configurationdatabase

Intro

In this tutorial you will learn the meaning of the most important parameters of the MLHEP package.

LcpK0s_multiplicity_test:
  mass: 2.2864
  sel_reco_unp: "pt_cand>4 and pt_cand<8"
  sel_reco_singletrac_unp : null
  sel_gen_unp: "pt_cand>4 and pt_cand<8 and abs(z_vtx_gen)<10"
  sel_cen_unp: null
  #sel_good_evt_unp: "is_ev_rej == 0"
  sel_good_evt_unp: null
  sel_reco_skim: [null,null]
  sel_gen_skim: [null,null]
  sel_skim_binmin: [ 0,20] #list of nbins
  sel_skim_binmax: [20,100] #list of nbins
  var_binning: n_tracklets

The first part of the database includes the parameters needed to perform the conversion and skimming step. In particular:

• sel_reco_unp: is the selection applied at the conversion stage on the reco candidates
• sel_reco_singletrac_unp: option to apply single track selection at the unpacking level. In most of the cases this selection is already preapplied at the level of the TTree creation
• sel_gen_unp: is the selection applied at the conversion stage on the gen candidates
• sel_cen_unp: centrality selection applied at the conversion stage
• sel_reco_skim: is the selection applied at the skimming stage on the reco candidates
• sel_reco_skim: is the selection applied at the skimming stage on the reco candidates
• sel_skim_binmin, sel_skim_binmax: ranges used to bin the converted dataframes in skimmed dataframes. At the skimming level indeed the pandas dataframe is splitted into several subdataframes according to the value of a given variable. This is typically done in bins of pT if you are performing an analysis vs pt.
• var_binning: here you define the variable name used for splitting the datasets (e.g. pt_cand or multiplicity)

  variables:
    var_all: [cos_t_star, dca_K0s, signd0, imp_par_K0s, d_len_K0s, armenteros_K0s, ctau_K0s,
              cos_p_K0s, pt_prong0, pt_prong1, pt_prong2, imp_par_prong0, imp_par_prong1, imp_par_prong2,
              inv_mass, pt_cand, inv_mass_K0s, pt_K0s, cand_type, y_cand,
              run_number, ev_id, nsigTPC_Pr_0, nsigTOF_Pr_0]
    var_evt:
      data: [is_ev_rej, run_number, ev_id, centrality, z_vtx_reco, n_tracklets]
      mc: [is_ev_rej, run_number, ev_id, centrality, z_vtx_reco, z_vtx_gen, n_tracklets]
    var_gen: [y_cand, pt_cand, cand_type, run_number, ev_id]
    var_evt_match: [run_number, ev_id]
    var_training: [cos_t_star, signd0, dca_K0s, imp_par_K0s, d_len_K0s, armenteros_K0s, ctau_K0s, cos_p_K0s,  
                   imp_par_prong0, imp_par_prong1, imp_par_prong2, inv_mass_K0s, nsigTOF_Pr_0, nsigTPC_Pr_0] 

In this block of the frames you define:

• var_all: the list of variables you want to extract from the ROOT TTree and include in the Pandas dataframe for the reco tree
• ** var_gen**: the list of variables you want to extract from the ROOT TTree and include in the Pandas dataframe for the gen tree
• ** var_evt**: the list of variables you want to extract from the ROOT TTree and include in the Pandas dataframe in the event tree.