add noise filters, good PV requirement, and loose puId to SL selection

hbw/config/config_run2.py

@@ -49,6 +49,8 @@ def add_config(
 
     # create a config by passing the campaign, so id and name will be identical
     cfg = analysis.add_config(campaign, name=config_name, id=config_id, tags=analysis.tags)
+    cfg.add_tag("is_run2")
+
     if cfg.has_tag("is_sl"):
         cfg.x.lepton_tag = "sl"
     elif cfg.has_tag("is_dl"):
@@ -423,7 +425,7 @@ def make_jme_filename(jme_aux, sample_type, name, era=None):
             "btag_weight*",
         } | four_vec(  # Jets
             {"Jet", "Bjet", "VBFJet"},
-            {"btagDeepFlavB", "hadronFlavour"},
+            {"btagDeepFlavB", "hadronFlavour", "qgl"},
         ) | four_vec(  # FatJets
             {"FatJet", "HbbJet"},
             {

hbw/selection/common.py

@@ -234,8 +234,72 @@ def sl_boosted_jet_selection(
 )
 
 
+@selector(
+    exposed=False,
+)
+def noise_filter(
+    self: Selector,
+    events: ak.Array,
+    results: SelectionResult,
+    **kwargs,
+) -> Tuple[ak.Array, SelectionResult]:
+    mask = ak.Array(np.ones(len(events), dtype=bool))
+    for flag in self.noise_filter:
+        mask = mask & events.Flag[flag]
+
+    results.steps["noise_filter"] = mask
+    return events, results
+
+
+@noise_filter.init
+def noise_filter_init(self: Selector):
+    if not getattr(self, "dataset_inst", None):
+        return
+
+    # TODO: make campaign dependent
+    self.noise_filter = {
+        "goodVertices",
+        "globalSuperTightHalo2016Filter",
+        "HBHENoiseFilter",
+        "HBHENoiseIsoFilter",
+        "EcalDeadCellTriggerPrimitiveFilter",
+        "BadPFMuonFilter",
+        "BadPFMuonDzFilter",
+        # "hfNoisyHitsFilter",  # optional for UL
+        "eeBadScFilter",  # might be data only
+        # "ecalBadCalibReducedMINIAODFilter",  # 2017 and 2018 only, only in MiniAOD
+    }
+
+    if self.dataset_inst.has_tag("is_hbw") and self.config_inst.has_tag("is_run2"):
+        # missing in MiniAOD HH samples
+        self.event_flats.remove("BadPFMuonDzFilter")
+
+    if self.config_inst.has_tag("is_run3"):
+        self.noise_filter.add("ecalBadCalibFilter")
+    # if self.dataset_inst.is_data:
+    #     self.noise_filter.add("eeBadScFilter")
+
+    self.uses = {f"Flag.{flag}" for flag in self.noise_filter}
+
+
+@selector(
+    uses={"PV.npvsGood"},
+    exposed=False,
+)
+def primary_vertex(
+    self: Selector,
+    events: ak.Array,
+    results: SelectionResult,
+    **kwargs,
+) -> Tuple[ak.Array, SelectionResult]:
+    """ requires at least one good primary vertex """
+    results.steps["good_vertex"] = events.PV.npvsGood >= 1
+    return events, results
+
+
 @selector(
     uses={
+        noise_filter, primary_vertex,
         process_ids, attach_coffea_behavior,
         mc_weight, large_weights_killer,
     },
@@ -271,6 +335,10 @@ def pre_selection(
     # prepare the selection results that are updated at every step
     results = SelectionResult()
 
+    # apply some general quality criteria on events
+    events, results = self[noise_filter](events, results, **kwargs)
+    events, results = self[primary_vertex](events, results, **kwargs)
+
     return events, results
 
 
@@ -317,6 +385,10 @@ def log_fraction(stats_key: str, msg: str | None = None):
     if self.config_inst.x("do_cutflow_features", False):
         events = self[cutflow_features](events, results=results, **kwargs)
 
+    # temporary fix for optional types from Calibration (e.g. events.Jet.pt --> ?float32)
+    # TODO: remove as soon as possible as it might lead to weird bugs when there are none entries in inputs
+    events = ak.fill_none(events, EMPTY_FLOAT)
+
     return events, results
 
 

hbw/selection/sl.py

@@ -14,6 +14,7 @@
 from columnflow.production.categories import category_ids
 from columnflow.production.processes import process_ids
 
+from hbw.util import four_vec
 from hbw.selection.common import (
     masked_sorted_indices, sl_boosted_jet_selection, vbf_jet_selection,
     pre_selection, post_selection,
@@ -27,7 +28,9 @@
 
 
 @selector(
-    uses={"Jet.pt", "Jet.eta", "Jet.phi", "Jet.mass", "Jet.btagDeepFlavB", "Jet.jetId"},
+    uses=four_vec("Jet", {
+        "btagDeepFlavB", "jetId", "puId",
+    }),
     produces={"cutflow.n_jet", "cutflow.n_deepjet_med"},
     exposed=True,
 )
@@ -38,19 +41,23 @@ def sl_jet_selection(
     stats: defaultdict,
     **kwargs,
 ) -> Tuple[ak.Array, SelectionResult]:
+    # NanoAOD documentation: https://twiki.cern.ch/twiki/bin/view/CMSPublic/WorkBookNanoAOD#Jets
     # HH -> bbWW(qqlnu) jet selection
     # - require at least 3 jets with pt>30, eta<2.4
     # - require at least 1 jet with pt>30, eta<2.4, b-score>0.3040 (Medium WP)
 
     # assign local index to all Jets
     events = set_ak_column(events, "local_index", ak.local_index(events.Jet))
-
     # jets
     jet_mask_loose = (events.Jet.pt > 5) & abs(events.Jet.eta < 2.4)
     jet_mask = (
         (events.Jet.pt > 25) & (abs(events.Jet.eta) < 2.4) & (events.Jet.jetId == 6) &
         ak.all(events.Jet.metric_table(lepton_results.x.lepton) > 0.4, axis=2)
     )
+    # apply loose Jet puId to jets with pt below 50 GeV
+    jet_pu_mask = (events.Jet.puId >= 4) | (events.Jet.pt > 50)
+    jet_mask = jet_mask & jet_pu_mask
+
     events = set_ak_column(events, "cutflow.n_jet", ak.sum(jet_mask, axis=1))
     jet_sel = events.cutflow.n_jet >= 3
     jet_indices = masked_sorted_indices(jet_mask, events.Jet.pt)
@@ -269,7 +276,8 @@ def sl(
 
     # combined event selection after all steps except b-jet selection
     results.steps["all_but_bjet"] = (
-        # NOTE: the boosted selection actually includes a b-jet selection...
+        results.steps.noise_filter &
+        results.steps.good_vertex &
         (results.steps.Jet | results.steps.HbbJet_no_bjet) &
         results.steps.Lepton &
         results.steps.VetoLepton &
@@ -285,6 +293,7 @@ def sl(
         results.steps.all_but_bjet &
         ((results.steps.Jet & results.steps.Bjet) | results.steps.HbbJet)
     )
+    results.steps["all"] = results.event
 
     # build categories
     events, results = self[post_selection](events, results, stats, **kwargs)