Difference: MeerkatPIDResampling (1 vs. 44)

Revision 442019-07-23 - AntonPoluektov

Line: 1 to 1
 
META TOPICPARENT name="PIDCalibPackage"

Unbinned PID resampling using kernel density estimation

Line: 180 to 180
  In addition to the issues mentioned above, there can be a few issues specific to PIDCorr:
  1. The version of simulation (sim08, sim09) used to generate your signal sample should match the version used to create PID response templates. PIDCorr allows one to choose either sim08 or sim09 (but note that not all PID responses are available for both versions).
  2. Typically, MC samples used to create simulation response templates are much smaller than the data calibration samples. Therefore, uncertainty due to the size of calibration samples can be larger with PIDCorr than with PIDGen.
Changed:
<
<
  1. The kinematic coverage of the MC sample can be different from the coverage of the calibration sample, in which case PIDCorr can perform worse than PIDGen.
>
>
  1. The kinematic coverage of the MC sample can be different from the coverage of the calibration sample, in which case PIDCorr can perform worse than PIDGen. This is known to be the case for Run2 low-Pt pions (below 500 MeV), to be fixed.
 
Can I set up PIDGen locally in my university?
It can be done, but will require plenty of space:

Revision 432019-07-22 - AntonPoluektov

Line: 1 to 1
 
META TOPICPARENT name="PIDCalibPackage"

Unbinned PID resampling using kernel density estimation

Line: 74 to 74
 $ python $PIDPERFSCRIPTSROOT/scripts/python/PIDGenUser/PIDGen.py
Changed:
<
<

PID templates available in Urania v7r0 and nightlies

>
>

PID templates available in Urania v8r0 and nightlies

  Available combinations of track and PID variable in Run 1:

Revision 422019-07-12 - AntonPoluektov

Line: 1 to 1
 
META TOPICPARENT name="PIDCalibPackage"

Unbinned PID resampling using kernel density estimation

Line: 97 to 97
 
V3ProbNNp G/C8/C9 G/C8/C9 G/C8/C9    
V3ProbNNpi G/C8/C9 G/C8/C9 G/C8/C9 G/C8 G/C9
Changed:
<
<
Also check the Run 1 control plots for all available combinations of track and PID variable.
>
>
Also check the Run 1 control plots for all available combinations of track and PID variable.
  Available combinations of track and PID variable in Run 2:
Line: 141 to 141
 C8: PIDCorr with sim08 MC, C9: PIDCorr with sim09 MC,
Changed:
<
<
Also check the Run 2 control plots for all available combinations of track and PID variable.
>
>
Also check the Run 2 control plots for all available combinations of track and PID variable.
  Electron templates ending with _Stripping are produced from the Stripping28 samples (updated sWeights calculation using HasBremAdded category). It is recommended to use those instead of the _Brunel versions (direct output from Turcal).

Revision 412019-07-10 - AntonPoluektov

Line: 1 to 1
 
META TOPICPARENT name="PIDCalibPackage"

Unbinned PID resampling using kernel density estimation

Line: 97 to 97
 
V3ProbNNp G/C8/C9 G/C8/C9 G/C8/C9    
V3ProbNNpi G/C8/C9 G/C8/C9 G/C8/C9 G/C8 G/C9
Added:
>
>
Also check the Run 1 control plots for all available combinations of track and PID variable.
 Available combinations of track and PID variable in Run 2:

Variable pi K p e mu
Line: 139 to 141
 C8: PIDCorr with sim08 MC, C9: PIDCorr with sim09 MC,
Added:
>
>
Also check the Run 2 control plots for all available combinations of track and PID variable.
 Electron templates ending with _Stripping are produced from the Stripping28 samples (updated sWeights calculation using HasBremAdded category). It is recommended to use those instead of the _Brunel versions (direct output from Turcal).

K_MC15TuneV1_ProbNNK_Brunel_Mod2 and pi_MC15TuneV1_ProbNNpi_Brunel_Mod2 templates feature improved description of the distribution near ProbNN=0 and are recommended to be used instead of x_MC15TuneV1_ProbNNx_Brunel versions (where a step-like structure can be observed around zero).

Line: 176 to 180
  In addition to the issues mentioned above, there can be a few issues specific to PIDCorr:
  1. The version of simulation (sim08, sim09) used to generate your signal sample should match the version used to create PID response templates. PIDCorr allows one to choose either sim08 or sim09 (but note that not all PID responses are available for both versions).
  2. Typically, MC samples used to create simulation response templates are much smaller than the data calibration samples. Therefore, uncertainty due to the size of calibration samples can be larger with PIDCorr than with PIDGen.
Added:
>
>
  1. The kinematic coverage of the MC sample can be different from the coverage of the calibration sample, in which case PIDCorr can perform worse than PIDGen.
 
Can I set up PIDGen locally in my university?
It can be done, but will require plenty of space:

Revision 402019-07-02 - AntonPoluektov

Line: 1 to 1
 
META TOPICPARENT name="PIDCalibPackage"

Unbinned PID resampling using kernel density estimation

Line: 46 to 46
 To run PIDGen resampling, you need to setup Urania v7r0, copy the example script to a directory with write access (the output ntuple will be created there) and run it. To benefit from the latest updates in the package, it is recommended to run from Urania HEAD nightlies:
Changed:
<
<
$ lb-run -c x86_64-slc6-gcc8-opt --nightly lhcb-head Urania/HEAD bash
>
>
$ lb-run -c x86_64-centos7-gcc8-opt --nightly lhcb-head Urania/HEAD bash
 $ cp $PIDPERFSCRIPTSROOT/scripts/python/PIDGenUser/Examples/Lb2Lcpi/Lb2Lcpi_pidgen.py {your_place} $ cd {your_place} $ python Lb2Lcpi_pidgen.py
Line: 63 to 63
 To run PIDCorr variable transformation, you need to setup Urania, copy the example script to a directory with write access (the output ntuple will be created there) and run it:
Changed:
<
<
$ lb-run -c x86_64-slc6-gcc8-opt --nightly lhcb-head Urania/HEAD bash
>
>
$ lb-run -c x86_64-centos7-gcc8-opt --nightly lhcb-head Urania/HEAD bash
 $ cp $PIDPERFSCRIPTSROOT/scripts/python/PIDGenUser/Examples/Lb2Lcpi/Lb2Lcpi_pidcorr.py {your_place} $ cd {your_place} $ python Lb2Lcpi_pidcorr.py
Line: 194 to 194
 
Nightlies are broken
Sometimes the nightlies in "today's" slot are not compiled properly. In this case, one can try to specify an older slot:
Changed:
<
<
$ lb-run -c x86_64-slc6-gcc8-opt --nightly lhcb-head Mon Urania/master bash
>
>
$ lb-run -c x86_64-centos7-gcc8-opt --nightly lhcb-head Mon Urania/master bash
  (note the "Mon" part, which means take the Monday slot).

-- AntonPoluektov - 2017-05-09

Revision 392019-07-01 - AntonPoluektov

Line: 1 to 1
 
META TOPICPARENT name="PIDCalibPackage"

Unbinned PID resampling using kernel density estimation

Line: 112 to 112
 
CombDLLp_Brunel G/C9 G/C9 G G G
CombDLLp_Stripping       G  
LbLcMu_MC15TuneV1_ProbNNp_Brunel     G    
Changed:
<
<
LbLcPi_MC12TuneV2gt0.05_MC15TuneV     G    
>
>
LbLcPi_MC12TuneV2gt0.05_MC15TuneV1_ProbNNp_Brunel     G    
 
LbLcPi_MC15TuneV1_ProbNNK_Brunel     G/C9    
LbLcPi_MC15TuneV1_ProbNNp_Brunel     G/C9    
LbLcPi_MC15TuneV1_ProbNNpi_Brunel     G/C9    
Added:
>
>
LbLcPi_PIDpgt0_MC15TuneV1_ProbNNp_Brunel     G    
Lc_MC15TuneV1_ProbNNp_Brunel     G    
 
MC15TuneV1_ProbNNKNotpi_Brunel G G      
MC15TuneV1_ProbNNK_Brunel G/C9 G/C9 G G G
MC15TuneV1_ProbNNK_Brunel_Mod2   G/C9      
Line: 130 to 132
 
MC15TuneV1_ProbNNpi_Brunel G/C9 G/C9 G G G
MC15TuneV1_ProbNNpi_Brunel_Mod2 G/C9        
MC15TuneV1_ProbNNpi_Stripping       G  
Added:
>
>
PIDKgt4_MC15TuneV1_ProbNNK_Brunel   G      
PIDKlt2_MC15TuneV1_ProbNNpi_Brunel G        
  G: PIDGen, C8: PIDCorr with sim08 MC,
Line: 141 to 145
  PIDGen templates MC15TuneV1_ProbNNpiNotK_Brunel and MC15TuneV1_ProbNNKNotpi_Brunel provide resampling for ProbNNpi*(1-ProbNNK)* and ProbNNK*(1-ProbNNpi) combinations of variables, respectively (for instance, for tracker-only MC where PIDCorr cannot be used).
Added:
>
>
Templates with the names containing e.g. "PIDKgt4" or similar are for resampling of variables with a certain cut on PID (e.g. PIDKgt4_MC15TuneV1_ProbNNK_Brunel is for the ProbNNK with PIDK>4 cut).
 

