Changes between Version 6 and Version 7 of SFS

Timestamp:

Jun 9, 2020, 4:17:20 PM (5 years ago)

Author:

jackaraz

Comment:

--

SFS

@@ -11 +11 @@
 }}}
 
-First-line activates the `RECO` mode for !MadAnalysis 5 framework, the second line is to install !FasJet interface and finally last line activates the !FastJet interface for the corresponding session. User can set the desired reconstruction algorithm as before, please see [https://arxiv.org/abs/1808.00480 arXiv:1808.00480] for details on the usage of !FastJet module in !MadAnalysis 5. SFS contains three submodules, namely `reco_efficiency`, `smearer` and `tagger`. These submodules set a probability distribution to reconstruct a given object, smear given object's four-momentum using normalized Gaussian functions and sets identification efficiencies respectively. The goal is to create a CPU efficient, user-friendly, easy-to-use and generic environment. Thus given transfer functions are translated into C++ functions to act on reconstructed final state objects such as jets, hadronic taus, electrons, muons and photons. 
+First-line activates the `RECO` mode for !MadAnalysis 5 framework, the second line is to install !FasJet interface, and finally, the last line enables the !FastJet interface for the corresponding session. User can set the desired reconstruction algorithm as before, please see [https://arxiv.org/abs/1808.00480 arXiv:1808.00480] for details on the usage of !FastJet module in!MadAnalysis 5. SFS contains three submodules, namely `reco_efficiency`, `smearer`, and `tagger`. These submodules set a probability distribution to reconstruct a given object, smear carried object's four-momentum using normalized Gaussian functions and sets identification efficiencies, respectively. The goal is to create a CPU efficient, user-friendly, easy-to-use, and generic environment. Thus given transfer functions are translated into C++ functions to act on reconstructed final state objects such as jets, hadronic taus, electrons, muons, and photons. 
 
 Jet reconstruction contains two possible options, namely jet smearing and substructure smearing. These options can be set via
@@ -21 +21 @@
 == Reconstruction Efficiencies ==
 
-This submodule can be used on jets (`j`, `21`), electrons (`e`,`11`), muons (`mu`,`13`), hadronic taus (`ta`,`15`) or photons (`a`,`22`). It simply takes three input;
+This submodule can the  on jets (`j`, `21`), electrons (`e`, `11`), muons (`mu`, `13`), hadronic taus (`ta`, `15`) or photons (`a`, `22`). It simply takes three input;
 
 {{{
@@ -38 +38 @@
 == Object Smearing ==
 
-Here we introduce the smearing function which uses a user-defined standard deviation function which can depend on any observable given in !MadAnalysis 5's framework. This function can be, again, defined in a piecewise manner in order to have different sensitivities to different phase-spaces. This submodule can be defined as follows;
+Here we introduce the smearing function, which uses a user-defined standard deviation function, which can depend on any observable given in !MadAnalysis 5's framework. This function can be, again, defined in a piecewise manner to have different sensitivities to different phase-spaces. This submodule can be defined as follows;
 {{{
 define smearer <obj> with <var> <func> [<dom>]
@@ -50 +50 @@
 ma5> define smearer j with PT sqrt(0.25^2 + pt^2*3.1e-3^2) [abseta > 1.5 and abseta <= 2.5 and pt > 0.1]
 }}}
-Here we exemplified transverse momentum smearing of the jet object. This function simply generates a standard deviation which depends on `PT` of the given jet. This STD will be further used in a normalized Gaussian function to simulate the uncertainty on the transverse momentum observation. The transverse momentum then shifted within the Gaussian width.
+Here we exemplified transverse momentum smearing of the jet object. This function simply generates a standard deviation, which depends on `PT` of the given jet. This STD will be further used in a normalized Gaussian function to simulate the uncertainty on the transverse momentum observation. The transverse momentum then shifted within the Gaussian width.
 
