diff --git a/models/bdt_trained_on_hhh_qcd.json b/models/bdt_trained_on_hhh_qcd.json
new file mode 100644
index 0000000..dd28927
--- /dev/null
+++ b/models/bdt_trained_on_hhh_qcd.json
@@ -0,0 +1 @@
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diff --git a/notebooks/bdt/hhh_qcd_bdt.ipynb b/notebooks/bdt/hhh_qcd_bdt.ipynb
index 64825d8..a48c50b 100644
--- a/notebooks/bdt/hhh_qcd_bdt.ipynb
+++ b/notebooks/bdt/hhh_qcd_bdt.ipynb
@@ -34,7 +34,7 @@
    },
    "outputs": [],
    "source": [
-    "fn_delphes = \"../../data/delphes/v2/merged_training_all.h5\"\n",
+    "fn_delphes = \"../../data/delphes/v2/hhh_training.h5"\"\n",
     "f2 = h5py.File(fn_delphes, \"r+\")"
    ]
   },
@@ -304,7 +304,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "6254969 1948467\n"
+      "553548 164601\n"
      ]
     }
    ],
@@ -562,7 +562,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "booster.save_model('bdt_trained_on_all.json')"
+    "booster.save_model('../../models/bdt_trained_on_hhh_qcd.json')"
    ]
   },
   {
@@ -600,7 +600,7 @@
     {
      "data": {
       "text/plain": [
-       "0.95553684"
+       "0.95626426"
       ]
      },
      "execution_count": 21,
@@ -621,7 +621,7 @@
     {
      "data": {
       "text/plain": [
-       "0.9346559"
+       "0.93498826"
       ]
      },
      "execution_count": 22,
@@ -642,7 +642,7 @@
     {
      "data": {
       "text/plain": [
-       "0.91041"
+       "0.911348"
       ]
      },
      "execution_count": 23,
diff --git a/notebooks/mix_baseline_bdt/hh.ipynb b/notebooks/mix_baseline_bdt/hh.ipynb
new file mode 100644
index 0000000..38d77d7
--- /dev/null
+++ b/notebooks/mix_baseline_bdt/hh.ipynb
@@ -0,0 +1,735 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "2664c5a5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import itertools\n",
+    "import logging\n",
+    "from pathlib import Path\n",
+    "import numba as nb\n",
+    "\n",
+    "import awkward as ak\n",
+    "import click\n",
+    "import h5py\n",
+    "import numpy as np\n",
+    "import vector\n",
+    "\n",
+    "#from src.data.cms.convert_to_h5 import MIN_JETS, N_JETS, N_FJETS\n",
+    "\n",
+    "vector.register_awkward()\n",
+    "\n",
+    "logging.basicConfig(level=logging.INFO)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "fad54964",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import xgboost as xgb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "06140c21",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# a function that loads jets from hhh_test.h5\n",
+    "def load_jets(in_file):\n",
+    "    # load jets from the h5\n",
+    "    pt = ak.Array(in_file[\"INPUTS\"][\"Jets\"][\"pt\"])\n",
+    "    eta = ak.Array(in_file[\"INPUTS\"][\"Jets\"][\"eta\"])\n",
+    "    phi = ak.Array(in_file[\"INPUTS\"][\"Jets\"][\"phi\"])\n",
+    "    btag = ak.Array(in_file[\"INPUTS\"][\"Jets\"][\"btag\"])\n",
+    "    mass = ak.Array(in_file[\"INPUTS\"][\"Jets\"][\"mass\"])\n",
+    "    mask = ak.Array(in_file[\"INPUTS\"][\"Jets\"][\"MASK\"])\n",
+    "\n",
+    "    jets = ak.zip(\n",
+    "        {\n",
+    "            \"pt\": pt,\n",
+    "            \"eta\": eta,\n",
+    "            \"phi\": phi,\n",
+    "            \"btag\": btag,\n",
+    "            \"mass\": mass,\n",
+    "            \"mask\": mask\n",
+    "        },\n",
+    "        with_name=\"Momentum4D\",\n",
+    "    )\n",
+    "    \n",
+    "    return jets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "a356a240",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# a function that loads fat jets from hhh_test.h5\n",
+    "def load_fjets(in_file):\n",
+    "     # load fatjets from h5\n",
+    "    fj_pt = ak.Array(in_file[\"INPUTS\"][\"BoostedJets\"][\"fj_pt\"])\n",
+    "    fj_eta = ak.Array(in_file[\"INPUTS\"][\"BoostedJets\"][\"fj_eta\"])\n",
+    "    fj_phi = ak.Array(in_file[\"INPUTS\"][\"BoostedJets\"][\"fj_phi\"])\n",
+    "    fj_mass = ak.Array(in_file[\"INPUTS\"][\"BoostedJets\"][\"fj_mass\"])\n",
+    "    fj_mask = ak.Array(in_file[\"INPUTS\"][\"BoostedJets\"][\"MASK\"])\n",
+    "\n",
+    "    fjets = ak.zip(\n",
+    "        {\n",
+    "            \"pt\": fj_pt,\n",
+    "            \"eta\": fj_eta,\n",
+    "            \"phi\": fj_phi,\n",
+    "            'mass': fj_mass,\n",
+    "            'mask': fj_mask\n",
+    "        },\n",
+    "        with_name=\"Momentum4D\"\n",
+    "    )\n",
+    "    \n",
+    "    return fjets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "2ae41356",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@nb.njit\n",
+    "def match_fjet_to_jet(fjets, jets, builder, FJET_DR = 0.8):\n",
+    "    for fjets_event, jets_event in zip(fjets, jets):\n",
+    "        builder.begin_list()\n",
+    "        for i, jet in enumerate(jets_event):\n",
+    "            match_idx = -1\n",
+    "            for j, fjet in enumerate(fjets_event):\n",
+    "                if jet.deltaR(fjet) < FJET_DR:\n",
+    "                    match_idx = j\n",
+    "            builder.append(match_idx)\n",
+    "        builder.end_list()\n",
+    "\n",
+    "    return builder"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "d93ef2b6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def to_np_array(ak_array, axis=-1, max_n=10, pad=0):\n",
+    "    return ak.fill_none(ak.pad_none(ak_array, max_n, clip=True, axis=axis), pad, axis=axis).to_numpy()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "42e8de48",
+   "metadata": {},
+   "source": [
+    "### BDT WP by background misidentification rate\n",
+    "Tight: 0.3%\n",
+    "\n",
+    "Medium: 1%\n",
+    "\n",
+    "Loose: 2%"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "f2ad1605",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "WP_tight = 0.95626426\n",
+    "WP_medium = 0.93498826\n",
+    "WP_loose = 0.911348"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d7cceadc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "WP = WP_loose\n",
+    "pred_file = \"../../predictions/hh_chi2_loose_bdt_baseline_predictions_west.h5\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6d2502b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_file = \"../../data/delphes/v2/hh_testing.h5\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "ea6b8317",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "in_file = h5py.File(test_file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "401061e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<KeysViewHDF5 ['MASK', 'fj_charge', 'fj_chargedenergyfrac', 'fj_cosphi', 'fj_ehadovereem', 'fj_eta', 'fj_mass', 'fj_ncharged', 'fj_neutralenergyfrac', 'fj_nneutral', 'fj_phi', 'fj_pt', 'fj_sdmass', 'fj_sinphi', 'fj_tau21', 'fj_tau32']>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "in_file[\"INPUTS\"][\"BoostedJets\"].keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "f92218a5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<HDF5 dataset \"MASK\": shape (60369, 2), type \"|b1\">"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "in_file[\"INPUTS\"][\"BoostedJets\"]['MASK']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "1632da1f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# preliminary\n",
+    "N_JETS = 10\n",
+    "HIGGS_MASS = 125"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "92c92853",
+   "metadata": {},
+   "source": [
+    "### Reconstruct boosted H"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "1820e38a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_test_XY(file):\n",
+    "    bh1 = file[\"TARGETS\"][\"bh1\"][\"bb\"][:]\n",
+    "    bh2 = file[\"TARGETS\"][\"bh2\"][\"bb\"][:]\n",
+    "\n",
+    "    mask_fj1_bh1 = (bh1 == 0).astype(float)\n",
+    "    mask_fj1_bh2 = (bh2 == 0).astype(float)\n",
+    "    mask_fj1 = mask_fj1_bh1 + mask_fj1_bh2\n",
+    "\n",
+    "    mask_fj2_bh1 = (bh1 == 1).astype(float)\n",
+    "    mask_fj2_bh2 = (bh2 == 1).astype(float)\n",
+    "    mask_fj2 = mask_fj2_bh1 + mask_fj2_bh2\n",
+    "\n",
+    "    mask_signal = np.stack([mask_fj1, mask_fj2], axis=1).flatten()\n",
+    "\n",
+    "    # get zero mask\n",
+    "    mask_zero = file[\"INPUTS\"][\"BoostedJets\"][\"MASK\"][:].astype(float).flatten()\n",
+    "\n",
+    "    feature_names = [\n",
+    "        \"fj_pt\",\n",
+    "        \"fj_eta\",\n",
+    "        # \"fj_phi\",\n",
+    "        \"fj_mass\",\n",
+    "        \"fj_sdmass\",\n",
+    "        # \"fj_charge\",\n",
+    "        \"fj_chargedenergyfrac\",\n",
+    "        \"fj_ncharged\",\n",
+    "        # \"fj_neutralenergyfrac\",\n",
+    "        \"fj_nneutral\",\n",
+    "        \"fj_tau21\",\n",
+    "        \"fj_tau32\",\n",
+    "    ]\n",
+    "    arrays = []\n",
+    "    for key in feature_names:\n",
+    "        feature = file[\"INPUTS\"][\"BoostedJets\"][key][:].astype(float).flatten()\n",
+    "        arrays.append(feature)\n",
+    "    data = np.stack(arrays, axis=1)\n",
+    "    labels = mask_signal.astype(bool)\n",
+    "    print(data.shape)\n",
+    "    print(labels.shape)\n",
+    "    return data, labels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "ad91ad5a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(120738, 9)\n",
+      "(120738,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "hh_data, hh_labels = get_test_XY(in_file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "30133d49",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# reconstruct BDT dataset\n",
+    "feature_names = [\n",
+    "        \"fj_pt\",\n",
+    "        \"fj_eta\",\n",
+    "        # \"fj_phi\",\n",
+    "        \"fj_mass\",\n",
+    "        \"fj_sdmass\",\n",
+    "        # \"fj_charge\",\n",
+    "        \"fj_chargedenergyfrac\",\n",
+    "        \"fj_ncharged\",\n",
+    "        # \"fj_neutralenergyfrac\",\n",
+    "        \"fj_nneutral\",\n",
+    "        \"fj_tau21\",\n",
+    "        \"fj_tau32\",\n",
+    "    ]\n",
+    "test = xgb.DMatrix(data=hh_data, label=hh_labels, feature_names=feature_names)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "9133ec8b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load model\n",
+    "param = {}\n",
+    "\n",