Categories

In some cases, separate templates are created for different categories of datasets. There are currently two classes of such categories:

Revision 382019-06-12 - AntonPoluektov

Line: 1 to 1
 
META TOPICPARENT name="PIDCalibPackage"

Unbinned PID resampling using kernel density estimation

Line: 81 to 81
 
Variable pi K p e mu
CombDLLK G/C8/C9 G/C8/C9 G/C8/C9   G
CombDLLK_IsMuon         G
Changed:
<
<
CombDLLe       G/C8  
>
>
CombDLLe G     G/C8 G
 
CombDLLmu G G     G/C9
CombDLLmu_IsMuon         G
CombDLLp G/C8/C9 G/C8/C9 G/C8/C9   G
S20CombDLLe       G  
S20V3ProbNNe       G  
V2ProbNNK G/C8/C9 G/C8/C9 G/C8/C9    
Added:
>
>
V2ProbNNe G        
 
V2ProbNNp G/C8/C9 G/C8/C9 G/C8/C9    
V2ProbNNpi G/C8/C9 G/C8/C9 G/C8/C9    
V3ProbNNK G/C8/C9 G/C8/C9 G/C8/C9 G/C8 G/C9
Line: 102 to 103
 
CombDLLK_Brunel G/C9 G/C9 G G G
CombDLLK_IsMuon_Brunel         G
CombDLLK_Stripping       G  
Changed:
<
<
CombDLLe_Brunel       G  
>
>
CombDLLe_Brunel G     G G
 
CombDLLe_Stripping       G  
CombDLLmu_Brunel G/C9 G/C9   G G
CombDLLmu_IsMuon_Brunel         G
Line: 119 to 120
 
MC15TuneV1_ProbNNK_Brunel G/C9 G/C9 G G G
MC15TuneV1_ProbNNK_Brunel_Mod2   G/C9      
MC15TuneV1_ProbNNK_Stripping       G  
Changed:
<
<
MC15TuneV1_ProbNNe_Brunel       G  
>
>
MC15TuneV1_ProbNNe_Brunel G     G  
 
MC15TuneV1_ProbNNe_Stripping       G  
Changed:
<
<
MC15TuneV1_ProbNNmu_Brunel G/C9 G/C9     G
>
>
MC15TuneV1_ProbNNmu_Brunel G/C9 G/C9   G G
MC15TuneV1_ProbNNmu_Stripping       G  
 
MC15TuneV1_ProbNNp_Brunel G/C9 G/C9 G G  
MC15TuneV1_ProbNNp_Stripping       G  
MC15TuneV1_ProbNNpiNotK_Brunel G G      

Revision 372019-03-01 - DanielJohnson

Line: 1 to 1
 
META TOPICPARENT name="PIDCalibPackage"

Unbinned PID resampling using kernel density estimation

Line: 46 to 46
 To run PIDGen resampling, you need to setup Urania v7r0, copy the example script to a directory with write access (the output ntuple will be created there) and run it. To benefit from the latest updates in the package, it is recommended to run from Urania HEAD nightlies:
Changed:
<
<
$ lb-run -c x86_64-slc6-gcc7-opt --nightly lhcb-head Urania/HEAD bash
>
>
$ lb-run -c x86_64-slc6-gcc8-opt --nightly lhcb-head Urania/HEAD bash
 $ cp $PIDPERFSCRIPTSROOT/scripts/python/PIDGenUser/Examples/Lb2Lcpi/Lb2Lcpi_pidgen.py {your_place} $ cd {your_place} $ python Lb2Lcpi_pidgen.py
Line: 63 to 63
 To run PIDCorr variable transformation, you need to setup Urania, copy the example script to a directory with write access (the output ntuple will be created there) and run it:
Changed:
<
<
$ lb-run -c x86_64-slc6-gcc7-opt --nightly lhcb-head Urania/HEAD bash
>
>
$ lb-run -c x86_64-slc6-gcc8-opt --nightly lhcb-head Urania/HEAD bash
 $ cp $PIDPERFSCRIPTSROOT/scripts/python/PIDGenUser/Examples/Lb2Lcpi/Lb2Lcpi_pidcorr.py {your_place} $ cd {your_place} $ python Lb2Lcpi_pidcorr.py
Line: 186 to 186
 
Nightlies are broken
Sometimes the nightlies in "today's" slot are not compiled properly. In this case, one can try to specify an older slot:
Changed:
<
<
$ lb-run -c x86_64-slc6-gcc7-opt --nightly lhcb-head Mon Urania/master bash
>
>
$ lb-run -c x86_64-slc6-gcc8-opt --nightly lhcb-head Mon Urania/master bash
  (note the "Mon" part, which means take the Monday slot).

-- AntonPoluektov - 2017-05-09

Revision 362018-11-15 - AntonPoluektov

Line: 1 to 1
 
META TOPICPARENT name="PIDCalibPackage"

Unbinned PID resampling using kernel density estimation

Line: 115 to 115
 
LbLcPi_MC15TuneV1_ProbNNK_Brunel     G/C9    
LbLcPi_MC15TuneV1_ProbNNp_Brunel     G/C9    
LbLcPi_MC15TuneV1_ProbNNpi_Brunel     G/C9    
Added:
>
>
MC15TuneV1_ProbNNKNotpi_Brunel G G      
 
MC15TuneV1_ProbNNK_Brunel G/C9 G/C9 G G G
MC15TuneV1_ProbNNK_Brunel_Mod2   G/C9      
MC15TuneV1_ProbNNK_Stripping       G  
Line: 123 to 124
 
MC15TuneV1_ProbNNmu_Brunel G/C9 G/C9     G
MC15TuneV1_ProbNNp_Brunel G/C9 G/C9 G G  
MC15TuneV1_ProbNNp_Stripping       G  
Added:
>
>
MC15TuneV1_ProbNNpiNotK_Brunel G G      
 
MC15TuneV1_ProbNNpi_Brunel G/C9 G/C9 G G G
MC15TuneV1_ProbNNpi_Brunel_Mod2 G/C9        
MC15TuneV1_ProbNNpi_Stripping       G  
Line: 131 to 133
 C8: PIDCorr with sim08 MC, C9: PIDCorr with sim09 MC,
Changed:
<
<
Electron templates ending with _Stripping are produced from the Stripping28 samples (updated sWeights calculation using HasBremAdded category). It is recommended to use those instead of the _Brunel versions (direct output from Turcal).
>
>
Electron templates ending with _Stripping are produced from the Stripping28 samples (updated sWeights calculation using HasBremAdded category). It is recommended to use those instead of the _Brunel versions (direct output from Turcal).
 
Changed:
<
<
K_MC15TuneV1_ProbNNK_Brunel_Mod2 template features improved description of the distribution near ProbNNK=0 and is recommended to be used instead of K_MC15TuneV1_ProbNNK_Brunel (where a step-like structure can be observed around zero).
>
>
K_MC15TuneV1_ProbNNK_Brunel_Mod2 and pi_MC15TuneV1_ProbNNpi_Brunel_Mod2 templates feature improved description of the distribution near ProbNN=0 and are recommended to be used instead of x_MC15TuneV1_ProbNNx_Brunel versions (where a step-like structure can be observed around zero).

PIDGen templates MC15TuneV1_ProbNNpiNotK_Brunel and MC15TuneV1_ProbNNKNotpi_Brunel provide resampling for ProbNNpi*(1-ProbNNK)* and ProbNNK*(1-ProbNNpi) combinations of variables, respectively (for instance, for tracker-only MC where PIDCorr cannot be used).

 

Categories

Revision 352018-09-28 - AntonPoluektov

Line: 1 to 1
 
META TOPICPARENT name="PIDCalibPackage"

Unbinned PID resampling using kernel density estimation

Line: 43 to 43
 

Running PID resampling (PIDGen)

Changed:
<
<
To run PIDGen resampling, you need to setup Urania v7r0, copy the example script to a directory with write access (the output ntuple will be created there) and run it. To benefit from the latest updates in the package, it is recommended to run from Urania prerelease nightlies:
>
>
To run PIDGen resampling, you need to setup Urania v7r0, copy the example script to a directory with write access (the output ntuple will be created there) and run it. To benefit from the latest updates in the package, it is recommended to run from Urania HEAD nightlies:
 
Changed:
<
<
$ lb-run --nightly lhcb-prerelease Urania/master bash
>
>
$ lb-run -c x86_64-slc6-gcc7-opt --nightly lhcb-head Urania/HEAD bash
 $ cp $PIDPERFSCRIPTSROOT/scripts/python/PIDGenUser/Examples/Lb2Lcpi/Lb2Lcpi_pidgen.py {your_place} $ cd {your_place} $ python Lb2Lcpi_pidgen.py
Line: 63 to 63
 To run PIDCorr variable transformation, you need to setup Urania, copy the example script to a directory with write access (the output ntuple will be created there) and run it:
Changed:
<
<
$ lb-run --nightly lhcb-prerelease Urania/master bash
>
>
$ lb-run -c x86_64-slc6-gcc7-opt --nightly lhcb-head Urania/HEAD bash
 $ cp $PIDPERFSCRIPTSROOT/scripts/python/PIDGenUser/Examples/Lb2Lcpi/Lb2Lcpi_pidcorr.py {your_place} $ cd {your_place} $ python Lb2Lcpi_pidcorr.py
Line: 180 to 180
 
