Changes between Version 2 and Version 3 of SFS

Timestamp:

Jun 6, 2020, 12:44:16 PM (5 years ago)

Author:

jackaraz

Comment:

--

SFS

@@ -2 +2 @@
 == Simplified - Fast Simulation (SFS) of detector response ==
 
-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. SFS contains three submodules, namely `reco_efficiency`, `smearer` and `tagger`. Default ATLAS and CMS cards can be found through corresponding links.
+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. Although we provide default ATLAS and 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. 
 
-* Prerequisites: !FastJet, 
+* Prerequisites: only !FastJet
 {{{
 $> ./bin/ma5 -R
 ma5> install fastjet
 }}}
-and you are ready to go. 
 
-under development...
+SFS contains three submodules, namely `reco_efficiency`, `smearer` and `tagger`. These submodules set a probability distribution to reconstruct a given object, smear gave objects four-momentum using normalized Gaussian function and sets identification efficiencies respectively. The goal is to create a CPU efficient, user-friendly, easy-to-use and inclusive 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. It is also possible to manipulate jet constituents for substructure analyses which are described below.
+
+
+== 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;
+
+{{{
+ma5> define reco_efficiency <obj> <func> [<domain>]
+}}}
+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. `<func>` can also include any functional forms like trigonometric or hyperbolic functions. `<dom>` represents the domain where this function lives. It creates a piecewise function to construct probability distribution for the given object. This piece of input will generate a probability distribution for the desired object to decide if its going to be used in the analysis or not. 
+
+Example:
+{{{
+ma5> define reco_efficiency e 0.0   [pt <= 10.0 or abseta > 2.5]
+ma5> define reco_efficiency e 0.7   [pt > 10.0 and abseta <= 1.5]
+ma5> define reco_efficiency e 0.55  [pt > 10.0 and abseta > 1.5 and abseta <= 2.5]
+}}}
+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 has to be separated with `and` or `or` keywords to be effective.
+
+
+
+== Object Smearing ==
+
+
+
+== Particle Identification ==
+
+
+
+== SFS in Expert Mode ==
+
+
+
+== LHC Recasting with SFS ==
+