+    "param[\"seed\"] = 42  # set seed for reproducibility\n",
+    "\n",
+    "# Booster parameters\n",
+    "param[\"eta\"] = 0.1  # learning rate\n",
+    "param[\"max_depth\"] = 5  # maximum depth of a tree\n",
+    "# param[\"subsample\"] = 0.8  # fraction of events to train tree on\n",
+    "# param[\"colsample_bytree\"] = 0.8  # fraction of features to train tree on\n",
+    "\n",
+    "# Learning task parameters\n",
+    "# param[\"scale_pos_weight\"] = scale_pos_weight\n",
+    "param[\"objective\"] = \"binary:logistic\"  # objective function\n",
+    "param[\n",
+    "    \"eval_metric\"\n",
+    "] = \"error\"  # evaluation metric for cross validation, note: last one is used for early stopping\n",
+    "param = list(param.items())\n",
+    "\n",
+    "num_trees = 150  # number of trees to make\n",
+    "booster = xgb.Booster(param, model_file=\"bdt.json\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "60993088",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(60369, 2)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# predict data\n",
+    "pred_label = (booster.predict(test) > WP).reshape(-1, 2)\n",
+    "print(pred_label.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "118d59dc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load jets and fat jets from test h5 file\n",
+    "jets = load_jets(in_file)\n",
+    "js_idx = ak.local_index(jets)\n",
+    "fjs = load_fjets(in_file)\n",
+    "fj_idx = ak.local_index(fjs)\n",
+    "\n",
+    "# select real fjets based on pT and mass cut\n",
+    "fj_mask = fjs['mask']\n",
+    "fj_cond = pred_label & fj_mask\n",
+    "fjs_selected = fjs[fj_cond]\n",
+    "\n",
+    "# save the qualified fjets indices\n",
+    "# they will be bH candidates\n",
+    "bh_fj_idx = fj_idx[fj_cond]\n",
+    "bh_fj_idx = to_np_array(bh_fj_idx, max_n=2, pad=-1)\n",
+    "\n",
+    "# convert indices to AP and DP\n",
+    "bhs_dp = np.zeros(shape=bh_fj_idx.shape)\n",
+    "fjs_ap = np.zeros(shape=bh_fj_idx.shape)\n",
+    "bhs_dp[bh_fj_idx!=-1] = 1\n",
+    "fjs_ap[bh_fj_idx!=-1] = 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9f12c161",
+   "metadata": {},
+   "source": [
+    "### Select un_padded jets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "e3d30de8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# find ak4jets that matched to selected ak8jets (dR check)\n",
+    "not_padded = jets['mask']\n",
+    "j_cond = not_padded\n",
+    "js_selected = jets[j_cond]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cedbcf9e",
+   "metadata": {},
+   "source": [
+    "### Reconstruct resolved H"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "1bc2f62a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n_higgs = 2\n",
+    "N_JETS = 10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "8eda8e8b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    " MIN_JETS = 2 * n_higgs\n",
+    "# compute possible jet assignments lookup table\n",
+    "JET_ASSIGNMENTS = {}\n",
+    "for nj in range(MIN_JETS, N_JETS + 1):\n",
+    "    a = list(itertools.combinations(range(nj), 2))\n",
+    "    b = np.array([(i, j, k) for i, j, k in itertools.combinations(a, 3) if len(set(i + j + k)) == MIN_JETS])\n",
+    "    JET_ASSIGNMENTS[nj] = b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "64a7368b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nj = 2 * n_higgs\n",
+    "mjj = (jets[:, JET_ASSIGNMENTS[nj][:, :, 0]] + jets[:, JET_ASSIGNMENTS[nj][:, :, 1]]).mass\n",
+    "chi2 = ak.sum(np.square(mjj - HIGGS_MASS), axis=-1)\n",
+    "chi2_argmin = ak.argmin(chi2, axis=-1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "3b38686d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# just consider top 2*N_rH jets\n",
+    "\n",
+    "rH_b1 = JET_ASSIGNMENTS[nj][chi2_argmin][:, :, 0]\n",
+    "rH_b2 = JET_ASSIGNMENTS[nj][chi2_argmin][:, :, 1]\n",
+    "\n",
+    "rH_dp = np.ones(shape=rH_b1.shape)\n",
+    "rH_ap = np.ones(shape=rH_b2.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "1662a585",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# save all assignment to the h5file\n",
+    "# boosted \n",
+    "datasets = {}\n",
+    "datasets[\"TARGETS/bh1/bb\"] = bh_fj_idx[:,0]+10\n",
+    "datasets[\"TARGETS/bh2/bb\"] = bh_fj_idx[:,1]+10\n",
+    "\n",
+    "datasets[\"TARGETS/bh1/detection_probability\"] = bhs_dp[:,0]\n",
+    "datasets[\"TARGETS/bh2/detection_probability\"] = bhs_dp[:,1]\n",
+    "\n",
+    "datasets[\"TARGETS/bh1/assignment_probability\"] = bhs_dp[:,0]\n",
+    "datasets[\"TARGETS/bh2/assignment_probability\"] = bhs_dp[:,1]\n",
+    "\n",
+    "# resolved\n",
+    "for i in range(1, n_higgs+1):\n",
+    "    datasets[f\"TARGETS/h{i}/b1\"] = rH_b1[:, i-1]\n",
+    "    datasets[f\"TARGETS/h{i}/b2\"] = rH_b2[:, i-1]\n",
+    "\n",
+    "    datasets[f\"TARGETS/h{i}/detection_probability\"] = rH_dp[:, i-1]\n",
+    "    datasets[f\"TARGETS/h{i}/assignment_probability\"] = rH_ap[:, i-1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "b6b04fe7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_datasets = {}\n",
+    "for dataset_name, data in datasets.items():\n",
+    "    if dataset_name not in all_datasets:\n",
+    "        all_datasets[dataset_name] = []\n",
+    "    all_datasets[dataset_name].append(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "d7607a1b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with h5py.File(pred_file, \"w\") as output:\n",
+    "    for jet_type_name, jet_type in in_file[\"INPUTS\"].items():\n",
+    "        for feature_name, feature in jet_type.items():\n",
+    "            dataset_name = f\"INPUTS/{jet_type_name}/{feature_name}\"\n",
+    "            data = np.array(feature)\n",
+    "            output.create_dataset(dataset_name, data=data)\n",
+    "    for dataset_name, all_data in all_datasets.items():\n",
+    "        concat_data = np.concatenate(all_data, axis=0)\n",
+    "        output.create_dataset(dataset_name, data=concat_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "f620bdfc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pred_h5 = h5py.File(pred_file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "d91e4dc0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<KeysViewHDF5 ['assignment_probability', 'b1', 'b2', 'detection_probability']>"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pred_h5['TARGETS']['h2'].keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "17aa80d9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<KeysViewHDF5 ['INPUTS', 'TARGETS']>"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pred_h5.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "a880420d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pred_h5['TARGETS']['h2']['b1'][2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "6e9ac5bc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pred_h5['TARGETS']['h1']['b2'][2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "60d2009b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<HDF5 dataset \"detection_probability\": shape (60369,), type \"<f8\">"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pred_h5['TARGETS']['h1']['detection_probability']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "56e3ac4b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(60369, 3)"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rH_dp.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5effbfae",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/mix_baseline_bdt/hhh.ipynb b/notebooks/mix_baseline_bdt/hhh.ipynb
new file mode 100644
index 0000000..0427e22
--- /dev/null
+++ b/notebooks/mix_baseline_bdt/hhh.ipynb
@@ -0,0 +1,725 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "2664c5a5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import itertools\n",
+    "import logging\n",
+    "from pathlib import Path\n",
+    "import numba as nb\n",
+    "\n",
+    "import awkward as ak\n",
+    "import click\n",
+    "import h5py\n",
+    "import numpy as np\n",
+    "import vector\n",
+    "\n",
+    "#from src.data.cms.convert_to_h5 import MIN_JETS, N_JETS, N_FJETS\n",
+    "\n",
+    "vector.register_awkward()\n",
+    "\n",
+    "logging.basicConfig(level=logging.INFO)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "fad54964",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import xgboost as xgb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "06140c21",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# a function that loads jets from hhh_test.h5\n",
+    "def load_jets(in_file):\n",
+    "    # load jets from the h5\n",
+    "    pt = ak.Array(in_file[\"INPUTS\"][\"Jets\"][\"pt\"])\n",
+    "    eta = ak.Array(in_file[\"INPUTS\"][\"Jets\"][\"eta\"])\n",
+    "    phi = ak.Array(in_file[\"INPUTS\"][\"Jets\"][\"phi\"])\n",
+    "    btag = ak.Array(in_file[\"INPUTS\"][\"Jets\"][\"btag\"])\n",
+    "    mass = ak.Array(in_file[\"INPUTS\"][\"Jets\"][\"mass\"])\n",
+    "    mask = ak.Array(in_file[\"INPUTS\"][\"Jets\"][\"MASK\"])\n",
+    "\n",
+    "    jets = ak.zip(\n",
+    "        {\n",
+    "            \"pt\": pt,\n",
+    "            \"eta\": eta,\n",
+    "            \"phi\": phi,\n",
+    "            \"btag\": btag,\n",
+    "            \"mass\": mass,\n",
+    "            \"mask\": mask\n",
+    "        },\n",
+    "        with_name=\"Momentum4D\",\n",
+    "    )\n",
+    "    \n",
+    "    return jets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "a356a240",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# a function that loads fat jets from hhh_test.h5\n",
+    "def load_fjets(in_file):\n",
+    "     # load fatjets from h5\n",
+    "    fj_pt = ak.Array(in_file[\"INPUTS\"][\"BoostedJets\"][\"fj_pt\"])\n",
+    "    fj_eta = ak.Array(in_file[\"INPUTS\"][\"BoostedJets\"][\"fj_eta\"])\n",
+    "    fj_phi = ak.Array(in_file[\"INPUTS\"][\"BoostedJets\"][\"fj_phi\"])\n",
+    "    fj_mass = ak.Array(in_file[\"INPUTS\"][\"BoostedJets\"][\"fj_mass\"])\n",
+    "    fj_mask = ak.Array(in_file[\"INPUTS\"][\"BoostedJets\"][\"MASK\"])\n",
+    "\n",
+    "    fjets = ak.zip(\n",
+    "        {\n",
+    "            \"pt\": fj_pt,\n",
+    "            \"eta\": fj_eta,\n",