Distribution of ProbNNp for protons in Run2 doesn't look good
Run2 uses Lambda0 calibration sample which is biased for tracks coming from the PV. See this presentation. The solution is to either use the variable p_LbLcPi_MC15TuneV1_ProbNNp_Brunel (coming from the low-stats Lb->LcPi sample) or wait for inclusive Lc sample to be produced.
Changed:
<
<
Prerelease nightlies are broken
>
>
Nightlies are broken
  Sometimes the nightlies in "today's" slot are not compiled properly. In this case, one can try to specify an older slot:
Changed:
<
<
$ lb-run --nightly lhcb-prerelease Mon Urania/master bash (note the "Mon" part, which means take the Monday slot). Alternatively, one can try "lhcb-head": $ lb-run --nightly lhcb-head Urania/HEAD bash
>
>
$ lb-run -c x86_64-slc6-gcc7-opt --nightly lhcb-head Mon Urania/master bash (note the "Mon" part, which means take the Monday slot).
  -- AntonPoluektov - 2017-05-09 \ No newline at end of file

Revision 342018-09-24 - AntonPoluektov

Line: 1 to 1
 
META TOPICPARENT name="PIDCalibPackage"

Unbinned PID resampling using kernel density estimation

Line: 101 to 101
 
Variable pi K p e mu
CombDLLK_Brunel G/C9 G/C9 G G G
CombDLLK_IsMuon_Brunel         G
Added:
>
>
CombDLLK_Stripping       G  
 
CombDLLe_Brunel       G  
Added:
>
>
CombDLLe_Stripping       G  
 
CombDLLmu_Brunel G/C9 G/C9   G G
CombDLLmu_IsMuon_Brunel         G
Added:
>
>
CombDLLmu_Stripping       G  
 
CombDLLmu_isMuon_Brunel         G
CombDLLp_Brunel G/C9 G/C9 G G G
Added:
>
>
CombDLLp_Stripping       G  
 
LbLcMu_MC15TuneV1_ProbNNp_Brunel     G    
Changed:
<
<
LbLcPi_MC15TuneV1_ProbNNp_Brunel     G    
>
>
LbLcPi_MC12TuneV2gt0.05_MC15TuneV     G    
LbLcPi_MC15TuneV1_ProbNNK_Brunel     G/C9    
LbLcPi_MC15TuneV1_ProbNNp_Brunel     G/C9    
LbLcPi_MC15TuneV1_ProbNNpi_Brunel     G/C9    
 
MC15TuneV1_ProbNNK_Brunel G/C9 G/C9 G G G
MC15TuneV1_ProbNNK_Brunel_Mod2   G/C9      
Added:
>
>
MC15TuneV1_ProbNNK_Stripping       G  
 
MC15TuneV1_ProbNNe_Brunel       G  
Added:
>
>
MC15TuneV1_ProbNNe_Stripping       G  
 
MC15TuneV1_ProbNNmu_Brunel G/C9 G/C9     G
MC15TuneV1_ProbNNp_Brunel G/C9 G/C9 G G  
Deleted:
<
<
MC15TuneV1_ProbNNpi_Brunel G/C9 G/C9 G G G
CombDLLK_Stripping       G  
CombDLLe_Stripping       G  
CombDLLmu_Stripping       G  
CombDLLp_Stripping       G  
MC15TuneV1_ProbNNK_Stripping       G  
MC15TuneV1_ProbNNe_Stripping       G  
 
MC15TuneV1_ProbNNp_Stripping       G  
Added:
>
>
MC15TuneV1_ProbNNpi_Brunel G/C9 G/C9 G G G
MC15TuneV1_ProbNNpi_Brunel_Mod2 G/C9        
 
MC15TuneV1_ProbNNpi_Stripping       G  

G: PIDGen,

Revision 332018-07-05 - AntonPoluektov

Line: 1 to 1
 
META TOPICPARENT name="PIDCalibPackage"

Unbinned PID resampling using kernel density estimation

Line: 84 to 84
 
CombDLLe       G/C8  
CombDLLmu G G     G/C9
CombDLLmu_IsMuon         G
Changed:
<
<
CombDLLp G/C8/C9 G/C8/C9 G/C8/C9    
>
>
CombDLLp G/C8/C9 G/C8/C9 G/C8/C9   G
 
S20CombDLLe       G  
S20V3ProbNNe       G  
V2ProbNNK G/C8/C9 G/C8/C9 G/C8/C9    
Line: 105 to 105
 
CombDLLmu_Brunel G/C9 G/C9   G G
CombDLLmu_IsMuon_Brunel         G
CombDLLmu_isMuon_Brunel         G
Changed:
<
<
CombDLLp_Brunel G/C9 G/C9 G G  
>
>
CombDLLp_Brunel G/C9 G/C9 G G G
 
LbLcMu_MC15TuneV1_ProbNNp_Brunel     G    
LbLcPi_MC15TuneV1_ProbNNp_Brunel     G    
MC15TuneV1_ProbNNK_Brunel G/C9 G/C9 G G G

Revision 322018-05-20 - AntonPoluektov

Line: 1 to 1
 
META TOPICPARENT name="PIDCalibPackage"

Unbinned PID resampling using kernel density estimation

Line: 11 to 11
 
  • Transformation of PID variables, where the PID variables from the simulation are transformed in such a way that they are distributed
Changed:
<
<
as in data. (This is only available for Run1 for the moment).
>
>
as in data.
  Like in PIDCalib, the PID correction is a function of track kinematics (momentum and pseudorapidity) and event multiplicity (number of tracks). Unlike PIDCalib, the correction is done with an unbinned approach, where the calibration PDFs in four dimensions (PID, Pt, eta, Ntrack) are described by a kernel density estimation procedure using the Meerkat library.

Revision 312018-05-04 - AntonPoluektov

Line: 1 to 1
 
META TOPICPARENT name="PIDCalibPackage"

Unbinned PID resampling using kernel density estimation

Line: 176 to 176
 
Distribution of ProbNNp for protons in Run2 doesn't look good
Run2 uses Lambda0 calibration sample which is biased for tracks coming from the PV. See this presentation. The solution is to either use the variable p_LbLcPi_MC15TuneV1_ProbNNp_Brunel (coming from the low-stats Lb->LcPi sample) or wait for inclusive Lc sample to be produced.
Added:
>
>
Prerelease nightlies are broken
Sometimes the nightlies in "today's" slot are not compiled properly. In this case, one can try to specify an older slot: $ lb-run --nightly lhcb-prerelease Mon Urania/master bash (note the "Mon" part, which means take the Monday slot). Alternatively, one can try "lhcb-head": $ lb-run --nightly lhcb-head Urania/HEAD bash
 -- AntonPoluektov - 2017-05-09 \ No newline at end of file

Revision 302018-04-30 - AntonPoluektov

Line: 1 to 1
 
META TOPICPARENT name="PIDCalibPackage"

Unbinned PID resampling using kernel density estimation

Line: 107 to 107
 
CombDLLmu_isMuon_Brunel         G
CombDLLp_Brunel G/C9 G/C9 G G  
LbLcMu_MC15TuneV1_ProbNNp_Brunel     G    
Deleted:
<
<
LbLcPi_MC12TuneV2gt0.05_MC15TuneV     G    
 
LbLcPi_MC15TuneV1_ProbNNp_Brunel     G    
MC15TuneV1_ProbNNK_Brunel G/C9 G/C9 G G G
MC15TuneV1_ProbNNK_Brunel_Mod2   G/C9      

Revision 292018-04-29 - AntonPoluektov

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META TOPICPARENT name="PIDCalibPackage"

Unbinned PID resampling using kernel density estimation

Line: 69 to 69
 $ python Lb2Lcpi_pidcorr.py
Changed:
<
<
The result of PIDCorr is dependent on the version of MC used to generate your signal (sim08 or sim09)! Only Run 1 PID configurations are available for the moment. The complete list of PID configurations available can be obtained by running PIDCorr.py without arguments:
>
>
The result of PIDCorr is dependent on the version of MC used to generate your signal (sim08 or sim09)! The complete list of PID configurations available can be obtained by running PIDCorr.py without arguments:
 
$ python $PIDPERFSCRIPTSROOT/scripts/python/PIDGenUser/PIDGen.py

Revision 282018-04-20 - AntonPoluektov

Line: 1 to 1
 
META TOPICPARENT name="PIDCalibPackage"

Unbinned PID resampling using kernel density estimation

Line: 79 to 79
 Available combinations of track and PID variable in Run 1:

Variable pi K p e mu
Changed:
<
<
CombDLLK G/C8/C9 G/C8/C9 G/C8/C9   G
>
>
CombDLLK G/C8/C9 G/C8/C9 G/C8/C9   G
CombDLLK_IsMuon         G
 
CombDLLe       G/C8  
Changed:
<
<
CombDLLmu G G     G/C9
>
>
CombDLLmu G G     G/C9
CombDLLmu_IsMuon         G
 
CombDLLp G/C8/C9 G/C8/C9 G/C8/C9    
S20CombDLLe       G  
S20V3ProbNNe       G  
V2ProbNNK G/C8/C9 G/C8/C9 G/C8/C9    
V2ProbNNp G/C8/C9 G/C8/C9 G/C8/C9    
V2ProbNNpi G/C8/C9 G/C8/C9 G/C8/C9    
Changed:
<
<
V3ProbNNK G/C8/C9 G/C8/C9 G/C8/C9 G/C8 G/C9
>
>
V3ProbNNK G/C8/C9 G/C8/C9 G/C8/C9 G/C8 G/C9
 
V3ProbNNe       G/C8  
Changed:
<
<
V3ProbNNmu G G     G/C9
>
>
V3ProbNNmu G G     G/C9
 
V3ProbNNp G/C8/C9 G/C8/C9 G/C8/C9    
Changed:
<
<
V3ProbNNpi G/C8/C9 G/C8/C9 G/C8/C9 G/C8 G/C9
>
>
V3ProbNNpi G/C8/C9 G/C8/C9 G/C8/C9 G/C8 G/C9
  Available combinations of track and PID variable in Run 2:
Line: 130 to 132
  K_MC15TuneV1_ProbNNK_Brunel_Mod2 template features improved description of the distribution near ProbNNK=0 and is recommended to be used instead of K_MC15TuneV1_ProbNNK_Brunel (where a step-like structure can be observed around zero).
Added:
>
>

Categories

In some cases, separate templates are created for different categories of datasets. There are currently two classes of such categories:

  • Templates from datasets split by track charge. These are available for most pion, kaon and proton responses and can be accessed by appending "_Plus" or "_Minus" to the dataset name, e.g. "MagDown_2012_Plus".

