diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 520c889a..c1acd148 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -1,10 +1,11 @@
 ci:
   autoupdate_commit_msg: "chore: update pre-commit hooks"
   autofix_commit_msg: "style: pre-commit fixes"
+  autoupdate_schedule: quarterly
 
 repos:
   - repo: https://github.com/psf/black-pre-commit-mirror
-    rev: "24.2.0"
+    rev: "24.3.0"
     hooks:
       - id: black-jupyter
         args: [--line-length=100]
@@ -47,7 +48,7 @@ repos:
   #       args: [--prose-wrap=always]
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: "v0.3.2"
+    rev: "v0.3.3"
     hooks:
       - id: ruff
         args: ["--fix", "--show-fixes"]
@@ -70,7 +71,7 @@ repos:
         args: ["--ignore-words", ".codespell-whitelist.txt"]
 
   - repo: https://github.com/shellcheck-py/shellcheck-py
-    rev: "v0.9.0.6"
+    rev: "v0.10.0.1"
     hooks:
       - id: shellcheck
 
diff --git a/src/HHbbVV/VBF_binder/VBFKinematicsStudyRK.ipynb b/src/HHbbVV/VBF_binder/VBFKinematicsStudyRK.ipynb
index ce390363..ebd7a1dd 100644
--- a/src/HHbbVV/VBF_binder/VBFKinematicsStudyRK.ipynb
+++ b/src/HHbbVV/VBF_binder/VBFKinematicsStudyRK.ipynb
@@ -49,7 +49,8 @@
    "source": [
     "events = nanoevents.NanoEventsFactory.from_root(\n",
     "    # \"root://cmseos.fnal.gov///store/user/lpcpfnano/cmantill/v2_3/2018/HH/VBF_HHTobbVV_CV_1_C2V__C3_1_TuneCP5_13TeV-madgraph-pythia8/VBF_HHTobbVV_CV_1_C2V_1_C3_1/220808_150149/0000/nano_mc2018_1-1.root\",\n",
-    "    \"root://cmseos.fnal.gov///store/user/lpcpfnano/cmantill/v2_3/2018/HH/VBF_HHTobbVV_CV_1_C2V_0_C3_1_TuneCP5_13TeV-madgraph-pythia8/VBF_HHTobbVV_CV_1_C2V_0_C3_1/220808_150000/0000/nano_mc2018_1-1.root\",\n",
+    "    # \"root://cmseos.fnal.gov///store/user/lpcpfnano/cmantill/v2_3/2018/HH/VBF_HHTobbVV_CV_1_C2V_0_C3_1_TuneCP5_13TeV-madgraph-pythia8/VBF_HHTobbVV_CV_1_C2V_0_C3_1/220808_150000/0000/nano_mc2018_1-1.root\",\n",
+    "    \"/uscms/home/rkansal/nobackup/vbf.root\",\n",
     "    schemaclass=nanoevents.NanoAODSchema,\n",
     ").events()\n",
     "\n",
@@ -223,15 +224,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "ak4_jet_selection = {  # noqa: RUF012\n",
-    "    \"pt\": 25,\n",
-    "    \"eta\": 2.7,\n",
-    "    \"jetId\": \"tight\",\n",
-    "    \"puId\": \"medium\",\n",
-    "    \"dR_fatjetbb\": 1.2,\n",
-    "    \"dR_fatjetVV\": 0.8,\n",
-    "}\n",
-    "\n",
     "# ak8 jet preselection\n",
     "preselection = {  # noqa: RUF012\n",
     "    \"pt\": 300.0,\n",
@@ -259,6 +251,10 @@
    "source": [
     "fatjets = events.FatJet\n",
     "\n",
+    "fatjets = ak.pad_none(\n",
+    "    fatjets[(fatjets.pt > 300) * (fatjets.isTight) * (np.abs(fatjets.eta) <= 2.4)], 2, axis=1\n",
+    ")\n",
+    "\n",
     "# particlenet xbb vs qcd\n",
     "\n",
     "txbb = pad_val(\n",
@@ -273,6 +269,28 @@
     "bb_mask = np.stack((bb_mask, ~bb_mask)).T"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "6922c210",
+   "metadata": {},
+   "source": [
+    "Leptons"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fa386f98",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "electrons = events.Electron\n",
+    "electrons = electrons[(electrons.pt > 5) & (electrons.cutBased >= electrons.LOOSE)]\n",
+    "\n",
+    "muons = events.Muon\n",
+    "muons = muons[(muons.pt > 7) & (muons.looseId)]"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -280,6 +298,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "ak4_jet_selection = {  # noqa: RUF012\n",
+    "    \"pt\": 15,\n",
+    "    \"eta_min\": 0,\n",
+    "    \"eta_max\": 4.9,\n",
+    "    \"jetId\": \"tight\",\n",
+    "    \"puId\": \"medium\",\n",
+    "    \"dR_fatjetbb\": 1.15,\n",
+    "    \"dR_fatjetVV\": 0.95,\n",
+    "}\n",
+    "\n",
     "jets = events.Jet\n",
     "\n",
     "# dR_fatjetVV = 0.8 used from last two cells of VBFgenInfoTests.ipynb with data generated from SM signal vbf\n",