+    "            \"phi\": fj_phi,\n",
+    "            'mass': fj_mass,\n",
+    "            'mask': fj_mask\n",
+    "        },\n",
+    "        with_name=\"Momentum4D\"\n",
+    "    )\n",
+    "    \n",
+    "    return fjets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "2ae41356",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@nb.njit\n",
+    "def match_fjet_to_jet(fjets, jets, builder, FJET_DR = 0.8):\n",
+    "    for fjets_event, jets_event in zip(fjets, jets):\n",
+    "        builder.begin_list()\n",
+    "        for i, jet in enumerate(jets_event):\n",
+    "            match_idx = -1\n",
+    "            for j, fjet in enumerate(fjets_event):\n",
+    "                if jet.deltaR(fjet) < FJET_DR:\n",
+    "                    match_idx = j\n",
+    "            builder.append(match_idx)\n",
+    "        builder.end_list()\n",
+    "\n",
+    "    return builder"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "d93ef2b6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def to_np_array(ak_array, axis=-1, max_n=10, pad=0):\n",
+    "    return ak.fill_none(ak.pad_none(ak_array, max_n, clip=True, axis=axis), pad, axis=axis).to_numpy()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "42e8de48",
+   "metadata": {},
+   "source": [
+    "### BDT WP by background misidentification rate\n",
+    "Tight: 0.3%\n",
+    "\n",
+    "Medium: 1%\n",
+    "\n",
+    "Loose: 2%"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "f2ad1605",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "WP_tight = 0.95626426\n",
+    "WP_medium = 0.93498826\n",
+    "WP_loose = 0.911348"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d7cceadc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "WP = WP_loose\n",
+    "pred_file = \"../../predictions/hhh_chi2_loose_bdt_baseline_predictions_west.h5\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6d2502b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_file = \"../../data/delphes/v2/hhh_test.h5\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "ea6b8317",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "in_file = h5py.File(test_file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "401061e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<KeysViewHDF5 ['MASK', 'fj_charge', 'fj_chargedenergyfrac', 'fj_cosphi', 'fj_ehadovereem', 'fj_eta', 'fj_mass', 'fj_ncharged', 'fj_neutralenergyfrac', 'fj_nneutral', 'fj_phi', 'fj_pt', 'fj_sdmass', 'fj_sinphi', 'fj_tau21', 'fj_tau32']>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "in_file[\"INPUTS\"][\"BoostedJets\"].keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "1632da1f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# preliminary\n",
+    "N_JETS = 10\n",
+    "HIGGS_MASS = 125"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "92c92853",
+   "metadata": {},
+   "source": [
+    "### Reconstruct boosted H"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "1820e38a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_test_XY(file):\n",
+    "    bh1 = file[\"TARGETS\"][\"bh1\"][\"bb\"][:]\n",
+    "    bh2 = file[\"TARGETS\"][\"bh2\"][\"bb\"][:]\n",
+    "    bh3 = file[\"TARGETS\"][\"bh3\"][\"bb\"][:]\n",
+    "\n",
+    "    mask_fj1_bh1 = (bh1 == 0).astype(float)\n",
+    "    mask_fj1_bh2 = (bh2 == 0).astype(float)\n",
+    "    mask_fj1_bh3 = (bh3 == 0).astype(float)\n",
+    "    mask_fj1 = mask_fj1_bh1 + mask_fj1_bh2 + mask_fj1_bh3\n",
+    "\n",
+    "    mask_fj2_bh1 = (bh1 == 1).astype(float)\n",
+    "    mask_fj2_bh2 = (bh2 == 1).astype(float)\n",
+    "    mask_fj2_bh3 = (bh3 == 1).astype(float)\n",
+    "    mask_fj2 = mask_fj2_bh1 + mask_fj2_bh2 + mask_fj2_bh3\n",
+    "\n",
+    "    mask_fj3_bh1 = (bh1 == 2).astype(float)\n",
+    "    mask_fj3_bh2 = (bh2 == 2).astype(float)\n",
+    "    mask_fj3_bh3 = (bh3 == 2).astype(float)\n",
+    "    mask_fj3 = mask_fj3_bh1 + mask_fj3_bh2 + mask_fj3_bh3\n",
+    "\n",
+    "    mask_signal = np.stack([mask_fj1, mask_fj2, mask_fj3], axis=1).flatten()\n",
+    "\n",
+    "    # get zero mask\n",
+    "    mask_zero = file[\"INPUTS\"][\"BoostedJets\"][\"MASK\"][:].astype(float).flatten()\n",
+    "\n",
+    "    feature_names = [\n",
+    "        \"fj_pt\",\n",
+    "        \"fj_eta\",\n",
+    "        # \"fj_phi\",\n",
+    "        \"fj_mass\",\n",
+    "        \"fj_sdmass\",\n",
+    "        # \"fj_charge\",\n",
+    "        \"fj_chargedenergyfrac\",\n",
+    "        \"fj_ncharged\",\n",
+    "        # \"fj_neutralenergyfrac\",\n",
+    "        \"fj_nneutral\",\n",
+    "        \"fj_tau21\",\n",
+    "        \"fj_tau32\",\n",
+    "    ]\n",
+    "    arrays = []\n",
+    "    for key in feature_names:\n",
+    "        feature = file[\"INPUTS\"][\"BoostedJets\"][key][:].astype(float).flatten()\n",
+    "        arrays.append(feature)\n",
+    "    data = np.stack(arrays, axis=1)\n",
+    "    labels = mask_signal.astype(bool)\n",
+    "    print(data.shape)\n",
+    "    print(labels.shape)\n",
+    "    return data, labels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "ad91ad5a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(128487, 9)\n",
+      "(128487,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "hhh_data, hhh_labels = get_test_XY(in_file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "30133d49",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# reconstruct BDT dataset\n",
+    "feature_names = [\n",
+    "        \"fj_pt\",\n",
+    "        \"fj_eta\",\n",
+    "        # \"fj_phi\",\n",
+    "        \"fj_mass\",\n",
+    "        \"fj_sdmass\",\n",
+    "        # \"fj_charge\",\n",
+    "        \"fj_chargedenergyfrac\",\n",
+    "        \"fj_ncharged\",\n",
+    "        # \"fj_neutralenergyfrac\",\n",
+    "        \"fj_nneutral\",\n",
+    "        \"fj_tau21\",\n",
+    "        \"fj_tau32\",\n",
+    "    ]\n",
+    "test = xgb.DMatrix(data=hhh_data, label=hhh_labels, feature_names=feature_names)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "9133ec8b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load model\n",
+    "param = {}\n",
+    "\n",
+    "param[\"seed\"] = 42  # set seed for reproducibility\n",
+    "\n",
+    "# Booster parameters\n",
+    "param[\"eta\"] = 0.1  # learning rate\n",
+    "param[\"max_depth\"] = 5  # maximum depth of a tree\n",
+    "# param[\"subsample\"] = 0.8  # fraction of events to train tree on\n",
+    "# param[\"colsample_bytree\"] = 0.8  # fraction of features to train tree on\n",
+    "\n",
+    "# Learning task parameters\n",
+    "# param[\"scale_pos_weight\"] = scale_pos_weight\n",
+    "param[\"objective\"] = \"binary:logistic\"  # objective function\n",
+    "param[\n",
+    "    \"eval_metric\"\n",
+    "] = \"error\"  # evaluation metric for cross validation, note: last one is used for early stopping\n",
+    "param = list(param.items())\n",
+    "\n",
+    "num_trees = 150  # number of trees to make\n",
+    "booster = xgb.Booster(param, model_file=\"bdt.json\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "60993088",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(42829, 3)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# predict data\n",
+    "pred_label = (booster.predict(test) > WP).reshape(-1, 3)\n",
+    "print(pred_label.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "118d59dc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load jets and fat jets from test h5 file\n",
+    "jets = load_jets(in_file)\n",
+    "js_idx = ak.local_index(jets)\n",
+    "fjs = load_fjets(in_file)\n",
+    "fj_idx = ak.local_index(fjs)\n",
+    "\n",
+    "# select real fjets based on pT and mass cut\n",
+    "fj_mask = fjs['mask']\n",
+    "fj_cond = pred_label & fj_mask\n",
+    "fjs_selected = fjs[fj_cond]\n",
+    "\n",
+    "# save the qualified fjets indices\n",
+    "# they will be bH candidates\n",
+    "bh_fj_idx = fj_idx[fj_cond]\n",
+    "bh_fj_idx = to_np_array(bh_fj_idx, max_n=3, pad=-1)\n",
+    "\n",
+    "# convert indices to AP and DP\n",
+    "bhs_dp = np.zeros(shape=bh_fj_idx.shape)\n",
+    "fjs_ap = np.zeros(shape=bh_fj_idx.shape)\n",
+    "bhs_dp[bh_fj_idx!=-1] = 1\n",
+    "fjs_ap[bh_fj_idx!=-1] = 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9f12c161",
+   "metadata": {},
+   "source": [
+    "### Select un_padded jets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "e3d30de8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# find ak4jets that matched to selected ak8jets (dR check)\n",
+    "not_padded = jets['mask']\n",
+    "j_cond = not_padded\n",
+    "js_selected = jets[j_cond]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cedbcf9e",
+   "metadata": {},
+   "source": [
+    "### Reconstruct resolved H"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "1bc2f62a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n_higgs = 3\n",
+    "N_JETS = 10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "8eda8e8b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    " MIN_JETS = 2 * n_higgs\n",
+    "# compute possible jet assignments lookup table\n",
+    "JET_ASSIGNMENTS = {}\n",
+    "for nj in range(MIN_JETS, N_JETS + 1):\n",
+    "    a = list(itertools.combinations(range(nj), 2))\n",
+    "    b = np.array([(i, j, k) for i, j, k in itertools.combinations(a, 3) if len(set(i + j + k)) == MIN_JETS])\n",
+    "    JET_ASSIGNMENTS[nj] = b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "64a7368b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nj = 2 * n_higgs\n",
+    "mjj = (jets[:, JET_ASSIGNMENTS[nj][:, :, 0]] + jets[:, JET_ASSIGNMENTS[nj][:, :, 1]]).mass\n",
+    "chi2 = ak.sum(np.square(mjj - HIGGS_MASS), axis=-1)\n",
+    "chi2_argmin = ak.argmin(chi2, axis=-1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "3b38686d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# just consider top 2*N_rH jets\n",
+    "\n",
+    "rH_b1 = JET_ASSIGNMENTS[nj][chi2_argmin][:, :, 0]\n",
+    "rH_b2 = JET_ASSIGNMENTS[nj][chi2_argmin][:, :, 1]\n",
+    "\n",
+    "rH_dp = np.ones(shape=rH_b1.shape)\n",
+    "rH_ap = np.ones(shape=rH_b2.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "1662a585",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# save all assignment to the h5file\n",