  • Templates from datasets split by "BremAdded" category for electrons. Can be accessed by appending "_Brem" or "_NoBrem" to the dataset name, e.g. "MagDown_2012_Brem".
 

Frequently asked questions

I don't have the Eta (pseudorapidity) variable in my simulation ntuple.

Revision 272018-04-16 - AntonPoluektov

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META TOPICPARENT name="PIDCalibPackage"

Unbinned PID resampling using kernel density estimation

Line: 97 to 97
 Available combinations of track and PID variable in Run 2:

Variable pi K p e mu
Changed:
<
<
CombDLLK_Brunel G G G G G
>
>
CombDLLK_Brunel G/C9 G/C9 G G G
CombDLLK_IsMuon_Brunel         G
 
CombDLLe_Brunel       G  
Changed:
<
<
CombDLLmu_Brunel G G   G G
CombDLLp_Brunel G G G G  
CombDLLp_Stripping       G  
>
>
CombDLLmu_Brunel G/C9 G/C9   G G
CombDLLmu_IsMuon_Brunel         G
CombDLLmu_isMuon_Brunel         G
CombDLLp_Brunel G/C9 G/C9 G G  
 
LbLcMu_MC15TuneV1_ProbNNp_Brunel     G    
LbLcPi_MC12TuneV2gt0.05_MC15TuneV     G    
LbLcPi_MC15TuneV1_ProbNNp_Brunel     G    
Changed:
<
<
MC15TuneV1_ProbNNK_Brunel G G G G G
MC15TuneV1_ProbNNK_Brunel_Mod2   G      
>
>
MC15TuneV1_ProbNNK_Brunel G/C9 G/C9 G G G
MC15TuneV1_ProbNNK_Brunel_Mod2   G/C9      
 
MC15TuneV1_ProbNNe_Brunel       G  
Changed:
<
<
MC15TuneV1_ProbNNmu_Brunel G G     G
MC15TuneV1_ProbNNp_Brunel G G G G  
MC15TuneV1_ProbNNpi_Brunel G G G G G
>
>
MC15TuneV1_ProbNNmu_Brunel G/C9 G/C9     G
MC15TuneV1_ProbNNp_Brunel G/C9 G/C9 G G  
MC15TuneV1_ProbNNpi_Brunel G/C9 G/C9 G G G
 
CombDLLK_Stripping       G  
CombDLLe_Stripping       G  
CombDLLmu_Stripping       G  
Added:
>
>
CombDLLp_Stripping       G  
 
MC15TuneV1_ProbNNK_Stripping       G  
MC15TuneV1_ProbNNe_Stripping       G  
MC15TuneV1_ProbNNp_Stripping       G  

Revision 262018-03-14 - AntonPoluektov

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META TOPICPARENT name="PIDCalibPackage"

Unbinned PID resampling using kernel density estimation

Line: 43 to 43
 

Running PID resampling (PIDGen)

Changed:
<
<
To run PIDGen resampling, you need to setup Urania v7r0, copy the example script to a directory with write access (the output ntuple will be created there) and run it:
>
>
To run PIDGen resampling, you need to setup Urania v7r0, copy the example script to a directory with write access (the output ntuple will be created there) and run it. To benefit from the latest updates in the package, it is recommended to run from Urania prerelease nightlies:
 
Changed:
<
<
$ lb-run Urania/v7r0 bash
>
>
$ lb-run --nightly lhcb-prerelease Urania/master bash
 $ cp $PIDPERFSCRIPTSROOT/scripts/python/PIDGenUser/Examples/Lb2Lcpi/Lb2Lcpi_pidgen.py {your_place} $ cd {your_place} $ python Lb2Lcpi_pidgen.py
Line: 58 to 58
  Note that Run 1 and Run 2 PID configurations have different names!
Deleted:
<
<
For most recent PID configurations which were not added yet to Urania release, you might need to use Urania prerelease nightlies instead:
$ LbLogin -c x86_64-slc6-gcc62-opt
$ lb-run --nightly-cvmfs --nightly lhcb-prerelease Urania/master bash
 

Running PID variable transformation (PIDCorr)

Changed:
<
<
To run PIDCorr variable transformation, you need to setup Urania v7r0, copy the example script to a directory with write access (the output ntuple will be created there) and run it:
>
>
To run PIDCorr variable transformation, you need to setup Urania, copy the example script to a directory with write access (the output ntuple will be created there) and run it:
 
Changed:
<
<
$ lb-run Urania/v7r0 bash
>
>
$ lb-run --nightly lhcb-prerelease Urania/master bash
 $ cp $PIDPERFSCRIPTSROOT/scripts/python/PIDGenUser/Examples/Lb2Lcpi/Lb2Lcpi_pidcorr.py {your_place} $ cd {your_place} $ python Lb2Lcpi_pidcorr.py
Changed:
<
<
The result of PIDCorr is dependent on the version of MC used to generate your signal! Only Sim08 Run 1 PID configurations are available for the moment. The complete list of PID configurations available can be obtained by running PIDCorr.py without arguments:
>
>
The result of PIDCorr is dependent on the version of MC used to generate your signal (sim08 or sim09)! Only Run 1 PID configurations are available for the moment. The complete list of PID configurations available can be obtained by running PIDCorr.py without arguments:
 
$ python $PIDPERFSCRIPTSROOT/scripts/python/PIDGenUser/PIDGen.py
Line: 103 to 97
 Available combinations of track and PID variable in Run 2:

Variable pi K p e mu
Changed:
<
<
CombDLLK_Brunel G G G G G
CombDLLe_Brunel       G  
CombDLLp_Brunel G G G G  
CombDLLmu_Brunel G G   G G
MC15TuneV1_ProbNNK_Brunel G G G G G
MC15TuneV1_ProbNNmu_Brunel G G     G
MC15TuneV1_ProbNNe_Brunel       G  
MC15TuneV1_ProbNNp_Brunel G G G G  
MC15TuneV1_ProbNNpi_Brunel G G G G G
>
>
CombDLLK_Brunel G G G G G
CombDLLe_Brunel       G  
CombDLLmu_Brunel G G   G G
CombDLLp_Brunel G G G G  
CombDLLp_Stripping       G  
LbLcMu_MC15TuneV1_ProbNNp_Brunel     G    
LbLcPi_MC12TuneV2gt0.05_MC15TuneV     G    
LbLcPi_MC15TuneV1_ProbNNp_Brunel     G    
MC15TuneV1_ProbNNK_Brunel G G G G G
MC15TuneV1_ProbNNK_Brunel_Mod2   G      
MC15TuneV1_ProbNNe_Brunel       G  
MC15TuneV1_ProbNNmu_Brunel G G     G
MC15TuneV1_ProbNNp_Brunel G G G G  
MC15TuneV1_ProbNNpi_Brunel G G G G G
CombDLLK_Stripping       G  
CombDLLe_Stripping       G  
CombDLLmu_Stripping       G  
MC15TuneV1_ProbNNK_Stripping       G  
MC15TuneV1_ProbNNe_Stripping       G  
MC15TuneV1_ProbNNp_Stripping       G  
MC15TuneV1_ProbNNpi_Stripping       G  
  G: PIDGen, C8: PIDCorr with sim08 MC, C9: PIDCorr with sim09 MC,
Changed:
<
<
Bold : only available in the nightlies.
>
>
Electron templates ending with _Stripping are produced from the Stripping28 samples (updated sWeights calculation using HasBremAdded category). It is recommended to use those instead of the _Brunel versions (direct output from Turcal).

K_MC15TuneV1_ProbNNK_Brunel_Mod2 template features improved description of the distribution near ProbNNK=0 and is recommended to be used instead of K_MC15TuneV1_ProbNNK_Brunel (where a step-like structure can be observed around zero).

 

Frequently asked questions

Line: 132 to 141
 
The result of PIDGen does not match data
  1. Check that there are no "hidden" PID cuts in the simulated or data samples (e.g. in the stripping).
  2. Check that kinematic distributions of the simulated sample match those in data. It might be necessary to e.g. reweigh MC events with the Dalitz plot distributions observed in data.
Changed:
<
<
  1. Usually, one needs to either reweigh MC such that the number of tracks distribution matches that in data (or at least rescale the Ntracks variable by multiplying it by 1.10-1.15 such that it roughly resembles data distribution).
>
>
  1. Usually, one needs to either reweigh MC such that the number of tracks distribution matches that in data (or at least rescale the Ntracks variable by multiplying it by 1.10-1.15 such that it roughly resembles data distribution). If the nTracks is the only variable you need to correct, you can use the command line option in PIDGen/PIDCorr.py scripts such as "--ntrscale 1.15" to do this internally.
 
  1. If data distribution is obtained with sWeights, make sure there are no biases in the distribution (e.g. correlations between reweighting and PID variables, or backgrounds that peak in the reweighting variable).

The result of PIDCorr does not match data
Line: 150 to 159
  e_CombDLLe and e_VxProbNNe variables correspond to Stripping 21, e_S20CombDLLe and e_S20VxProbNNe to Stripping 20.

I use weights to match simulation kinematics to data. Should I pass the weights to PIDGen/PIDCorr?
Changed:
<
<
You don't need to pass your weights to PIDCorr. PIDCorr ensures that for each particular Pt, Eta, Ntracks the PID distribution will match data. Thus if your Pt, Eta, Ntracks distribution matches data, PID response will be correct. How exactly you make Pt, Eta, Ntracks match data is up to you. If you use event-by-event weighs, you will just correct each singe event with PIDCorr and then use that event weight later to e.g. compare with your data calibration sample of BDT tuning.
>
>
You don't need to pass your weights to PIDGen/PIDCorr. PIDGen/PIDCorr ensures that for each particular Pt, Eta, Ntracks the PID distribution will match data. Thus if your Pt, Eta, Ntracks distribution matches data, PID response will be correct. How exactly you make Pt, Eta, Ntracks match data is up to you. If you use event-by-event weighs, you will just correct each single event with PIDGen/PIDCorr and then use that event weight later to e.g. compare with your data calibration sample of BDT tuning.
 
Distribution of ProbNNp for protons in Run2 doesn't look good
Run2 uses Lambda0 calibration sample which is biased for tracks coming from the PV. See this presentation. The solution is to either use the variable p_LbLcPi_MC15TuneV1_ProbNNp_Brunel (coming from the low-stats Lb->LcPi sample) or wait for inclusive Lc sample to be produced.

Revision 252018-01-23 - AntonPoluektov

Line: 1 to 1
 
META TOPICPARENT name="PIDCalibPackage"

Unbinned PID resampling using kernel density estimation

Line: 103 to 103
 Available combinations of track and PID variable in Run 2:

Variable pi K p e mu
Changed:
<
<
CombDLLK_Brunel G G G   G
CombDLLp_Brunel G G G    
CombDLLmu_Brunel G G     G
MC15TuneV1_ProbNNK_Brunel G G G   G
>
>
CombDLLK_Brunel G G G G G
CombDLLe_Brunel       G  
CombDLLp_Brunel G G G G  
CombDLLmu_Brunel G G   G G
MC15TuneV1_ProbNNK_Brunel G G G G G
 
MC15TuneV1_ProbNNmu_Brunel G G     G
MC15TuneV1_ProbNNe_Brunel       G  
Changed:
<
<
MC15TuneV1_ProbNNp_Brunel G G G    
MC15TuneV1_ProbNNpi_Brunel G G G   G
>
>
MC15TuneV1_ProbNNp_Brunel G G G G  
MC15TuneV1_ProbNNpi_Brunel G G G G G
  G: PIDGen, C8: PIDCorr with sim08 MC,

Revision 242017-12-12 - AntonPoluektov

Line: 1 to 1
 
META TOPICPARENT name="PIDCalibPackage"

Unbinned PID resampling using kernel density estimation

Line: 103 to 103
 Available combinations of track and PID variable in Run 2:

Variable pi K p e mu
Changed:
<
<
CombDLLK_Brunel G G G    
>
>
CombDLLK_Brunel G G G   G
 
CombDLLp_Brunel G G G    
Changed:
<
<
MC15TuneV1_ProbNNK_Brunel G G G    
MC15TuneV1_ProbNNmu_Brunel         G
>
>
CombDLLmu_Brunel G G     G
MC15TuneV1_ProbNNK_Brunel G G G   G
MC15TuneV1_ProbNNmu_Brunel G G     G
 
MC15TuneV1_ProbNNe_Brunel       G  
MC15TuneV1_ProbNNp_Brunel G G G    
Changed:
<
<
MC15TuneV1_ProbNNpi_Brunel G G G    
>
>
MC15TuneV1_ProbNNpi_Brunel G G G   G
  G: PIDGen, C8: PIDCorr with sim08 MC,

Revision 232017-10-11 - RafaelCoutinho

Line: 1 to 1
 
META TOPICPARENT name="PIDCalibPackage"

Unbinned PID resampling using kernel density estimation

Revision 222017-10-03 - AntonPoluektov

Line: 1 to 1
 
META TOPICPARENT name="PIDCalibPackage"

Unbinned PID resampling using kernel density estimation

Line: 150 to 150
 
I use weights to match simulation kinematics to data. Should I pass the weights to PIDGen/PIDCorr?
You don't need to pass your weights to PIDCorr. PIDCorr ensures that for each particular Pt, Eta, Ntracks the PID distribution will match data. Thus if your Pt, Eta, Ntracks distribution matches data, PID response will be correct. How exactly you make Pt, Eta, Ntracks match data is up to you. If you use event-by-event weighs, you will just correct each singe event with PIDCorr and then use that event weight later to e.g. compare with your data calibration sample of BDT tuning.
Added:
>
>
Distribution of ProbNNp for protons in Run2 doesn't look good
Run2 uses Lambda0 calibration sample which is biased for tracks coming from the PV. See this presentation. The solution is to either use the variable p_LbLcPi_MC15TuneV1_ProbNNp_Brunel (coming from the low-stats Lb->LcPi sample) or wait for inclusive Lc sample to be produced.
 -- AntonPoluektov - 2017-05-09 \ No newline at end of file

Revision 212017-10-01 - AntonPoluektov

Line: 1 to 1
 
META TOPICPARENT name="PIDCalibPackage"

Unbinned PID resampling using kernel density estimation

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CombDLLp_Brunel G G G    
MC15TuneV1_ProbNNK_Brunel G G G    
MC15TuneV1_ProbNNmu_Brunel         G
Added:
>
>
MC15TuneV1_ProbNNe_Brunel       G  
 
MC15TuneV1_ProbNNp_Brunel G G G    
MC15TuneV1_ProbNNpi_Brunel G G G    

Revision 202017-09-21 - AntonPoluektov

Line: 1 to 1
 
META TOPICPARENT name="PIDCalibPackage"

Unbinned PID resampling using kernel density estimation

Line: 60 to 60
  For most recent PID configurations which were not added yet to Urania release, you might need to use Urania prerelease nightlies instead:
Added:
>
>
$ LbLogin -c x86_64-slc6-gcc62-opt
 $ lb-run --nightly-cvmfs --nightly lhcb-prerelease Urania/master bash
Line: 79 to 80
 $ python $PIDPERFSCRIPTSROOT/scripts/python/PIDGenUser/PIDGen.py
Changed:
<
<

PID templates available in Urania v7r0

>
>

PID templates available in Urania v7r0 and nightlies

  Available combinations of track and PID variable in Run 1:

Revision 192017-09-21 - AntonPoluektov

Line: 1 to 1
 
META TOPICPARENT name="PIDCalibPackage"

Unbinned PID resampling using kernel density estimation

Line: 84 to 84
 Available combinations of track and PID variable in Run 1:

Variable pi K p e mu
Changed:
<
<
CombDLLK G/C8/C9 G/C8/C9 G/C8/C9   G
>
>
CombDLLK G/C8/C9 G/C8/C9 G/C8/C9   G
 
CombDLLe       G/C8  
Changed:
<
<
CombDLLmu G G     G
>
>
CombDLLmu G G     G/C9
 
CombDLLp G/C8/C9 G/C8/C9 G/C8/C9    
S20CombDLLe       G  
S20V3ProbNNe       G  
V2ProbNNK G/C8/C9 G/C8/C9 G/C8/C9    
V2ProbNNp G/C8/C9 G/C8/C9 G/C8/C9    
V2ProbNNpi G/C8/C9 G/C8/C9 G/C8/C9    
Changed:
<
<
V3ProbNNK G/C8/C9 G/C8/C9 G/C8/C9 G/C8 G
>
>
V3ProbNNK G/C8/C9 G/C8/C9 G/C8/C9 G/C8 G/C9
 
V3ProbNNe       G/C8  
Changed:
<
<
V3ProbNNmu G G     G
>
>
V3ProbNNmu G G     G/C9
 
V3ProbNNp G/C8/C9 G/C8/C9 G/C8/C9    
Changed:
<
<
V3ProbNNpi G/C8/C9 G/C8/C9 G/C8/C9 G/C8 G
>
>
V3ProbNNpi G/C8/C9 G/C8/C9 G/C8/C9 G/C8 G/C9
  Available combinations of track and PID variable in Run 2:
Line: 105 to 105
 
CombDLLK_Brunel G G G    
CombDLLp_Brunel G G G    
MC15TuneV1_ProbNNK_Brunel G G G    
Changed:
<
<
MC15TuneV1_ProbNNmu_Brunel         G
>
>
MC15TuneV1_ProbNNmu_Brunel         G
 
MC15TuneV1_ProbNNp_Brunel G G G    
MC15TuneV1_ProbNNpi_Brunel G G G    

Revision 182017-09-12 - AntonPoluektov

Line: 1 to 1
 
META TOPICPARENT name="PIDCalibPackage"

Unbinned PID resampling using kernel density estimation

Line: 105 to 105
 
CombDLLK_Brunel G G G    
CombDLLp_Brunel G G G    
MC15TuneV1_ProbNNK_Brunel G G G    
Changed:
<
<
MC15TuneV1_ProbNNmu_Brunel         G
>
>
MC15TuneV1_ProbNNmu_Brunel         G
 
MC15TuneV1_ProbNNp_Brunel G G G    
MC15TuneV1_ProbNNpi_Brunel G G G    

Revision 172017-09-06 - AntonPoluektov

Line: 1 to 1
 
META TOPICPARENT name="PIDCalibPackage"

Unbinned PID resampling using kernel density estimation

Line: 84 to 84
 Available combinations of track and PID variable in Run 1:

Variable pi K p e mu
Changed:
<
<
CombDLLK G/C8/C9 G/C8/C9 G/C8/C9   G
>
>
CombDLLK G/C8/C9 G/C8/C9 G/C8/C9   G
 
CombDLLe       G/C8  
CombDLLmu G G     G
CombDLLp G/C8/C9 G/C8/C9 G/C8/C9    
Line: 95 to 95
 
V2ProbNNpi G/C8/C9 G/C8/C9 G/C8/C9    
V3ProbNNK G/C8/C9 G/C8/C9 G/C8/C9 G/C8 G
V3ProbNNe       G/C8  
Changed:
<
<
V3ProbNNmu G   G   G
>
>
V3ProbNNmu G G     G
 
V3ProbNNp G/C8/C9 G/C8/C9 G/C8/C9    
V3ProbNNpi G/C8/C9 G/C8/C9 G/C8/C9 G/C8 G
Line: 112 to 112
 G: PIDGen, C8: PIDCorr with sim08 MC, C9: PIDCorr with sim09 MC,
Changed:
<
<
Italic: only available in the nightlies.
>
>
Bold : only available in the nightlies.
 

