Centrality Framework

Requirements

Root

https://root.cern/install/

Version 6.20 or newer is recommended. Root with c++17 standard is strongly recommended.

AnalysisTree

Optional. Needed for building AnalysisTree interface.

https://github.com/HeavyIonAnalysis/AnalysisTree

Tag v2.2.7 or newer is required. Both pre-installed AnalysisTree and automatically installed together with Centrality framework can be used (see chapter Installation)

AnalysisTree with c++17 standard is strongly recommended.

Installation

To use pre-installed AnalysisTree set the environment variable: export AnalysisTree_DIR=/path-to-analysistree/install/lib/cmake/AnalysisTree To build AnalysisTree automatically together with Centrality use following cmake keys: -DCentrality_BUNDLED_AT=ON -DCentrality_BUNDLED_AT_VERSION=v2.2.7

Building with c++17 standard is strongly recommended. Use following key: -DCMAKE_CXX_STANDARD=17

cd centrality
mkdir build
cd build
source /path-to-root/thisroot.sh
cmake -DCMAKE_INSTALL_PREFIX=/path-to-centrality/install/ ../
make install

Examples and short description

General

main has 3 arguments:

1. input file name
2. input histo name
3. is input histo is 2D (false or true)

Example how to run it with test input file:

./main ../input/test_input.root hMreco false

or in case of 2D histogram

./main ../input/test_input.root hMEcorr true

To modify options (centrality ranges, detector type, etc) change corresponding lines in file main.cpp:

bf.SetRanges( 10,0,100 );   // number of bins, min, max value

or

bf.SetRanges( {0,10,30,60,100} );  // centrality bins borders with array

bf.IsSpectator(true);  // true if impact parameter b correlated with estimator (spectators eneggy), false - anticorrelated (multiplicity of produced particles) 
std::string outfilename = "test.root";

Minimal example on how to obtain centrality for a given event:

cd macro
root -l TestGetter.C

MC-Glauber fit

Glauber model based fitting procedure is similar to one used by ALICE collaboration:

http://inspirehep.net/record/1215085

Before running fitting procedure one needs to create MC-Glauber input file using software (there is also a test file in input directory)

https://tglaubermc.hepforge.org/downloads/

Fitting function is

$$F(f, \mu, k) = N_A(f) F_{NBD}(\mu, k)$$

where $F_{NDB}$ is Negative Binomial Distribution, $N_A$ is defined for example as:

$$N_A(f) = f N_{part} + (1-f) N_{coll}$$

$N_{part}$ and $N_{coll}$ are parameters from MC-Glauber.

To modify options (input files, fit function, fit ranges, etc) change corresponding lines in file glauber/main.cpp:

glauber has 2 arguments:

1. f value
2. k value

$\mu$ value is fitted automatically.

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