+    "# boosted \n",
+    "datasets = {}\n",
+    "datasets[\"TARGETS/bh1/bb\"] = bh_fj_idx[:,0]+10\n",
+    "datasets[\"TARGETS/bh2/bb\"] = bh_fj_idx[:,1]+10\n",
+    "datasets[\"TARGETS/bh3/bb\"] = bh_fj_idx[:,2]+10\n",
+    "\n",
+    "datasets[\"TARGETS/bh1/detection_probability\"] = bhs_dp[:,0]\n",
+    "datasets[\"TARGETS/bh2/detection_probability\"] = bhs_dp[:,1]\n",
+    "datasets[\"TARGETS/bh3/detection_probability\"] = bhs_dp[:,2]\n",
+    "\n",
+    "datasets[\"TARGETS/bh1/assignment_probability\"] = bhs_dp[:,0]\n",
+    "datasets[\"TARGETS/bh2/assignment_probability\"] = bhs_dp[:,1]\n",
+    "datasets[\"TARGETS/bh3/assignment_probability\"] = bhs_dp[:,2]\n",
+    "\n",
+    "# resolved\n",
+    "for i in range(1, 4):\n",
+    "    datasets[f\"TARGETS/h{i}/b1\"] = rH_b1[:, i-1]\n",
+    "    datasets[f\"TARGETS/h{i}/b2\"] = rH_b2[:, i-1]\n",
+    "\n",
+    "    datasets[f\"TARGETS/h{i}/detection_probability\"] = rH_dp[:, i-1]\n",
+    "    datasets[f\"TARGETS/h{i}/assignment_probability\"] = rH_ap[:, i-1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "b6b04fe7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_datasets = {}\n",
+    "for dataset_name, data in datasets.items():\n",
+    "    if dataset_name not in all_datasets:\n",
+    "        all_datasets[dataset_name] = []\n",
+    "    all_datasets[dataset_name].append(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "d7607a1b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with h5py.File(pred_file, \"w\") as output:\n",
+    "    for jet_type_name, jet_type in in_file[\"INPUTS\"].items():\n",
+    "        for feature_name, feature in jet_type.items():\n",
+    "            dataset_name = f\"INPUTS/{jet_type_name}/{feature_name}\"\n",
+    "            data = np.array(feature)\n",
+    "            output.create_dataset(dataset_name, data=data)\n",
+    "    for dataset_name, all_data in all_datasets.items():\n",
+    "        concat_data = np.concatenate(all_data, axis=0)\n",
+    "        output.create_dataset(dataset_name, data=concat_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "f620bdfc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pred_h5 = h5py.File(pred_file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "d91e4dc0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<KeysViewHDF5 ['assignment_probability', 'b1', 'b2', 'detection_probability']>"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pred_h5['TARGETS']['h3'].keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "17aa80d9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<KeysViewHDF5 ['INPUTS', 'TARGETS']>"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pred_h5.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "a880420d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pred_h5['TARGETS']['h3']['b1'][2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "6e9ac5bc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pred_h5['TARGETS']['h1']['b2'][2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "60d2009b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<HDF5 dataset \"detection_probability\": shape (42829,), type \"<f8\">"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pred_h5['TARGETS']['h1']['detection_probability']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "56e3ac4b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(42829, 3)"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rH_dp.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5effbfae",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/mix_baseline_bdt/qcd.ipynb b/notebooks/mix_baseline_bdt/qcd.ipynb
new file mode 100644
index 0000000..827fa74
--- /dev/null
+++ b/notebooks/mix_baseline_bdt/qcd.ipynb
@@ -0,0 +1,725 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "2664c5a5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import itertools\n",
+    "import logging\n",
+    "from pathlib import Path\n",
+    "import numba as nb\n",
+    "\n",
+    "import awkward as ak\n",
+    "import click\n",
+    "import h5py\n",
+    "import numpy as np\n",
+    "import vector\n",
+    "\n",
+    "#from src.data.cms.convert_to_h5 import MIN_JETS, N_JETS, N_FJETS\n",
+    "\n",
+    "vector.register_awkward()\n",
+    "\n",
+    "logging.basicConfig(level=logging.INFO)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "fad54964",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import xgboost as xgb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "06140c21",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# a function that loads jets from hhh_test.h5\n",
+    "def load_jets(in_file):\n",
+    "    # load jets from the h5\n",
+    "    pt = ak.Array(in_file[\"INPUTS\"][\"Jets\"][\"pt\"])\n",
+    "    eta = ak.Array(in_file[\"INPUTS\"][\"Jets\"][\"eta\"])\n",
+    "    phi = ak.Array(in_file[\"INPUTS\"][\"Jets\"][\"phi\"])\n",
+    "    btag = ak.Array(in_file[\"INPUTS\"][\"Jets\"][\"btag\"])\n",
+    "    mass = ak.Array(in_file[\"INPUTS\"][\"Jets\"][\"mass\"])\n",
+    "    mask = ak.Array(in_file[\"INPUTS\"][\"Jets\"][\"MASK\"])\n",
+    "\n",
+    "    jets = ak.zip(\n",
+    "        {\n",
+    "            \"pt\": pt,\n",
+    "            \"eta\": eta,\n",
+    "            \"phi\": phi,\n",
+    "            \"btag\": btag,\n",
+    "            \"mass\": mass,\n",
+    "            \"mask\": mask\n",
+    "        },\n",
+    "        with_name=\"Momentum4D\",\n",
+    "    )\n",
+    "    \n",
+    "    return jets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "a356a240",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# a function that loads fat jets from hhh_test.h5\n",
+    "def load_fjets(in_file):\n",
+    "     # load fatjets from h5\n",
+    "    fj_pt = ak.Array(in_file[\"INPUTS\"][\"BoostedJets\"][\"fj_pt\"])\n",
+    "    fj_eta = ak.Array(in_file[\"INPUTS\"][\"BoostedJets\"][\"fj_eta\"])\n",
+    "    fj_phi = ak.Array(in_file[\"INPUTS\"][\"BoostedJets\"][\"fj_phi\"])\n",
+    "    fj_mass = ak.Array(in_file[\"INPUTS\"][\"BoostedJets\"][\"fj_mass\"])\n",
+    "    fj_mask = ak.Array(in_file[\"INPUTS\"][\"BoostedJets\"][\"MASK\"])\n",
+    "\n",
+    "    fjets = ak.zip(\n",
+    "        {\n",
+    "            \"pt\": fj_pt,\n",
+    "            \"eta\": fj_eta,\n",
+    "            \"phi\": fj_phi,\n",
+    "            'mass': fj_mass,\n",
+    "            'mask': fj_mask\n",
+    "        },\n",
+    "        with_name=\"Momentum4D\"\n",
+    "    )\n",
+    "    \n",
+    "    return fjets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "2ae41356",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@nb.njit\n",
+    "def match_fjet_to_jet(fjets, jets, builder, FJET_DR = 0.8):\n",
+    "    for fjets_event, jets_event in zip(fjets, jets):\n",
+    "        builder.begin_list()\n",
+    "        for i, jet in enumerate(jets_event):\n",
+    "            match_idx = -1\n",
+    "            for j, fjet in enumerate(fjets_event):\n",
+    "                if jet.deltaR(fjet) < FJET_DR:\n",
+    "                    match_idx = j\n",
+    "            builder.append(match_idx)\n",
+    "        builder.end_list()\n",
+    "\n",
+    "    return builder"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "d93ef2b6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def to_np_array(ak_array, axis=-1, max_n=10, pad=0):\n",
+    "    return ak.fill_none(ak.pad_none(ak_array, max_n, clip=True, axis=axis), pad, axis=axis).to_numpy()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "42e8de48",
+   "metadata": {},
+   "source": [
+    "### BDT WP by background misidentification rate\n",
+    "Tight: 0.3%\n",
+    "\n",
+    "Medium: 1%\n",
+    "\n",
+    "Loose: 2%"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "f2ad1605",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "WP_tight = 0.95626426\n",
+    "WP_medium = 0.93498826\n",
+    "WP_loose = 0.911348"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d7cceadc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "WP = WP_loose\n",
+    "pred_file = \"../../predictions/qcd_chi2_loose_bdt_baseline_predictions_west.h5\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6d2502b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_file = \"../../data/delphes/qcd/qcd_test.h5\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "ea6b8317",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "in_file = h5py.File(test_file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "401061e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<KeysViewHDF5 ['MASK', 'fj_charge', 'fj_chargedenergyfrac', 'fj_cosphi', 'fj_ehadovereem', 'fj_eta', 'fj_mass', 'fj_ncharged', 'fj_neutralenergyfrac', 'fj_nneutral', 'fj_phi', 'fj_pt', 'fj_sdmass', 'fj_sinphi', 'fj_tau21', 'fj_tau32']>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "in_file[\"INPUTS\"][\"BoostedJets\"].keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "1632da1f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# preliminary\n",
+    "N_JETS = 10\n",
+    "HIGGS_MASS = 125"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "92c92853",
+   "metadata": {},
+   "source": [
+    "### Reconstruct boosted H"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "1820e38a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_test_XY(file):\n",
+    "    bh1 = file[\"TARGETS\"][\"bh1\"][\"bb\"][:]\n",
+    "    bh2 = file[\"TARGETS\"][\"bh2\"][\"bb\"][:]\n",
+    "    bh3 = file[\"TARGETS\"][\"bh3\"][\"bb\"][:]\n",
+    "\n",
+    "    mask_fj1_bh1 = (bh1 == 0).astype(float)\n",
+    "    mask_fj1_bh2 = (bh2 == 0).astype(float)\n",
+    "    mask_fj1_bh3 = (bh3 == 0).astype(float)\n",
+    "    mask_fj1 = mask_fj1_bh1 + mask_fj1_bh2 + mask_fj1_bh3\n",
+    "\n",
+    "    mask_fj2_bh1 = (bh1 == 1).astype(float)\n",
+    "    mask_fj2_bh2 = (bh2 == 1).astype(float)\n",
+    "    mask_fj2_bh3 = (bh3 == 1).astype(float)\n",
+    "    mask_fj2 = mask_fj2_bh1 + mask_fj2_bh2 + mask_fj2_bh3\n",
+    "\n",
+    "    mask_fj3_bh1 = (bh1 == 2).astype(float)\n",
+    "    mask_fj3_bh2 = (bh2 == 2).astype(float)\n",
+    "    mask_fj3_bh3 = (bh3 == 2).astype(float)\n",