Frequently asked questions

Revision 162017-07-24 - AntonPoluektov

Line: 1 to 1
 
META TOPICPARENT name="PIDCalibPackage"

Unbinned PID resampling using kernel density estimation

Line: 84 to 84
 Available combinations of track and PID variable in Run 1:

Variable pi K p e mu
Changed:
<
<
CombDLLK G/C8/C9 G/C8/C9 G/C8/C9    
>
>
CombDLLK G/C8/C9 G/C8/C9 G/C8/C9   G
 
CombDLLe       G/C8  
CombDLLmu G G     G
CombDLLp G/C8/C9 G/C8/C9 G/C8/C9    
Line: 95 to 95
 
V2ProbNNpi G/C8/C9 G/C8/C9 G/C8/C9    
V3ProbNNK G/C8/C9 G/C8/C9 G/C8/C9 G/C8 G
V3ProbNNe       G/C8  
Changed:
<
<
V3ProbNNmu G       G
>
>
V3ProbNNmu G   G   G
 
V3ProbNNp G/C8/C9 G/C8/C9 G/C8/C9    
V3ProbNNpi G/C8/C9 G/C8/C9 G/C8/C9 G/C8 G
Line: 105 to 105
 
CombDLLK_Brunel G G G    
CombDLLp_Brunel G G G    
MC15TuneV1_ProbNNK_Brunel G G G    
Added:
>
>
MC15TuneV1_ProbNNmu_Brunel         G
 
MC15TuneV1_ProbNNp_Brunel G G G    
MC15TuneV1_ProbNNpi_Brunel G G G    

G: PIDGen, C8: PIDCorr with sim08 MC, C9: PIDCorr with sim09 MC,

Added:
>
>
Italic: only available in the nightlies.
 

Frequently asked questions

Line: 118 to 120
  You can use PIDGen (PIDCorr) with either Pt and Eta variables (-p <ptvar> -e <etavar>), or Pt and P variables (-p <ptvar> -q <pvar>) taken from the ntuple. In the latter case Eta will be calculated from P and Pt in the script.

The PID response I need is missing in PIDGen
Changed:
<
<
Probably nobody asked for it before, please let us know.
>
>
Probably nobody asked for it before, please let us know. But check the Urania nightlies first, it might happen that the response you need was just added recently.
 
The PID response I need is present in PIDGen but not in PIDCorr
Probably nobody asked for it before, please let us know. But PIDCorr relies on MC samples as well, so you might need to wait until the sample is generated before the PID response you need becomes available.

Revision 152017-07-06 - AntonPoluektov

Line: 1 to 1
 
META TOPICPARENT name="PIDCalibPackage"

Unbinned PID resampling using kernel density estimation

Line: 143 to 143
 
What stripping version is used for Run1 electron samples?
e_CombDLLe and e_VxProbNNe variables correspond to Stripping 21, e_S20CombDLLe and e_S20VxProbNNe to Stripping 20.
Added:
>
>
I use weights to match simulation kinematics to data. Should I pass the weights to PIDGen/PIDCorr?
You don't need to pass your weights to PIDCorr. PIDCorr ensures that for each particular Pt, Eta, Ntracks the PID distribution will match data. Thus if your Pt, Eta, Ntracks distribution matches data, PID response will be correct. How exactly you make Pt, Eta, Ntracks match data is up to you. If you use event-by-event weighs, you will just correct each singe event with PIDCorr and then use that event weight later to e.g. compare with your data calibration sample of BDT tuning.
 -- AntonPoluektov - 2017-05-09 \ No newline at end of file

Revision 142017-06-15 - AntonPoluektov

Line: 1 to 1
 
META TOPICPARENT name="PIDCalibPackage"

Unbinned PID resampling using kernel density estimation

Line: 43 to 43
 

Running PID resampling (PIDGen)

Changed:
<
<
To run PIDGen resampling, you need to setup Urania nightly, copy the example script to a directory with write access (the output ntuple will be created there) and run it:
>
>
To run PIDGen resampling, you need to setup Urania v7r0, copy the example script to a directory with write access (the output ntuple will be created there) and run it:
 
Changed:
<
<
$ lb-run --nightly-cvmfs --nightly lhcb-prerelease Urania/master bash
>
>
$ lb-run Urania/v7r0 bash
 $ cp $PIDPERFSCRIPTSROOT/scripts/python/PIDGenUser/Examples/Lb2Lcpi/Lb2Lcpi_pidgen.py {your_place} $ cd {your_place} $ python Lb2Lcpi_pidgen.py
Line: 58 to 58
  Note that Run 1 and Run 2 PID configurations have different names!
Added:
>
>
For most recent PID configurations which were not added yet to Urania release, you might need to use Urania prerelease nightlies instead:
$ lb-run --nightly-cvmfs --nightly lhcb-prerelease Urania/master bash
 

Running PID variable transformation (PIDCorr)

Changed:
<
<
To run PIDCorr variable transformation, you need to setup Urania nightly, copy the example script to a directory with write access (the output ntuple will be created there) and run it:
>
>
To run PIDCorr variable transformation, you need to setup Urania v7r0, copy the example script to a directory with write access (the output ntuple will be created there) and run it:
 
Changed:
<
<
$ lb-run --nightly-cvmfs --nightly lhcb-prerelease Urania/master bash
>
>
$ lb-run Urania/v7r0 bash
 $ cp $PIDPERFSCRIPTSROOT/scripts/python/PIDGenUser/Examples/Lb2Lcpi/Lb2Lcpi_pidcorr.py {your_place} $ cd {your_place} $ python Lb2Lcpi_pidcorr.py
Line: 74 to 79
 $ python $PIDPERFSCRIPTSROOT/scripts/python/PIDGenUser/PIDGen.py
Changed:
<
<

Available PID templates

>
>

PID templates available in Urania v7r0

  Available combinations of track and PID variable in Run 1:

Revision 132017-06-12 - AntonPoluektov

Line: 1 to 1
 
META TOPICPARENT name="PIDCalibPackage"

Unbinned PID resampling using kernel density estimation

Line: 74 to 74
 $ python $PIDPERFSCRIPTSROOT/scripts/python/PIDGenUser/PIDGen.py
Added:
>
>

Available PID templates

Available combinations of track and PID variable in Run 1:

Variable pi K p e mu
CombDLLK G/C8/C9 G/C8/C9 G/C8/C9    
CombDLLe       G/C8  
CombDLLmu G G     G
CombDLLp G/C8/C9 G/C8/C9 G/C8/C9    
S20CombDLLe       G  
S20V3ProbNNe       G  
V2ProbNNK G/C8/C9 G/C8/C9 G/C8/C9    
V2ProbNNp G/C8/C9 G/C8/C9 G/C8/C9    
V2ProbNNpi G/C8/C9 G/C8/C9 G/C8/C9    
V3ProbNNK G/C8/C9 G/C8/C9 G/C8/C9 G/C8 G
V3ProbNNe       G/C8  
V3ProbNNmu G       G
V3ProbNNp G/C8/C9 G/C8/C9 G/C8/C9    
V3ProbNNpi G/C8/C9 G/C8/C9 G/C8/C9 G/C8 G

Available combinations of track and PID variable in Run 2:

Variable pi K p e mu
CombDLLK_Brunel G G G    
CombDLLp_Brunel G G G    
MC15TuneV1_ProbNNK_Brunel G G G    
MC15TuneV1_ProbNNp_Brunel G G G    
MC15TuneV1_ProbNNpi_Brunel G G G    

G: PIDGen, C8: PIDCorr with sim08 MC, C9: PIDCorr with sim09 MC,

 

Frequently asked questions

I don't have the Eta (pseudorapidity) variable in my simulation ntuple.

Revision 122017-05-29 - AntonPoluektov

Line: 1 to 1
 
META TOPICPARENT name="PIDCalibPackage"

Unbinned PID resampling using kernel density estimation

Line: 21 to 21
  The approach with PID variable transformation aims to remove this limitation. In that case, the corrected PID variables preserve correlations with the output of simulation, and through the correlations in simulation the correlations between PID variables for the same track are reproduced "in the first order". This is not perfect by probably acceptable in most cases. The drawback of this approach is that it also relies on the parametrisation of PID PDFs in simulation (which are extracted from samples that are typically much smaller than calibration data).
Changed:
<
<
More details about the approach can be found in this presentation.
>
>
More details about the approach can be found in this presentation and in the LHCb-INT-2017-007 internal note draft.
 