 == Particle Identification ==
@@ -71 +71 @@
 Here first three examples show the jet misidentification as a hadronic tau object where it can be misidentified with respect to the number of prongs inside the jet. The last line shows the b-jet tagging efficiency, which depends on its transverse momentum.
 
-The algorithm gives the importance to first b-jets, where if a jet is mistagged as c-jet it won't be mistagged as b, tau, electron or photon. Similar order goes for all other objects, we simply set the importance as follows b > c > tau > muon > electron > photon which means that if a jet is mistagged as any of the objects on this sequence it won't be mistagged as the following objects.
+The algorithm gives the importance to first b-jets, where if a jet is mistagged as c-jet, it won't be mistagged as b, tau, electron, or photon. Similar order goes for all other objects; we simply set the importance as follows b > c > tau > muon > electron > photon, which means that if a jet is mistagged as any of the objects on this sequence, it won't be mistagged as the following objects.
 
 == SFS in Expert Mode ==
 
-SFS machinery can be used with expert mode as well. User can set up a detector card with the desired options which are exemplified above and give to !MadAnalysis as input;
+SFS machinery can be used with expert mode as well. Users can set up a detector card with the desired options, which are exemplified above and give to !MadAnalysis as input;
 {{{
 $> ./bin/ma5 -Re <folder_name> <analysis_name> <SFS_card>
@@ -84 +84 @@
 == LHC Recasting with SFS ==
 
-SFS machinery can be used for LHC recasting. Details on how to write an analysis for [http://madanalysis.irmp.ucl.ac.be/wiki/PublicAnalysisDatabase Public Analysis Database] can be found [https://madanalysis.irmp.ucl.ac.be/wiki/WritingAnalyses here]. User should initialize the code with PDGIDs for hadrons and invisible particles as follows;
+SFS machinery can be used for LHC recasting. To install available analyses, please type;
 {{{
-PHYSICS->mcConfig().Reset();
-PHYSICS->mcConfig().AddHadronicId(<PDG Hadron>);
-PHYSICS->mcConfig().AddInvisibleId(<PDG Invisible>);
+ma5> install PADForSFS
 }}}
-a suggested list can be found 
+This will install all available analyses to be used in recast. User does not need to install Delphes or ROOT to be able to use this module. In order to initialize `PADForSFS` simply type;
+{{{
+ma5> set main.recast = on
+ma5> import <my_sample>.hepmc.gz as <sample_name>
+ma5> set <sample_name>.xsection = 123
+ma5> submit 
+}}}
+First-line initializes the recasting module; second line imports desired analysis with label `<sample_name>`, the third line sets the cross-section for the sample, and the last line submits the job. It is also possible to include theoretical uncertainties and high luminosity interpretations, for more info, see [https://arxiv.org/abs/1910.11418 arXiv:1910.11418].
+
+----
+'''How to write an analysis?''' Details on how to write an analysis for [http://madanalysis.irmp.ucl.ac.be/wiki/PublicAnalysisDatabase Public Analysis Database] can be found [https://madanalysis.irmp.ucl.ac.be/wiki/WritingAnalyses here]. User should initialize the code with PDGIDs for hadrons and invisible particles as follows;
+{{{
+bool cms_cat_43_21::Initialize(const MA5::Configuration& cfg,
+   const std::map<std::string,std::string>& parameters)
+{
+  PHYSICS->mcConfig().Reset();
+  PHYSICS->mcConfig().AddHadronicId(<PDG-ID Hadron>);
+  PHYSICS->mcConfig().AddInvisibleId(<PDG-ID Invisible>);
+  return true;
+}
+}}}
+a complete set or PDG-IDs to be initialized can be found in [https://madanalysis.irmp.ucl.ac.be/attachment/wiki/SFS/initialize.txt here]. Without such a list, !MadAnalysis won't be able to differentiate hadrons and invisible particles to be further used in the analysis.
+