+    "    mask_fj3 = mask_fj3_bh1 + mask_fj3_bh2 + mask_fj3_bh3\n",
+    "\n",
+    "    mask_signal = np.stack([mask_fj1, mask_fj2, mask_fj3], axis=1).flatten()\n",
+    "\n",
+    "    # get zero mask\n",
+    "    mask_zero = file[\"INPUTS\"][\"BoostedJets\"][\"MASK\"][:].astype(float).flatten()\n",
+    "\n",
+    "    feature_names = [\n",
+    "        \"fj_pt\",\n",
+    "        \"fj_eta\",\n",
+    "        # \"fj_phi\",\n",
+    "        \"fj_mass\",\n",
+    "        \"fj_sdmass\",\n",
+    "        # \"fj_charge\",\n",
+    "        \"fj_chargedenergyfrac\",\n",
+    "        \"fj_ncharged\",\n",
+    "        # \"fj_neutralenergyfrac\",\n",
+    "        \"fj_nneutral\",\n",
+    "        \"fj_tau21\",\n",
+    "        \"fj_tau32\",\n",
+    "    ]\n",
+    "    arrays = []\n",
+    "    for key in feature_names:\n",
+    "        feature = file[\"INPUTS\"][\"BoostedJets\"][key][:].astype(float).flatten()\n",
+    "        arrays.append(feature)\n",
+    "    data = np.stack(arrays, axis=1)\n",
+    "    labels = mask_signal.astype(bool)\n",
+    "    print(data.shape)\n",
+    "    print(labels.shape)\n",
+    "    return data, labels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "ad91ad5a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(76527, 9)\n",
+      "(76527,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "hhh_data, hhh_labels = get_test_XY(in_file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "30133d49",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# reconstruct BDT dataset\n",
+    "feature_names = [\n",
+    "        \"fj_pt\",\n",
+    "        \"fj_eta\",\n",
+    "        # \"fj_phi\",\n",
+    "        \"fj_mass\",\n",
+    "        \"fj_sdmass\",\n",
+    "        # \"fj_charge\",\n",
+    "        \"fj_chargedenergyfrac\",\n",
+    "        \"fj_ncharged\",\n",
+    "        # \"fj_neutralenergyfrac\",\n",
+    "        \"fj_nneutral\",\n",
+    "        \"fj_tau21\",\n",
+    "        \"fj_tau32\",\n",
+    "    ]\n",
+    "test = xgb.DMatrix(data=hhh_data, label=hhh_labels, feature_names=feature_names)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "9133ec8b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load model\n",
+    "param = {}\n",
+    "\n",
+    "param[\"seed\"] = 42  # set seed for reproducibility\n",
+    "\n",
+    "# Booster parameters\n",
+    "param[\"eta\"] = 0.1  # learning rate\n",
+    "param[\"max_depth\"] = 5  # maximum depth of a tree\n",
+    "# param[\"subsample\"] = 0.8  # fraction of events to train tree on\n",
+    "# param[\"colsample_bytree\"] = 0.8  # fraction of features to train tree on\n",
+    "\n",
+    "# Learning task parameters\n",
+    "# param[\"scale_pos_weight\"] = scale_pos_weight\n",
+    "param[\"objective\"] = \"binary:logistic\"  # objective function\n",
+    "param[\n",
+    "    \"eval_metric\"\n",
+    "] = \"error\"  # evaluation metric for cross validation, note: last one is used for early stopping\n",
+    "param = list(param.items())\n",
+    "\n",
+    "num_trees = 150  # number of trees to make\n",
+    "booster = xgb.Booster(param, model_file=\"bdt.json\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "60993088",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(25509, 3)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# predict data\n",
+    "pred_label = (booster.predict(test) > WP).reshape(-1, 3)\n",
+    "print(pred_label.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "118d59dc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load jets and fat jets from test h5 file\n",
+    "jets = load_jets(in_file)\n",
+    "js_idx = ak.local_index(jets)\n",
+    "fjs = load_fjets(in_file)\n",
+    "fj_idx = ak.local_index(fjs)\n",
+    "\n",
+    "# select real fjets based on pT and mass cut\n",
+    "fj_mask = fjs['mask']\n",
+    "fj_cond = pred_label & fj_mask\n",
+    "fjs_selected = fjs[fj_cond]\n",
+    "\n",
+    "# save the qualified fjets indices\n",
+    "# they will be bH candidates\n",
+    "bh_fj_idx = fj_idx[fj_cond]\n",
+    "bh_fj_idx = to_np_array(bh_fj_idx, max_n=3, pad=-1)\n",
+    "\n",
+    "# convert indices to AP and DP\n",
+    "bhs_dp = np.zeros(shape=bh_fj_idx.shape)\n",
+    "fjs_ap = np.zeros(shape=bh_fj_idx.shape)\n",
+    "bhs_dp[bh_fj_idx!=-1] = 1\n",
+    "fjs_ap[bh_fj_idx!=-1] = 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9f12c161",
+   "metadata": {},
+   "source": [
+    "### Select un_padded jets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "e3d30de8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# find ak4jets that matched to selected ak8jets (dR check)\n",
+    "not_padded = jets['mask']\n",
+    "j_cond = not_padded\n",
+    "js_selected = jets[j_cond]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cedbcf9e",
+   "metadata": {},
+   "source": [
+    "### Reconstruct resolved H"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "1bc2f62a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n_higgs = 3\n",
+    "N_JETS = 10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "8eda8e8b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    " MIN_JETS = 2 * n_higgs\n",
+    "# compute possible jet assignments lookup table\n",
+    "JET_ASSIGNMENTS = {}\n",
+    "for nj in range(MIN_JETS, N_JETS + 1):\n",
+    "    a = list(itertools.combinations(range(nj), 2))\n",
+    "    b = np.array([(i, j, k) for i, j, k in itertools.combinations(a, 3) if len(set(i + j + k)) == MIN_JETS])\n",
+    "    JET_ASSIGNMENTS[nj] = b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "64a7368b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nj = 2 * n_higgs\n",
+    "mjj = (jets[:, JET_ASSIGNMENTS[nj][:, :, 0]] + jets[:, JET_ASSIGNMENTS[nj][:, :, 1]]).mass\n",
+    "chi2 = ak.sum(np.square(mjj - HIGGS_MASS), axis=-1)\n",
+    "chi2_argmin = ak.argmin(chi2, axis=-1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "3b38686d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# just consider top 2*N_rH jets\n",
+    "\n",
+    "rH_b1 = JET_ASSIGNMENTS[nj][chi2_argmin][:, :, 0]\n",
+    "rH_b2 = JET_ASSIGNMENTS[nj][chi2_argmin][:, :, 1]\n",
+    "\n",
+    "rH_dp = np.ones(shape=rH_b1.shape)\n",
+    "rH_ap = np.ones(shape=rH_b2.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "1662a585",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# save all assignment to the h5file\n",
+    "# boosted \n",
+    "datasets = {}\n",
+    "datasets[\"TARGETS/bh1/bb\"] = bh_fj_idx[:,0]+10\n",
+    "datasets[\"TARGETS/bh2/bb\"] = bh_fj_idx[:,1]+10\n",
+    "datasets[\"TARGETS/bh3/bb\"] = bh_fj_idx[:,2]+10\n",
+    "\n",
+    "datasets[\"TARGETS/bh1/detection_probability\"] = bhs_dp[:,0]\n",
+    "datasets[\"TARGETS/bh2/detection_probability\"] = bhs_dp[:,1]\n",
+    "datasets[\"TARGETS/bh3/detection_probability\"] = bhs_dp[:,2]\n",
+    "\n",
+    "datasets[\"TARGETS/bh1/assignment_probability\"] = bhs_dp[:,0]\n",
+    "datasets[\"TARGETS/bh2/assignment_probability\"] = bhs_dp[:,1]\n",
+    "datasets[\"TARGETS/bh3/assignment_probability\"] = bhs_dp[:,2]\n",
+    "\n",
+    "# resolved\n",
+    "for i in range(1, 4):\n",
+    "    datasets[f\"TARGETS/h{i}/b1\"] = rH_b1[:, i-1]\n",
+    "    datasets[f\"TARGETS/h{i}/b2\"] = rH_b2[:, i-1]\n",
+    "\n",
+    "    datasets[f\"TARGETS/h{i}/detection_probability\"] = rH_dp[:, i-1]\n",
+    "    datasets[f\"TARGETS/h{i}/assignment_probability\"] = rH_ap[:, i-1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "b6b04fe7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_datasets = {}\n",
+    "for dataset_name, data in datasets.items():\n",
+    "    if dataset_name not in all_datasets:\n",
+    "        all_datasets[dataset_name] = []\n",
+    "    all_datasets[dataset_name].append(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "d7607a1b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with h5py.File(pred_file, \"w\") as output:\n",
+    "    for jet_type_name, jet_type in in_file[\"INPUTS\"].items():\n",
+    "        for feature_name, feature in jet_type.items():\n",
+    "            dataset_name = f\"INPUTS/{jet_type_name}/{feature_name}\"\n",
+    "            data = np.array(feature)\n",
+    "            output.create_dataset(dataset_name, data=data)\n",
+    "    for dataset_name, all_data in all_datasets.items():\n",
+    "        concat_data = np.concatenate(all_data, axis=0)\n",
+    "        output.create_dataset(dataset_name, data=concat_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "f620bdfc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pred_h5 = h5py.File(pred_file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "d91e4dc0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<KeysViewHDF5 ['assignment_probability', 'b1', 'b2', 'detection_probability']>"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pred_h5['TARGETS']['h3'].keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "17aa80d9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<KeysViewHDF5 ['INPUTS', 'TARGETS']>"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pred_h5.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "a880420d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pred_h5['TARGETS']['h3']['b1'][2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "6e9ac5bc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pred_h5['TARGETS']['h1']['b2'][2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "60d2009b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<HDF5 dataset \"detection_probability\": shape (25509,), type \"<f8\">"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pred_h5['TARGETS']['h1']['detection_probability']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "56e3ac4b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(25509, 3)"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rH_dp.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5effbfae",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/src/analysis/boosted.py b/src/analysis/boosted.py
index d899806..ac205e7 100644
--- a/src/analysis/boosted.py
+++ b/src/analysis/boosted.py
@@ -99,25 +99,25 @@ def parse_boosted_w_target(testfile, predfile, num_higgs=3):
 