Contents of the package

Revision 112017-05-22 - AntonPoluektov

Line: 1 to 1
 
META TOPICPARENT name="PIDCalibPackage"

Unbinned PID resampling using kernel density estimation

Line: 93 to 93
 
The result of PIDCorr does not match data
In addition to the issues mentioned above, there can be a few issues specific to PIDCorr:
Changed:
<
<
  1. The version of simulation (sim08, sim09) used to generate your signal sample should match to version used to create PID response templates. PIDCorr allows one to choose either sim08 or sim09.
>
>
  1. The version of simulation (sim08, sim09) used to generate your signal sample should match the version used to create PID response templates. PIDCorr allows one to choose either sim08 or sim09 (but note that not all PID responses are available for both versions).
  2. Typically, MC samples used to create simulation response templates are much smaller than the data calibration samples. Therefore, uncertainty due to the size of calibration samples can be larger with PIDCorr than with PIDGen.
 
Can I set up PIDGen locally in my university?
It can be done, but will require plenty of space:

Revision 102017-05-20 - AntonPoluektov

Line: 1 to 1
 
META TOPICPARENT name="PIDCalibPackage"

Unbinned PID resampling using kernel density estimation

Line: 80 to 80
  You can use PIDGen (PIDCorr) with either Pt and Eta variables (-p <ptvar> -e <etavar>), or Pt and P variables (-p <ptvar> -q <pvar>) taken from the ntuple. In the latter case Eta will be calculated from P and Pt in the script.

The PID response I need is missing in PIDGen
Added:
>
>
Probably nobody asked for it before, please let us know.
 
The PID response I need is present in PIDGen but not in PIDCorr
Added:
>
>
Probably nobody asked for it before, please let us know. But PIDCorr relies on MC samples as well, so you might need to wait until the sample is generated before the PID response you need becomes available.
 
The result of PIDGen does not match data
Added:
>
>
  1. Check that there are no "hidden" PID cuts in the simulated or data samples (e.g. in the stripping).
  2. Check that kinematic distributions of the simulated sample match those in data. It might be necessary to e.g. reweigh MC events with the Dalitz plot distributions observed in data.
  3. Usually, one needs to either reweigh MC such that the number of tracks distribution matches that in data (or at least rescale the Ntracks variable by multiplying it by 1.10-1.15 such that it roughly resembles data distribution).
  4. If data distribution is obtained with sWeights, make sure there are no biases in the distribution (e.g. correlations between reweighting and PID variables, or backgrounds that peak in the reweighting variable).
 
The result of PIDCorr does not match data
Added:
>
>
In addition to the issues mentioned above, there can be a few issues specific to PIDCorr:
  1. The version of simulation (sim08, sim09) used to generate your signal sample should match to version used to create PID response templates. PIDCorr allows one to choose either sim08 or sim09.
 
Can I set up PIDGen locally in my university?
It can be done, but will require plenty of space:

Revision 92017-05-18 - AntonPoluektov

Line: 1 to 1
 
META TOPICPARENT name="PIDCalibPackage"

Unbinned PID resampling using kernel density estimation

Line: 94 to 94
 
    1. Change the environment variable $PIDPERFSCRIPTSROOT to point to the location of modified PIDCalib scripts before running PIDGen or PIDCorr

What stripping version is used for Run1 electron samples?
Changed:
<
<
Stripping20. Stripping21 will follow shortly.
>
>
e_CombDLLe and e_VxProbNNe variables correspond to Stripping 21, e_S20CombDLLe and e_S20VxProbNNe to Stripping 20.
  -- AntonPoluektov - 2017-05-09

Revision 82017-05-16 - AntonPoluektov

Line: 1 to 1
 
META TOPICPARENT name="PIDCalibPackage"

Unbinned PID resampling using kernel density estimation

Line: 93 to 93
 
    1. Copy the contents of /eos/lhcb/wg/PID/PIDGen to your local machine and change the value of eosdir and eosrootdir variables in PIDGenExpert/Run*/Config*.py files to point to this location.
    2. Change the environment variable $PIDPERFSCRIPTSROOT to point to the location of modified PIDCalib scripts before running PIDGen or PIDCorr

Added:
>
>
What stripping version is used for Run1 electron samples?
Stripping20. Stripping21 will follow shortly.
  -- AntonPoluektov - 2017-05-09 \ No newline at end of file

Revision 72017-05-15 - AntonPoluektov

Line: 1 to 1
 
META TOPICPARENT name="PIDCalibPackage"

Unbinned PID resampling using kernel density estimation

Line: 25 to 25
 

Contents of the package

Changed:
<
<
All the user-level scripts are located in $PIDPERFSCRIPTSROOT/scripts/python/PIDGenUser/ directory under PIDCalib package in Urania project. Here is the GitLab repository of the code.
>
>
All the user-level scripts are located in $PIDPERFSCRIPTSROOT/scripts/python/PIDGenUser/ directory under PIDCalib package in Urania project. Here is the GitLab repository of the code.
 
  • PIDGen.py - Python script for PID resampling.

Revision 62017-05-13 - AntonPoluektov

Line: 1 to 1
 
META TOPICPARENT name="PIDCalibPackage"

Unbinned PID resampling using kernel density estimation

Introduction

Changed:
<
<
PIDGen is a set of scripts within PIDCalib package to correct the PID response for MC signal samples based on PID calibration data (the same data as used by "traditional" PIDCalib approach). It implements two alternative approaches:
>
>
PIDGen is a set of scripts within PIDCalib package to correct the PID response for MC signal samples based on PID calibration data (the same data as used by "traditional" PIDCalib approach). It implements two alternative approaches:
 
  • PID resampling, where the PID response is completely replaced by the one randomly generated from calibration PDFs.
Line: 18 to 17
  The corrected PID response from both the PID resampling and PID variable transformation can be used as an input to a multivariate classifier.
Changed:
<
<
The PID resampling approach has an important limitation that the PID variables for the same track are generated independently, and thus no correlations between them are reproduced. Therefore, only one PID variable per track can be used in the selection (correlations between variables for different tracks are, naturally, preserved via correlations with kinematics of tracks).
>
>
The PID resampling approach has an important limitation that the PID variables for the same track are generated independently, and thus no correlations between them are reproduced. Therefore, only one PID variable per track can be used in the selection (correlations between variables for different tracks are, naturally, preserved via correlations with kinematics of tracks).
 
Changed:
<
<
The approach with PID variable transformation aims to remove this limitation. In that case, the corrected PID variables preserve correlations with the output of simulation, and through the correlations in simulation the correlations between PID variables for the same track are reproduced "in the first order". This is not perfect by probably acceptable in most cases. The drawback of this approach is that it also relies on the parametrisation of PID PDFs in simulation (which are extracted from samples that are typically much smaller than calibration data).
>
>
The approach with PID variable transformation aims to remove this limitation. In that case, the corrected PID variables preserve correlations with the output of simulation, and through the correlations in simulation the correlations between PID variables for the same track are reproduced "in the first order". This is not perfect by probably acceptable in most cases. The drawback of this approach is that it also relies on the parametrisation of PID PDFs in simulation (which are extracted from samples that are typically much smaller than calibration data).
  More details about the approach can be found in this presentation.
Line: 72 to 69
 $ python Lb2Lcpi_pidcorr.py
Changed:
<
<
Only run 1 PID configurations are available for the moment. The complete list of PID configurations available can be obtained by running PIDCorr.py without arguments:
>
>
The result of PIDCorr is dependent on the version of MC used to generate your signal! Only Sim08 Run 1 PID configurations are available for the moment. The complete list of PID configurations available can be obtained by running PIDCorr.py without arguments:
 
$ python $PIDPERFSCRIPTSROOT/scripts/python/PIDGenUser/PIDGen.py

Frequently asked questions

Added:
>
>
I don't have the Eta (pseudorapidity) variable in my simulation ntuple.
You can use PIDGen (PIDCorr) with either Pt and Eta variables (-p <ptvar> -e <etavar>), or Pt and P variables (-p <ptvar> -q <pvar>) taken from the ntuple. In the latter case Eta will be calculated from P and Pt in the script.

The PID response I need is missing in PIDGen

The PID response I need is present in PIDGen but not in PIDCorr

The result of PIDGen does not match data

The result of PIDCorr does not match data

Can I set up PIDGen locally in my university?
It can be done, but will require plenty of space:
  1. Make sure Urania is accessible in your local machine (e.g. via cvmfs)
  2. Copy the contents of /eos/lhcb/wg/PID/PIDGen to your local machine and change the value of eosdir and eosrootdir variables in PIDGenExpert/Run*/Config*.py files to point to this location.
  3. Change the environment variable $PIDPERFSCRIPTSROOT to point to the location of modified PIDCalib scripts before running PIDGen or PIDCorr

 -- AntonPoluektov - 2017-05-09 \ No newline at end of file

Revision 52017-05-12 - AntonPoluektov

Line: 1 to 1
 
META TOPICPARENT name="PIDCalibPackage"

Unbinned PID resampling using kernel density estimation

Line: 28 to 28
 

Contents of the package

Changed:
<
<
All the user-level scripts are located in $PIDPERFSCRIPTSROOT/scripts/python/PIDGenUser/ directory under PIDCalib package in Urania project.
>
>
All the user-level scripts are located in $PIDPERFSCRIPTSROOT/scripts/python/PIDGenUser/ directory under PIDCalib package in Urania project. Here is the GitLab repository of the code.
 
