapidity.
Down: momentum distribution for full, central and unit of rapidity.
Image: 5000 cosmics track parameters distributions
PYTHIA analysis
Preliminary (old) studies. An update is ongoing.
Multiplicity
Original PYTHIA tunning. PYTHIA version 5.720 ("FORTRAN" version):
non minimumbiased events
 MD for all the energies (from 1 TeV to 14 TeV) /Wiki_JuanCastillo/MultiplicityAnalysis/plots/e_Non_MB/
 1000 events, pbeam = 1000 GeV:
Multiplicity dependencies
Original PYTHIA tunning. PYTHIA version 5.720 ("FORTRAN" version)
 average charged, 10 samples of 1000 events, no error bars: