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` ====== Analysis Description Language (ADL)======

Develop a domain-specific language capable of describing the contents of an LHC analysis in a standard and unambiguous way.

[[https://www.dropbox.com/s/a9gn1iyg26h3qms/SekmenLHADL1906.pdf?dl=0|Link to Sezen's presentation]]

HSF Gitter forum for discussions: {{https://gitter.im/HSF/ADL|}}

**ADL-related projects/discussions in Les Houches:**
  * Estimation of overlaps in analysis using analysis descriptions ([[2019:groups:tools:correlations|correlations exercise]], [[2019:groups:tools:adl#Quantifying overlaps between analysis search regions using ADLs|how to use ADLs]])
  * Recasting tools/methods comparison exercise with ADLs ([[2019:groups:tools:recastcmp|recast comparison exercise]], [[2019:groups:tools:adl#ADLs for recasting|ADL implementation]])
  * Questions for us to answer:
    * What is the ideal physics content for an ADL?  An inclusive list discussed at the [[https://indico.cern.ch/event/769263/|Fermilab ADL workshop]] is  [[https://docs.google.com/document/d/1KrSarg4_c_Ph76SI8i9_b-wBEN6V90mWNLTaZq3y85E/edit?usp=sharing|in this googledoc]]. You are welcome to take a look and edit.
    * What is the best syntax for expressing composite particles, like Zs, tops, Higgsses, etc.?  How can we access information on the constituents?
    * What is the minimal list of math and HEP functions/operators we need for describing the analysis?
    * What are some challenging analysis descriptions to try?
    * How can we benefit from an ADL in analysis combination / finding out non-overlapping regions?

Parsing/interpreting tools for the current ADL:
  * **adl2tnm** (transpiler: python script converts adl to generic c++ analysis code) ([[https://github.com/hbprosper/adl2tnm|github]])
  * **lhada2rivet** (transpiler: python script converts LHADA to c++ code for Rivet) ([[https://github.com/lhada-hep/lhada/tree/master/lhada2rivet.d|github]])
  * **CutLang** (runtime interpreter of adl, no intermediate code generation, no compilation) ([[https://github.com/unelg/CutLang|github]], Jupyter notebook: [[https://gke.mybinder.org/v2/gh/unelg/CutLang/master?urlpath=/lab/tree/binder%2Fexample1.ipynb|binder link]])


==== Quantifying overlaps between analysis search regions using ADLs ====

Members: Sezen, Wolfgang (, Harrison)

Find and visualize overlaps in a model-independent way, without generating events.  Directly sample the event selection.  Useful for analysis design phase, or quick comparisons within experiments (e.g. Run2 CMS SUSY pMSSM combination)

  * Start from the analysis description, which lists objects and event selections.  
  * Construct a feature space from all mathematically orthogonal "basic" variables (e.g. MET, jet1.pt, jet2.pt, electron1.eta, ...).  
  * Randomly sample the feature space for each analysis based on cuts on the feature space components (jet1.pt > 100, MET > 299, etc.).
  * Use the sampled points to compute values for "composite" variables such as HT(jets), dphi(jets), MT(lepton, MET), etc.
  * Compare feature spaces between analyses, find and visualize overlaps and exclusions.
  * As a very simple first step, we simply check if two analyses are disjoint in any of the basic variables.

==== ADLs for recasting ====

Members: Sezen, (Harrison, Gokhan as parser developers)

The ADL file and the external function fMtautau are given below for the CMS soft dilepton analysis considered in the LH19 [[2019:groups:tools:recastcmp|recasting comparison]]

=== The ADL file ===

<file python>
info analysis
  title "Search for new physics in events with two soft oppositely charged leptons and missing transverse momentum in proton-proton collisions at sqrts = 13 TeV "
  experiment CMS
  id SUS-16-048
  publication Phys. Lett. B 782 (2018) 440
  sqrtS 13.0
  lumi 35.9
  arXiv 1801.01846
  hepdata 
  doi 10.1016/j.physletb.2018.05.062

# ANALYSIS OBJECTS
object muons
  take Muon
  select pT [] 3.5 30
  select abs(eta) < 2.4
  # ID/iso efficiencies already folded into efficiencies provided by CMS:
  # https://twiki.cern.ch/twiki/bin/view/CMSPublic/SUSMoriond2017ObjectsEfficiency

object electrons
  take Electron
  select pT [] 3.5 30
  select abs(eta) < 2.5
  # ID/iso efficiencies already folded into efficiencies provided by CMS:
  # https://twiki.cern.ch/twiki/bin/view/CMSPublic/SUSMoriond2017ObjectsEfficiency

object leptons
  take electrons
  take muons
  
object jets
  take Jet
  select pT > 25  
  select abs(Eta) < 2.4

object bjets
  take jets
  select BTag == 1

object MET
  take MissingET

# EVENT VARIABLES
define dilepton = leptons[0] + leptons[1]
define dielectron = electrons[0] + electrons[1]
define dimuon = muons[0] + muons[1]
define HT = sum(jets.pT)
define MTl1 = sqrt( 2*leptons[0].pT * MET.MET*(1-cos(MET.phi - leptons[0].phi )))
define MTl2 = sqrt( 2*leptons[1].pT * MET.MET*(1-cos(MET.phi - leptons[1].phi )))
define Mtautau = fMtautau(leptons[0], leptons[1], MET)

# EVENT SELECTION

# Dimuon selection
# This selection follows the cutflow table in LH recasting twiki
# https://phystev.cnrs.fr/wiki/_detail/2019:groups:tools:cms-sus-16-048_cutflow.png?id=2019%3Agroups%3Atools%3Arecastcmp
region CharginoDimuonPresel
  weight xsec 0.688016
  select size(muons) == 2
  select muons[0].pT [] 5 30
  select muons[0].charge * muons[1].charge == -1
  select dimuon.pT > 3 
  select dimuon.mass [] 4 50
  select dimuon.mass ][ 9 10.5
  select MET.MET [] 125 200
  weight trigger 0.65
  select size(jets) >= 0
  select size(jets) >= 1
  select HT > 100
  select (MET.MET / HT) [] 0.6 1.4
  select size(bjets) == 0
  select Mtautau ][ 0 160 
  select MTl1 < 70 and MTl2 < 70

# Additional selections from the analysis (not included in the recast study)

# Dilepton preselection
#region DileptonPresel
  # This selection follows Table 1 in the paper 
  # (for the time being, except for the isolation criteria)
#  select size(leptons) == 2
#  select size(electrons) == 2 or size(muons) == 2
#  select leptons[0].charge * leptons[1].charge == -1
#  select leptons[0].pT > 5
#  select leptons[1].pT > 5
#  select size(bjets) == 0
#  select size(electrons) == 2 or size(muons) == 2 ? mass(dilepton) [] 4 9 : mass(dilepton) [] 10.5 50
#  select dilepton.pT > 3
#  select size(muons) == 2 ? MET.pT > 125 : MET.pT > 200
#  select MET.pT / HT [] 0.6 1.4
#  select HT > 100
#  select MTl1 < 70 and MTl2 < 70

# Dielectron selection
#region CharginoDielectronPresel
# This selection follows the cutflow table in the twiki
#  select size(electrons) == 2
#  select electrons[0].pT [] 5 30
#  select electrons[0].charge * electrons[1].charge == -1
#  select dielectron.pT > 3 
#  select dielectron.mass [] 4 9 or dielectron.mass [] 10.5 50
#  reject dielectron.mass [] 9 10.5
#  select MET.pT [] 125 200
#  select MET.pT / HT [] 0.6 1.4
#  select size(jets) >= 1
#  select HT > 100
#  select size(bjets) == 0
#  reject Mtautau [] 0 160 
#  select MTl1 < 70 and MTl2 < 70

# Stop dimuon preselection
#region StopDimuonPresel
#  # This selection follows the cutflow table in the twiki
#  select size(muons) == 2
#  select muons[0].pT [] 5 30
#  select muons[0].charge * muons[1].charge == -1
#  select dimuon.pT > 3 
#  select dimuon.mass [] 4 50
#  reject dimuon.mass [] 9 10.5
#  select MET.pT [] 125 200
#  select MET.pT / HT [] 0.6 1.4
#  select size(jets) >= 1
#  select HT > 100
#  select size(bjets) == 0
#  reject Mtautau [] 0 160


</file>


=== The external function Mtautau ===

<file cpp>

  // Find m(tautau) for boosted taus with alligned decay products.
  // For leptons l1, l2 and neutrino(s) nu1, nu2 coming from tau1, tau2 decays,
  // collinearity implies p(nu1) = a * p(l1), p(nu2) = b * p(l2)
  //   1) Use the constraint from MET to find a and b
  //   2) Compute nu and tau momenta
  //   3) Find the ditau invariant mass
  // Suggested in arXiv:1401.1235 , used in CMS SUS-16-048
  // Coded by S. Sekmen
  double fMtautau(TLorentzVector& lep1, TLorentzVector& lep2, TLorentzVector& met){

    double px1 = lep1.Px();
    double py1 = lep1.Py();
    double px2 = lep2.Px();
    double py2 = lep2.Py();
    double metx = met.Px();
    double mety = met.Py();

    // Compute the solution of the two coupled equations:
    //     metx = a * px1 + b * px2
    //     mety = a * py1 + b * py2
    // for a and b:

    double a = (metx * py2 - mety * px2) / (px1 * py2 - py1 * px2);
    double b = (metx * py1 - mety * px1) / (px2 * py1 - py2 * px1);

   // Neutrino vectors
    TLorentzVector nu1, nu2;
    nu1.SetPxPyPzE(a*px1, a*py1, 0., sqrt(a*px1 * a*px1 + a*py1 * a*py1));
    nu2.SetPxPyPzE(b*px2, b*py2, 0., sqrt(b*px2 * b*px2 + b*py2 * b*py2));

    // Reconstruct the taus from leptons and neutrinos
    TLorentzVector tau1 = lep1 + nu1;
    TLorentzVector tau2 = lep2 + nu2;

    // Reconstruct the Z from Z --> tau tau
    TLorentzVector Z = tau1 + tau2;

    return Z.M();
  }

</file>
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