Update POWHEG scaling

Author: qgp

machine_learning_hep/analysis/analyzer_jets.py

@@ -1108,9 +1108,6 @@ def estimate_feeddown(self):
                         bins_obs[var],
                     )
                     fill_hist_fast(h3_fd_gen_orig[var], df[["pt_jet", "pt_cand", f"{colname}"]])
-                    self._save_hist(
-                        project_hist(h3_fd_gen_orig[var], [0, 2], {}), f"fd/h_ptjet-{var}_feeddown_gen_noeffscaling.png"
-                    )
 
             case "sim":
                 h3_fd_gen_orig = {}
@@ -1120,15 +1117,11 @@ def estimate_feeddown(self):
                         label = f"-{var}" if var else ""
                         if fh := rfile.Get(f"h_mass-ptjet-pthf{label}"):
                             h3_fd_gen_orig[var] = project_hist(fh, list(range(1, get_dim(fh))), {})
-                            ensure_sumw2(h3_fd_gen_orig[var])
-                            self._save_hist(
-                                project_hist(h3_fd_gen_orig[var], [0, 2], {}),
-                                f"fd/h_ptjet-{var}_feeddown_genonly_noeffscaling.png",
-                            )
                     h_norm = rfile.Get("histonorm")
-                    powheg_xsection_scale_factor = h_norm.GetBinContent(5)
+                    powheg_xsection_avg = h_norm.GetBinContent(6) / h_norm.GetBinContent(5)
+                    powheg_xsection_scale_factor = powheg_xsection_avg / h_norm.GetBinContent(5)
                     self.logger.info("powheg_xsection_scale_factor = %f", powheg_xsection_scale_factor)
-                    self.logger.info("POWHEG luminosity (mb^{-1}): %g", 1.0 / powheg_xsection_scale_factor)
+                    self.logger.info("POWHEG luminosity (mb^{-1}): %g", 1. / powheg_xsection_scale_factor)
 
             case fd_input:
                 self.logger.critical("Invalid feeddown input %s", fd_input)

machine_learning_hep/data/data_run3/database_ml_parameters_D0Jet_pp.yml

@@ -71,6 +71,7 @@ D0Jet_pp:
         filter: "abs(fPosZ) < 10."
 
       coll_powheg_fd:
+        level: fd
         trees:
           O2d0cmcpjetmcco: [fEventWeight]
       collcnt_mc:

machine_learning_hep/processer_jet.py

@@ -240,7 +240,7 @@ def process_histomass_single(self, index):
 
         with TFile.Open(self.l_histomass[index], "recreate") as _:
             dfevtorig = read_df(self.l_evtorig[index])
-            histonorm = TH1F("histonorm", "histonorm", 5, 0, 5)
+            histonorm = TH1F("histonorm", "histonorm", 6, 0, 6)
             histonorm.SetBinContent(1, len(dfquery(dfevtorig, self.s_evtsel)))
             if self.l_collcnt:
                 dfcollcnt = read_df(self.l_collcnt[index])
@@ -259,14 +259,16 @@ def process_histomass_single(self, index):
             if self.l_wgt:
                 self.logger.info("Filling event weights")
                 dfwgt = read_df(self.l_wgt[index])
-                histonorm.SetBinContent(5, dfwgt["fEventWeight"].sum())
+                histonorm.SetBinContent(5, len(dfwgt["fEventWeight"]))
+                histonorm.SetBinContent(6, dfwgt["fEventWeight"].sum())
             elif self.datatype == "fd":
                 self.logger.warning("No event weights found, empty list: %s", self.l_wgt)
             get_axis(histonorm, 0).SetBinLabel(1, "N_{evt}")
             get_axis(histonorm, 0).SetBinLabel(2, "N_{coll}")
             get_axis(histonorm, 0).SetBinLabel(3, "N_{coll}^{TVX}")
             get_axis(histonorm, 0).SetBinLabel(4, "N_{BC}^{TVX}")
-            get_axis(histonorm, 0).SetBinLabel(5, "w_{POWHEG}")
+            get_axis(histonorm, 0).SetBinLabel(5, "N_{POWHEG}")
+            get_axis(histonorm, 0).SetBinLabel(6, "sum_xs_{POWHEG}")
             histonorm.Write()
 
             if self.datatype != "fd":