  • PIDGen.py - Python script for PID resampling.

Revision 42017-05-11 - AntonPoluektov

Line: 1 to 1
 
META TOPICPARENT name="PIDCalibPackage"

Unbinned PID resampling using kernel density estimation

Line: 77 to 77
 $ python $PIDPERFSCRIPTSROOT/scripts/python/PIDGenUser/PIDGen.py
Added:
>
>

Frequently asked questions

  -- AntonPoluektov - 2017-05-09

Revision 32017-05-10 - AntonPoluektov

Line: 1 to 1
 
META TOPICPARENT name="PIDCalibPackage"

Unbinned PID resampling using kernel density estimation

Line: 44 to 44
  Generation of PID response is performed by one of the two scripts, PIDGen.py or PIDCorr.py. The scripts require many parameters (MC data location, variable names, location of the calibrated PID response etc.). The sample python scripts are provided, Examples/Lb2Lcpi/Lb2Lcpi_{pidgen, pidcorr}, which the user can modify to actually run the PID generation for their own MC sample.
Deleted:
<
<
The complete list of PID configurations available in Run 1 and Run 2 can be obtained by running PIDGen.py or PIDCorr.py without arguments:
$ python $PIDPERFSCRIPTSROOT/scripts/python/PIDGenUser/PIDGen.py
Note that Run 1 and Run 2 PID configurations have different names!
 

Running PID resampling (PIDGen)

To run PIDGen resampling, you need to setup Urania nightly, copy the example script to a directory with write access (the output ntuple will be created there) and run it:

$ lb-run --nightly-cvmfs --nightly lhcb-prerelease Urania/master bash
Changed:
<
<
$ cp $PIDPERFSCRIPTSROOT/scripts/python/PIDGenUser/Examples/Lb2Lcpi_pidgen.py {your_place}
>
>
$ cp $PIDPERFSCRIPTSROOT/scripts/python/PIDGenUser/Examples/Lb2Lcpi/Lb2Lcpi_pidgen.py {your_place}
 $ cd {your_place} $ python Lb2Lcpi_pidgen.py
Added:
>
>
The complete list of PID configurations available in Run 1 and Run 2 can be obtained by running PIDGen.py without arguments:
$ python $PIDPERFSCRIPTSROOT/scripts/python/PIDGenUser/PIDGen.py
Note that Run 1 and Run 2 PID configurations have different names!
 

Running PID variable transformation (PIDCorr)

To run PIDCorr variable transformation, you need to setup Urania nightly, copy the example script to a directory with write access (the output ntuple will be created there) and run it:

$ lb-run --nightly-cvmfs --nightly lhcb-prerelease Urania/master bash
Changed:
<
<
$ cp $PIDPERFSCRIPTSROOT/scripts/python/PIDGenUser/Examples/Lb2Lcpi_pidcorr.py {your_place}
>
>
$ cp $PIDPERFSCRIPTSROOT/scripts/python/PIDGenUser/Examples/Lb2Lcpi/Lb2Lcpi_pidcorr.py {your_place}
 $ cd {your_place} $ python Lb2Lcpi_pidcorr.py
Added:
>
>
Only run 1 PID configurations are available for the moment. The complete list of PID configurations available can be obtained by running PIDCorr.py without arguments:
$ python $PIDPERFSCRIPTSROOT/scripts/python/PIDGenUser/PIDGen.py
 -- AntonPoluektov - 2017-05-09

Revision 22017-05-10 - AntonPoluektov

Line: 1 to 1
 
META TOPICPARENT name="PIDCalibPackage"

Unbinned PID resampling using kernel density estimation

Line: 46 to 46
  The complete list of PID configurations available in Run 1 and Run 2 can be obtained by running PIDGen.py or PIDCorr.py without arguments:
Changed:
<
<
$ python $PIDPERFSCRIPTSROOT/scripts/python/PIDGenUser/PIDGem.py
>
>
$ python $PIDPERFSCRIPTSROOT/scripts/python/PIDGenUser/PIDGen.py
 
Added:
>
>
Note that Run 1 and Run 2 PID configurations have different names!
 

Running PID resampling (PIDGen)

To run PIDGen resampling, you need to setup Urania nightly, copy the example script to a directory with write access (the output ntuple will be created there) and run it:

Changed:
<
<
$ LbLogin -c x86_64-slc6-gcc62-opt $ lb-run --nightly-cvmfs --nightly lhcb-head Urania/HEAD bash
>
>
$ lb-run --nightly-cvmfs --nightly lhcb-prerelease Urania/master bash
 $ cp $PIDPERFSCRIPTSROOT/scripts/python/PIDGenUser/Examples/Lb2Lcpi_pidgen.py {your_place} $ cd {your_place} $ python Lb2Lcpi_pidgen.py
Line: 66 to 66
 To run PIDCorr variable transformation, you need to setup Urania nightly, copy the example script to a directory with write access (the output ntuple will be created there) and run it:
Changed:
<
<
$ LbLogin -c x86_64-slc6-gcc62-opt $ lb-run --nightly-cvmfs --nightly lhcb-head Urania/HEAD bash
>
>
$ lb-run --nightly-cvmfs --nightly lhcb-prerelease Urania/master bash
 $ cp $PIDPERFSCRIPTSROOT/scripts/python/PIDGenUser/Examples/Lb2Lcpi_pidcorr.py {your_place} $ cd {your_place} $ python Lb2Lcpi_pidcorr.py

Revision 12017-05-09 - AntonPoluektov

Line: 1 to 1
Added:
>
>
META TOPICPARENT name="PIDCalibPackage"

Unbinned PID resampling using kernel density estimation

Introduction

PIDGen is a set of scripts within PIDCalib package to correct the PID response for MC signal samples based on PID calibration data (the same data as used by "traditional" PIDCalib approach). It implements two alternative approaches:

  • PID resampling, where the PID response is completely replaced by the one randomly generated from calibration PDFs.

  • Transformation of PID variables, where the PID variables from the simulation are transformed in such a way that they are distributed as in data. (This is only available for Run1 for the moment).

Like in PIDCalib, the PID correction is a function of track kinematics (momentum and pseudorapidity) and event multiplicity (number of tracks). Unlike PIDCalib, the correction is done with an unbinned approach, where the calibration PDFs in four dimensions (PID, Pt, eta, Ntrack) are described by a kernel density estimation procedure using the Meerkat library.

The corrected PID response from both the PID resampling and PID variable transformation can be used as an input to a multivariate classifier.

The PID resampling approach has an important limitation that the PID variables for the same track are generated independently, and thus no correlations between them are reproduced. Therefore, only one PID variable per track can be used in the selection (correlations between variables for different tracks are, naturally, preserved via correlations with kinematics of tracks).

The approach with PID variable transformation aims to remove this limitation. In that case, the corrected PID variables preserve correlations with the output of simulation, and through the correlations in simulation the correlations between PID variables for the same track are reproduced "in the first order". This is not perfect by probably acceptable in most cases. The drawback of this approach is that it also relies on the parametrisation of PID PDFs in simulation (which are extracted from samples that are typically much smaller than calibration data).

More details about the approach can be found in this presentation.

Contents of the package

All the user-level scripts are located in $PIDPERFSCRIPTSROOT/scripts/python/PIDGenUser/ directory under PIDCalib package in Urania project.

  • PIDGen.py - Python script for PID resampling.

  • PIDCorr.py - Python script for PID variable transformation.

  • Examples/Lb2Lcpi/Lb2Lcpi_pidgen.py - Example PID resampling script for Lb->Lcpi MC sample

  • Examples/Lb2Lcpi/Lb2Lcpi_pidcorr.py - Example script for PID variable transformation for Lb->Lcpi MC sample

Note that the scripts use data stored in CERN EOS, and assume that it's accessible at root://eoslhcb.cern.ch/ server.

Using PIDGen with MC samples

Generation of PID response is performed by one of the two scripts, PIDGen.py or PIDCorr.py. The scripts require many parameters (MC data location, variable names, location of the calibrated PID response etc.). The sample python scripts are provided, Examples/Lb2Lcpi/Lb2Lcpi_{pidgen, pidcorr}, which the user can modify to actually run the PID generation for their own MC sample.

The complete list of PID configurations available in Run 1 and Run 2 can be obtained by running PIDGen.py or PIDCorr.py without arguments:

$ python $PIDPERFSCRIPTSROOT/scripts/python/PIDGenUser/PIDGem.py

Running PID resampling (PIDGen)

To run PIDGen resampling, you need to setup Urania nightly, copy the example script to a directory with write access (the output ntuple will be created there) and run it:

$ LbLogin -c x86_64-slc6-gcc62-opt
$ lb-run --nightly-cvmfs --nightly lhcb-head Urania/HEAD bash
$ cp $PIDPERFSCRIPTSROOT/scripts/python/PIDGenUser/Examples/Lb2Lcpi_pidgen.py {your_place}
$ cd {your_place}
$ python Lb2Lcpi_pidgen.py

Running PID variable transformation (PIDCorr)

To run PIDCorr variable transformation, you need to setup Urania nightly, copy the example script to a directory with write access (the output ntuple will be created there) and run it:

$ LbLogin -c x86_64-slc6-gcc62-opt
$ lb-run --nightly-cvmfs --nightly lhcb-head Urania/HEAD bash
$ cp $PIDPERFSCRIPTSROOT/scripts/python/PIDGenUser/Examples/Lb2Lcpi_pidcorr.py {your_place}
$ cd {your_place}
$ python Lb2Lcpi_pidcorr.py

-- AntonPoluektov - 2017-05-09

 
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