up

Author: farakiko

boostedhiggs/hwwprocessor.py

@@ -350,6 +350,8 @@ def _build_objects(self, events):
         FirstFatjet = ak.firsts(good_fatjets[:, 0:1])
         SecondFatjet = ak.firsts(good_fatjets[:, 1:2])
 
+        bjet_selector = (jets.delta_r(candidatefj) > 0.8) & (abs(jets.eta) < 2.5)
+
         objects = {
             "muons": muons,
             "good_muons": good_muons,
@@ -381,6 +383,7 @@ def _build_objects(self, events):
             "jet2": jet2,
             "FirstFatjet": FirstFatjet,
             "SecondFatjet": SecondFatjet,
+            "bjet_selector": bjet_selector,
         }
 
         return objects
@@ -414,6 +417,8 @@ def _derive_variables(self, events, objects):
         FirstFatjet = objects["FirstFatjet"]
         SecondFatjet = objects["SecondFatjet"]
 
+        bjet_selector = objects["bjet_selector"]
+
         # VH jet
         minDeltaR = ak.argmin(candidatelep_p4.delta_r(good_fatjets), axis=1)  # similar to fj_idx_lep but without keepdims
         fatJetIndices = ak.local_index(good_fatjets, axis=1)
@@ -445,7 +450,6 @@ def _derive_variables(self, events, objects):
         lep_miso = candidatelep.miniPFRelIso_all  # miniso for candidate lepton
 
         # OBJECT: b-jets (only for jets with abs(eta)<2.5)
-        bjet_selector = (jets.delta_r(candidatefj) > 0.8) & (abs(jets.eta) < 2.5)
         ak4_bjet_candidate = jets[bjet_selector]
 
         # OBJECT: VBF variables
@@ -522,7 +526,6 @@ def _derive_variables(self, events, objects):
             "fj_eta": candidatefj.eta,
             "fj_phi": candidatefj.phi,
             "fj_mass": candidatefj.msdcorr,
-            "fj_idx_lep": fj_idx_lep,
             "VH_fj": VH_fj,
             "ak4_outside_ak8": ak4_outside_ak8,
             "n_loose_taus_mu": n_loose_taus_mu,
@@ -588,7 +591,6 @@ def _derive_variables(self, events, objects):
             "loose_lep1_pt": ak.firsts(muons[loose_muons1][ak.argsort(muons[loose_muons1].pt, ascending=False)]).pt,
             "msk_leptonic_taus": msk_leptonic_taus,
             "fj_mass_raw": good_fatjets[fj_idx_lep].mass,
-            "bjet_selector": bjet_selector,
         }
 
         if self.isMC:
@@ -600,7 +602,7 @@ def _derive_variables(self, events, objects):
 
     def _apply_JEC(self, events, objects, variables):
 
-        fj_idx_lep = variables["fj_idx_lep"]
+        fj_idx_lep = objects["fj_idx_lep"]
         candidatefj = objects["candidatefj"]
         jec_shifted_fatjetvars = objects["jec_shifted_fatjetvars"]
         jmsr_shifted_fatjetvars = objects["jmsr_shifted_fatjetvars"]
@@ -827,7 +829,7 @@ def _store_MCweights(self, dataset, events, objects, variables):
 
         candidatelep = objects["candidatelep"]
         jets = objects["jets"]
-        bjet_selector = variables["bjet_selector"]
+        bjet_selector = objects["bjet_selector"]
 
         for ch in self._channels:
             if self._year in ("2016", "2017"):
@@ -1005,9 +1007,9 @@ def process(self, events: ak.Array):
                     (pf_cands, gen_parts_eta_phi, gen_parts_pt_mass, ak8_jets, bgen_parts_eta_phi, genlep) = getLPweights(
                         dataset,
                         events[selection_ch],
-                        variables["candidatefj"][selection_ch],
-                        variables["fj_idx_lep"][selection_ch],
-                        variables["candidatelep_p4"][selection_ch],
+                        objects["candidatefj"][selection_ch],
+                        objects["fj_idx_lep"][selection_ch],
+                        objects["candidatelep_p4"][selection_ch],
                     )
 
                     lpvars = {}
@@ -1048,7 +1050,7 @@ def process(self, events: ak.Array):
                         pnet_vars = runInferenceTriton(
                             self.tagger_resources_path,
                             events[selection_ch],
-                            variables["fj_idx_lep"][selection_ch],
+                            objects["fj_idx_lep"][selection_ch],
                             model_name=model_name,
                         )
                         pnet_df = self.ak_to_pandas(pnet_vars)