Changes between Version 11 and Version 12 of SFS


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Timestamp:
Jun 14, 2020, 5:30:27 PM (5 years ago)
Author:
jackaraz
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  • SFS

    v11 v12  
    22== Simplified - Fast Simulation (SFS) of detector response ==
    33
    4 This page contains a brief introduction about the usage of SFS machinery, for details, please see [CITE]. SFS machinery allows the user to simulate detector response within !MadAnalysis 5 framework using only !FastJet libraries. It is fully integrated with [http://madanalysis.irmp.ucl.ac.be/wiki/PublicAnalysisDatabase Public Analysis Database], and the user can recast experimental analyses using SFS' fast interface, for details, please see below. Although we provide default [https://madanalysis.irmp.ucl.ac.be/raw-attachment/wiki/SFS/ATLAS_default.ma5 ATLAS] and [https://madanalysis.irmp.ucl.ac.be/raw-attachment/wiki/SFS/CMS_default.ma5 CMS] cards which are validated against corresponding Delphes cards for four different physics process, this introduction will provide all information that is needed to use SFS machinery for any homebrew detector simulation.
     4This page contains a brief introduction about the usage of SFS machinery, for details, please see [CITE]. SFS machinery allows the user to simulate detector response within !MadAnalysis 5 framework using only !FastJet libraries. SFS uses transfer functions on reconstructed objects to simulate detector response. It is, also, fully integrated with [http://madanalysis.irmp.ucl.ac.be/wiki/PublicAnalysisDatabase Public Analysis Database], and the user can recast experimental analyses using SFS' fast interface, for details, please see [https://madanalysis.irmp.ucl.ac.be/wiki/SFS#LHCRecastingwithSFS below]. Although we provide default [https://madanalysis.irmp.ucl.ac.be/raw-attachment/wiki/SFS/ATLAS_default.ma5 ATLAS] and [https://madanalysis.irmp.ucl.ac.be/raw-attachment/wiki/SFS/CMS_default.ma5 CMS] cards which are validated against corresponding Delphes cards for four different physics process, this introduction will provide all information that is needed to use SFS machinery for any homebrew detector simulation.
    55
    66* Prerequisites: !FastJet
    … …  
    1111}}}
    1212
    13 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. After installing !FastJet one can use default SFS cards by simply typing
     13First-line activates the `RECO` mode for !MadAnalysis 5 framework which is required to use SFS machinery, the second line is to install !FasJet interface, and finally, the last line enables the !FastJet interface for the current session. User can set the desired reconstruction algorithm as before, please see [https://arxiv.org/abs/1808.00480 arXiv:1808.00480] or [https://madanalysis.irmp.ucl.ac.be/wiki/tutorials tutorials] for details on the usage of the !FastJet module in !MadAnalysis 5. After installing !FastJet one can use default SFS cards by simply typing
    1414{{{
    1515$> ./bin/ma5 -R madanalysis/input/<EXP>_default.ma5
    … …  
    2525where `<opt>` represents user-defined input which can either be `jets` or `constituents` (default `jets`).  `jets` option allows the `reco_efficiency` and `smearer` submodules to act on clustered jet objects. The latter option, `constituents`, enables the possibility to apply detector response at the hadron level.
    2626
    27 == Reconstruction Efficiencies ==
     27== Reconstruction Efficiencies: `reco_efficiency` ==
    2828
    29 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;
    30 
     29This submodule can act on jets (`j`, `21`), electrons (`e`, `11`), muons (`mu`, `13`), hadronic taus (`ta`, `15`) or photons (`a`, `22`). It simply takes three input;
    3130{{{
    3231ma5> define reco_efficiency <obj> <func> [<domain>]
    3332}}}
    34 where `<obj>` represents the desired object to be reconstructed, `<func>` is the transfer function which can depend on any observable like `PT`, `E`, `ETA`,`ABSETA`,`PHI` etc. which are defined in !MadAnalysis 5's interface. `<func>` can also include any functional form like trigonometric or hyperbolic functions. `<dom>` represents the domain where defined function will be active. The module creates a piecewise function to construct a probability distribution for the given object. This piece of input will generate a probability distribution for the desired object to decide if it's going to be used in the analysis or not.
     33where `<obj>` represents the desired object to be reconstructed, `<func>` is the transfer function which can depend on any observable like `PT`, `E`, `ETA`,`ABSETA`,`PHI` etc. which are defined in !MadAnalysis 5's interface. `<func>` can also include any functional form like trigonometric or hyperbolic functions. `<dom>` (optional) represents the domain where the defined function will be active. The module creates a piecewise function to construct a probability distribution for the given object and phase-space. This piece of input will generate a probability distribution for the desired object to decide if it's going to be used in the analysis or not.