     for i in range(1, num_higgs + 1):
         # Collect target pt, mask, and assignment for each Higgs
-        bh_pt = np.array(testfile['TARGETS'][f'bh{i}']['pt'])
-        bh_mask = np.array(testfile['TARGETS'][f'bh{i}']['mask'])
-        bb_bh_t.append(np.array(testfile['TARGETS'][f'bh{i}']['bb']))
+        bh_pt = np.array(testfile["TARGETS"][f"bh{i}"]["pt"])
+        bh_mask = np.array(testfile["TARGETS"][f"bh{i}"]["mask"])
+        bb_bh_t.append(np.array(testfile["TARGETS"][f"bh{i}"]["bb"]))
         bh_masks_list.append(bh_mask.reshape(-1, 1))
         bh_pts_list.append(bh_pt.reshape(-1, 1))
 
         try:
             # Collect predicted assignment, detection probability, and fatjet assignment probability
-            bb_bh_p.append(np.array(predfile['TARGETS'][f'bh{i}']['bb']))
-            dp_bh.append(np.array(predfile['TARGETS'][f'bh{i}']['detection_probability']))
-            ap_bh.append(np.array(predfile['TARGETS'][f'bh{i}']['assignment_probability']))
+            bb_bh_p.append(np.array(predfile["TARGETS"][f"bh{i}"]["bb"]))
+            dp_bh.append(np.array(predfile["TARGETS"][f"bh{i}"]["detection_probability"]))
+            ap_bh.append(np.array(predfile["TARGETS"][f"bh{i}"]["assignment_probability"]))
         except:
             # In case of missing prediction, apply fallback logic
-            bb_bh_p.append(np.array(predfile['TARGETS'][f'bh{i}']['bb']) + 10)
-            dp_bh.append(np.array(predfile['TARGETS'][f'bh{i}']['mask']).astype('float'))
-            ap_bh.append(np.array(predfile['TARGETS'][f'bh{i}']['mask']).astype('float'))
+            bb_bh_p.append(np.array(predfile["TARGETS"][f"bh{i}"]["bb"]) + 10)
+            dp_bh.append(np.array(predfile["TARGETS"][f"bh{i}"]["mask"]).astype("float"))
+            ap_bh.append(np.array(predfile["TARGETS"][f"bh{i}"]["mask"]).astype("float"))
 
