-- -- 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:

  1. AliESDEvent, the full event generated via montecarlo truth (henceforth referred as ESD).
  2. AliMCEvent, the associated montecarlo truth (henceforth called MC).
  3. AliESDtrackCuts, responsible of handling of ESD track cuts.

Then the analysis of the event is performed. This is done via the next algorithm.

  1. A list of tracks passing the cut is obtained from the ESD after the AliESDtrackCuts function GetAcceptedTrack.
  2. Loop over the accepted tracks. Inside, we require:
    1. The existance of the AliESDtrack.
    2. A good AliESDtrack label.
    3. The existance of an AliMCParticle associated with that label.
    4. 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 detector-dependent 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 $p_t$ constrains ($p_t$ cuts, $p_t$ intervals) are applied.

The resulting track information per $p_t$ constrain and pseudorapidity interval is stored in a set of reference histograms: $P(N_{ch})$, $dN/d\eta$, $dN/dp_t$ for the full interval, the forward region and the backward region, and $N_{ch}(F) vs N_{ch}(B)$ 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 $p_t$ constrains ($p_t$ cuts, $p_t$ 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 cross-checks.
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:

  1. To have a AliMCParticle associated.
  2. 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

Forward-Backward 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.

ALERT! 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

Forward-backward correlations

We defined the center of a pseudorapidity interval like the center of the pseudorapidity range covered by it.
The pseudorapidity gap ( ${\eta}$ 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:
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

Forward-backward and forward-forward dispersion

We define:

  • backward-forward dispersion : ${D_{fb}^2=<N_{f}N_{b}>-<N_{f}><N_{b}>}$
  • forward-forward dispersion : ${D_{ff}^2=<N_{f}^2>-<N_{f}>^2}$

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: forward-forward dispersion sample size dependency
Image: backward-forward dispersion sample size dependency

Comments

A first glance to a Pythia equivalent analysis:

Forward-forward and backward-forward dispersion with Pythia:
Forward-forward and backward-forward dispersion with Pythia

Multiplicity Scaling

Preliminary results: old "non-robust" 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

Forward-backward correlations

The software for analyzing ESD FB correlation was run over cosmic data.

Image: forward-backward correlations for cosmics

Multiplicity

  • 5000 cosmic events:
    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 minimum-biased events

  • MD for all the energies (from 1 TeV to 14 TeV) /Wiki_JuanCastillo/MultiplicityAnalysis/plots/e_Non_MB/

  • 1000 events, pbeam = 1000 GeV:
    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:
    average charged, 10 samples of 1000 events, no error bars

Topic attachments
I Attachment Action Size Date Who Comment
average_charged.pngpng average_charged.png manage 6.9 K 2008-03-18 - 17:38 JuanCastillo average charged, 10 samples of 1000 events, no error bars
b_evol_kumar_fit.pngpng b_evol_kumar_fit.png manage 7.5 K 2008-08-14 - 13:53 JuanCastillo correlation stregth b, zero and high rapidity points removed, full statistics
bf_dispersion_sample_size.PNGPNG bf_dispersion_sample_size.PNG manage 18.9 K 2008-08-14 - 14:21 JuanCastillo Backward-forward dispersion sample size
cosmics_5000.pngpng cosmics_5000.png manage 21.5 K 2008-07-24 - 13:32 JuanCastillo 5000 cosmic events distributions
cosmics_Nch_5000.pngpng cosmics_Nch_5000.png manage 8.8 K 2008-07-24 - 13:30 JuanCastillo 5000 cosmic events
distrib_01.pngpng distrib_01.png manage 7.7 K 2008-03-18 - 15:31 JuanCastillo 1000 events, pbeam = 1000 GeV
fb_fits.pngpng fb_fits.png manage 19.6 K 2008-07-29 - 14:20 JuanCastillo forward-backward linear fits
fb_scat_2.pngpng fb_scat_2.png manage 20.4 K 2008-08-14 - 14:10 JuanCastillo FB correlation, full statistics
fb_scat_fit_2.pngpng fb_scat_fit_2.png manage 23.7 K 2008-08-14 - 14:10 JuanCastillo FB correlation fit, full statistics
fb_scatt.pngpng fb_scatt.png manage 15.0 K 2008-07-29 - 14:19 JuanCastillo forward-backward correlations
fb_scatt_cosmics.pngpng fb_scatt_cosmics.png manage 13.0 K 2008-07-30 - 09:23 JuanCastillo forward-backward linear fits for cosmics
ff_dispersion_sample_size.PNGPNG ff_dispersion_sample_size.PNG manage 18.9 K 2008-08-14 - 14:22 JuanCastillo Forward-forward dispersion sample size
ff_fb_dispersion_wP6.PNGPNG ff_fb_dispersion_wP6.PNG manage 10.7 K 2008-08-14 - 14:38 JuanCastillo Forward-forward and backward-forward dispersion with Pythia
md_full_recovered_comparison.pngpng md_full_recovered_comparison.png manage 9.2 K 2008-07-24 - 16:34 JuanCastillo MD full Tree vs Histos -sam statistics-
md_ratio_histo_method_full.pngpng md_ratio_histo_method_full.png manage 11.8 K 2008-07-24 - 16:51 JuanCastillo ratio MC value /ESD value (Full rapidity, same statistic)
sh_central.pngpng sh_central.png manage 13.9 K 2008-07-15 - 09:52 JuanCastillo ESDcut clan fit -Central rapidity
sh_full.pngpng sh_full.png manage 14.0 K 2008-07-15 - 09:51 JuanCastillo ESDcut clan fit -Full rapidity
sh_half.pngpng sh_half.png manage 13.1 K 2008-07-15 - 09:53 JuanCastillo ESDcut clan fit -Half rapidity
sh_unit.pngpng sh_unit.png manage 13.5 K 2008-07-15 - 09:53 JuanCastillo ESDcut clan fit -Unit rapidity
slide_00_01.pngpng slide_00_01.png manage 21.4 K 2008-07-22 - 12:45 JuanCastillo Slice 00-01
Topic revision: r25 - 2009-07-30, JuanCastillo
 
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