    3534
    36 Example:
     35* Example:
    3736{{{
    3837ma5> define reco_efficiency e 0.0   [pt <= 10.0 or abseta > 2.5]
    … …  
    4039ma5> define reco_efficiency e 0.55  [pt > 10.0 and abseta > 1.5 and abseta <= 2.5]
    4140}}}
    42 Here we exemplify an electron reconstruction efficiency where an electron object will not be reconstructed if it has less than 10 GeV transverse momentum or its pseudorapidity is above 2.5. Additionally, it will be reconstructed with 70% probability if it's within |\eta| < 1.5 and pT >= 10 GeV and with 55% probability if its between 1.5 < |\eta| <= 2.5. Domain inputs have to be separated with `and` or `or` keywords to be effective, `and` enforces all domain conditions to be true and `or` requires at least one domain to be true.
     41Here we exemplify an electron reconstruction efficiency where an electron object will not be reconstructed if it has less than 10 GeV transverse momentum or its pseudorapidity is above 2.5. Additionally, it will be reconstructed with 70% probability if it's within |\eta| < 1.5 and pT >= 10 GeV and with 55% probability if its between 1.5 < |\eta| <= 2.5. Domain inputs have to be separated with `and` or `or` keywords to be effective, `and` enforces all domain conditions to be true and `or` requires at least one domain to be true. It is also possible to connect multiple domains together such as `[(abseta>1 and abseta<1.5) or (abseta>2.5 and abseta<3)]`.
    4342
    44 == Object Smearing ==
     43== Object Smearing: `smearer` ==
    4544
    46 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;
     45Here we introduce the smearing function, which uses a user-defined standard deviation to be used in normalized Gaussian distribution. This function can depend on any observable defined in  !MadAnalysis interface. All information regarding the reconstructed objects to be used, function and domain syntax is the same as [https://madanalysis.irmp.ucl.ac.be/wiki/SFS#ReconstructionEfficiencies before]. This submodule can be defined as follows;
    4746{{{
    4847define smearer <obj> with <var> <func> [<dom>]
    4948}}}
    50 Here `<obj>` stands for the object to be smeared, `<var>` is the variable to be smeared which can be `E`, `PT`, `PX`, `PY`, `PZ`, `PHI` and `ETA`. `<func>` and `<dom>` are, as defined above, stands for the function and the domain that this function will be active. Note that `<obj>` and `<var>` are separated with the keyword `with`.
     49Here `<obj>` stands for the object (jet, electron, muon, hadronic tau or photon), `<var>` is the variable to be smeared which can be `E`, `PT`, `PX`, `PY`, `PZ`, `PHI` and `ETA`. `<func>` and `<dom>` are, as defined above, stands for the function and the domain that this function will be active. Note that `<obj>` and `<var>` are separated with the keyword `with` and `<dom>` is optional.
    5150
    52 Example:
     51* Example:
    5352{{{
    5453ma5> define smearer j with PT sqrt(0.06^2 + pt^2*1.3e-3^2) [abseta <= 0.5 and pt > 0.1]
    … …  
    5857Here 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.
    5958
    60 == Particle Identification ==
     59== Particle Identification: `tagger` ==
    6160
    6261`tagger` submodule is used to set particle identification or misidentification efficiencies for desired objects. It can be defined as follows;
    … …  
    6766where `<true>` stands for the true object that will be reconstructed as `<reco>` object. If `<true>` and `<reco>` objects are same, the module will apply efficiency to reconstruct the given object. If, on the other hand, the `<true>` and `<reco>` objects are different, the module will apply misidentification efficiency on the `<true>` object. This submodule can be used on jets (`j`, `21`), b-jets (`b`, `5`), c-jets (`c`, `4`), electrons (`e`, `11`), muons (`mu`, `13`), hadronic taus (`ta`, `15`) or photons (`a`, `22`) where within certain physical limitations each object can be reconstructed as other objects, please see [CITE] for the available options. It is important to note that `<true>` and `<reco>` are separated by the keyword `as`.
    6867
    69 Example:
     68* Example:
    7069{{{
    7170ma5> define tagger j as ta 0.01 [ntracks >= 2 and abseta <= 2.7]
    … …  
    7776Here 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.
    7877
    79 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.
     78The 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. Similarly, user can mistag electrons, muons and photons as well;
    8079
     80* Example:
     81{{{
     82ma5> define tagger e as mu 0.01*exp(-pt)
     83}}}
     84This example sets a mistagging efficiency of 1% to tag an electron as a muon which decreases exponentially by the transverse momentum of the electron.
    8185== SFS in Expert Mode ==
    8286
    83 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;
     87SFS machinery can be used with expert mode as well. Users can set up an SFS card with the desired options, which are exemplified above and given to !MadAnalysis as input;
    8488{{{
    8589$> ./bin/ma5 -Re <folder_name> <analysis_name> <SFS_card>
    … …  
    101105ma5> submit
    102106}}}
    103 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].
     107First-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 extrapolations, for more info, see [https://arxiv.org/abs/1910.11418 arXiv:1910.11418].
    104108
    105109----