     # Collect fatjet pt
-    fj_pt = np.array(testfile['INPUTS']['BoostedJets']['fj_pt'])
+    fj_pt = np.array(testfile["INPUTS"]["BoostedJets"]["fj_pt"])
 
     # Concatenate detection and assignment probabilities into arrays
     dps = np.concatenate([dp.reshape(-1, 1) for dp in dp_bh], axis=1)
@@ -145,9 +145,9 @@ def parse_boosted_w_target(testfile, predfile, num_higgs=3):
     LUT_target = gen_target_bH_LUT(bb_ps_selected, bb_ts_selected, targetH_selected_pts)
 
     # Reconstruct bH to remove overlapped ak4 jets
-    fj_eta = np.array(testfile['INPUTS']['BoostedJets']['fj_eta'])
-    fj_phi = np.array(testfile['INPUTS']['BoostedJets']['fj_phi'])
-    fj_mass = np.array(testfile['INPUTS']['BoostedJets']['fj_mass'])
+    fj_eta = np.array(testfile["INPUTS"]["BoostedJets"]["fj_eta"])
+    fj_phi = np.array(testfile["INPUTS"]["BoostedJets"]["fj_phi"])
+    fj_mass = np.array(testfile["INPUTS"]["BoostedJets"]["fj_mass"])
 