@@ -288,32 +316,47 @@
     "vbf_jet_mask = (\n",
     "    jets.isTight\n",
     "    & (jets.pt > ak4_jet_selection[\"pt\"])\n",
-    "    & (np.abs(jets.eta) < 4.7)\n",
+    "    & (np.abs(jets.eta) < ak4_jet_selection[\"eta_max\"])\n",
+    "    & (np.abs(jets.eta) >= ak4_jet_selection[\"eta_min\"])\n",
     "    # medium puId https://twiki.cern.ch/twiki/bin/viewauth/CMS/PileupJetIDUL\n",
     "    & ((jets.pt > 50) | ((jets.puId & 2) == 2))\n",
     "    & (\n",
-    "        ak.all(\n",
-    "            jets.metric_table(\n",
-    "                ak.singletons(ak.pad_none(fatjets, num_jets, axis=1, clip=True)[bb_mask])\n",
-    "            )\n",
-    "            > ak4_jet_selection[\"dR_fatjetbb\"],\n",
-    "            axis=-1,\n",
-    "        )\n",
+    "        ak.pad_none(fatjets, num_jets, axis=1, clip=True)[bb_mask].delta_r(jets)\n",
+    "        > ak4_jet_selection[\"dR_fatjetbb\"]\n",
     "    )\n",
     "    & (\n",
-    "        ak.all(\n",
-    "            jets.metric_table(\n",
-    "                ak.singletons(ak.pad_none(fatjets, num_jets, axis=1, clip=True)[~bb_mask])\n",
-    "            )\n",
-    "            > ak4_jet_selection[\"dR_fatjetVV\"],\n",
-    "            axis=-1,\n",
-    "        )\n",
+    "        ak.pad_none(fatjets, num_jets, axis=1, clip=True)[~bb_mask].delta_r(jets)\n",
+    "        > ak4_jet_selection[\"dR_fatjetVV\"]\n",
     "    )\n",
+    "    & ak.all(jets.metric_table(electrons) > 0.4, axis=2)\n",
+    "    & ak.all(jets.metric_table(muons) > 0.4, axis=2)\n",
     ")\n",
     "\n",
     "vbf_jets = jets[vbf_jet_mask]"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b44eb17d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sel = ak.fill_none(\n",
+    "    (\n",
+    "        (txbb[bb_mask] > 0.97)\n",
+    "        * (fatjets.particleNet_H4qvsQCD[~bb_mask] > 0.6)\n",
+    "        * (fatjets.pt[:, 0] > 500)\n",
+    "        * (fatjets.pt[:, 1] > 400)\n",
+    "        * (np.abs(fatjets[:, 0].delta_phi(fatjets[:, 1])) > 2.6)\n",
+    "        * (np.abs(fatjets[:, 0].eta - fatjets[:, 1].eta) < 2.0)\n",
+    "    ),\n",
+    "    False,\n",
+    ")\n",
+    "\n",
+    "matching_efficiency(gen_quarks[sel], vbf_jets[sel])"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -321,15 +364,54 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "etas = pad_val(vbf_jets.eta, 2, axis=1)\n",
+    "etas = pad_val(\n",
+    "    vbf_jets[ak.fill_none(sel & (ak.count(vbf_jets.pt, axis=1) >= 2), False)].eta, 2, axis=1\n",
+    ")\n",
     "\n",
     "plt.rcParams.update({\"font.size\": 24})\n",
     "plt.figure(figsize=(12, 12))\n",
-    "plt.hist(np.abs(etas[:, 0] - etas[:, 1]), np.arange(0, 6, 0.25), histtype=\"step\", density=True)\n",
+    "plt.hist(np.abs(etas[:, 0] - etas[:, 1]), np.arange(0, 10, 0.25), histtype=\"step\", density=True)\n",
     "plt.xlabel(r\"$\\eta_{jj}$\")\n",
     "plt.ylabel(\"Events (A. U.)\")\n",
     "plt.show()"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f820fb8e",
+   "metadata": {},
+   "source": [
+    "Checking how many events have jets which match with the gen quarks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b5c0658f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "jets = events.Jet\n",
+    "np.mean(ak.all(ak.any(gen_quarks.metric_table(jets) < 0.4, axis=2), axis=1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7901612f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(8, 8))\n",
+    "plt.hist(\n",
+    "    ak.sum(ak.any(gen_quarks.metric_table(jets) < 0.4, axis=2), axis=1),\n",
+    "    [0, 1, 2, 3],\n",
+    "    histtype=\"step\",\n",
+    "    density=True,\n",
+    ")\n",
+    "plt.title(\"Fraction of Gen VBF Quarks which have\\n a matching AK4 jet in the event\")\n",
+    "plt.show()"
+   ]
   }
  ],
  "metadata": {