     fjs = ak.zip(
         {
@@ -156,10 +156,9 @@ def parse_boosted_w_target(testfile, predfile, num_higgs=3):
             "phi": fj_phi,
             "mass": fj_mass,
         },
-        with_name="Momentum4D"
+        with_name="Momentum4D",
     )
     fj_reco = fjs[bb_ps_selected - 10]
 
     # Return the predicted and target LUTs and the reconstructed jets
     return LUT_pred, LUT_target, fj_reco
-
diff --git a/src/analysis/plot.py b/src/analysis/plot.py
index 435fe5f..a1273d8 100644
--- a/src/analysis/plot.py
+++ b/src/analysis/plot.py
@@ -22,8 +22,12 @@ def calc_pur_eff(target_path, pred_path, bins, num_higgs=3):
 
     # generate look up tables
     LUT_boosted_pred, LUT_boosted_target, fjs_reco = parse_boosted_w_target(target_h5, pred_h5, num_higgs)
-    LUT_resolved_pred, LUT_resolved_target, _ = parse_resolved_w_target(target_h5, pred_h5,  fjs_reco=None,  num_higgs=num_higgs)
-    LUT_resolved_wOR_pred, LUT_resolved_wOR_target, _ = parse_resolved_w_target(target_h5, pred_h5, fjs_reco=fjs_reco, num_higgs=num_higgs)
+    LUT_resolved_pred, LUT_resolved_target, _ = parse_resolved_w_target(
+        target_h5, pred_h5, fjs_reco=None, num_higgs=num_higgs
+    )
+    LUT_resolved_wOR_pred, LUT_resolved_wOR_target, _ = parse_resolved_w_target(
+        target_h5, pred_h5, fjs_reco=fjs_reco, num_higgs=num_higgs
+    )
 
     LUT_resolved_pred_no_OR = []
     for event in LUT_resolved_wOR_pred:
@@ -43,8 +47,12 @@ def calc_pur_eff(target_path, pred_path, bins, num_higgs=3):
 
     # calculate efficiencies and purities for b+r, b, and r
     results = {}
-    results["pur_m"], results["purerr_m"], avg_pur_m, n_correct_pred_m = calc_pur(LUT_boosted_pred, LUT_resolved_wOR_pred, bins)
-    results["eff_m"], results["efferr_m"], avg_eff_m, n_reco_target_m = calc_eff(LUT_boosted_target, LUT_resolved_wOR_target, bins)
+    results["pur_m"], results["purerr_m"], avg_pur_m, n_correct_pred_m = calc_pur(
+        LUT_boosted_pred, LUT_resolved_wOR_pred, bins
+    )
+    results["eff_m"], results["efferr_m"], avg_eff_m, n_reco_target_m = calc_eff(
+        LUT_boosted_target, LUT_resolved_wOR_target, bins
+    )
 
     results["pur_b"], results["purerr_b"], avg_pur_b, n_correct_pred_b = calc_pur(LUT_boosted_pred, None, bins)
     results["eff_b"], results["efferr_b"], avg_eff_b, n_reco_target_b = calc_eff(LUT_boosted_target, None, bins)
@@ -139,7 +147,7 @@ def plot_pur_eff_w_dict(plot_dict, target_path, save_path=None, proj_name=None,
     # plot purities and efficiencies
     for tag, pred_path in plot_dict.items():
         print("Processing", tag)
-        
+
         results = calc_pur_eff(target_path, pred_path, bins, num_higgs)
 
         ax_m[0].errorbar(
@@ -169,29 +177,29 @@ def plot_pur_eff_w_dict(plot_dict, target_path, save_path=None, proj_name=None,
 
     # adjust limits and legends
     event_type = "H" * num_higgs
-    ax_m[0].legend(title=f'{event_type} Boosted+Resolved')
-    ax_m[1].legend(title=f'{event_type} Boosted+Resolved')
+    ax_m[0].legend(title=f"{event_type} Boosted+Resolved")
+    ax_m[1].legend(title=f"{event_type} Boosted+Resolved")
     ax_m[0].set_ylim([-0.1, 1.1])
     ax_m[1].set_ylim([-0.1, 1.1])
-    ax_b[0].legend(title=f'{event_type} Boosted')
-    ax_b[1].legend(title=f'{event_type} Boosted')
+    ax_b[0].legend(title=f"{event_type} Boosted")
+    ax_b[1].legend(title=f"{event_type} Boosted")
     ax_b[0].set_ylim([-0.1, 1.1])
     ax_b[1].set_ylim([-0.1, 1.1])
-    ax_r[0].legend(title=f'{event_type} Resolved')
-    ax_r[1].legend(title=f'{event_type} Resolved')
+    ax_r[0].legend(title=f"{event_type} Resolved")
+    ax_r[1].legend(title=f"{event_type} Resolved")
     ax_r[0].set_ylim([-0.1, 1.1])
     ax_r[1].set_ylim([-0.1, 1.1])
-    ax_r_or[0].legend(title=f'{event_type} Resolved+OR')
-    ax_r_or[1].legend(title=f'{event_type} Resolved+OR')
+    ax_r_or[0].legend(title=f"{event_type} Resolved+OR")
+    ax_r_or[1].legend(title=f"{event_type} Resolved+OR")
     ax_r_or[0].set_ylim([-0.1, 1.1])
     ax_r_or[1].set_ylim([-0.1, 1.1])
 
     plt.show()
 
     if save_path is not None:
-        fig_m.savefig(f"{save_path}/{proj_name}_merged.pdf", format='pdf')
-        fig_b.savefig(f"{save_path}/{proj_name}_boosted.pdf", format='pdf')
-        fig_r.savefig(f"{save_path}/{proj_name}_resolved.pdf", format='pdf')
-        fig_r_or.savefig(f"{save_path}/{proj_name}_resolved_wOR.pdf", format='pdf')
+        fig_m.savefig(f"{save_path}/{proj_name}_merged.pdf", format="pdf")
+        fig_b.savefig(f"{save_path}/{proj_name}_boosted.pdf", format="pdf")
+        fig_r.savefig(f"{save_path}/{proj_name}_resolved.pdf", format="pdf")
+        fig_r_or.savefig(f"{save_path}/{proj_name}_resolved_wOR.pdf", format="pdf")
 
     return
diff --git a/src/analysis/resolved.py b/src/analysis/resolved.py
index c844acc..f49145f 100644
--- a/src/analysis/resolved.py
+++ b/src/analysis/resolved.py
@@ -133,21 +133,22 @@ def gen_target_h_LUT(b1_ps_passed, b2_ps_passed, b1_ts_selected, b2_ts_selected,
         builder.end_list()
     return builder
 
+
 def parse_resolved_w_target(testfile, predfile, num_higgs=3, fjs_reco=None):
     # Lists to store h_pt, h_masks, and bh_masks for each Higgs
     h_pts_list = []
     h_masks_list = []
     bh_masks_list = []
-    
+
     for i in range(1, num_higgs + 1):
         # Collect pt and mask for resolved Higgs
-        h_pt = np.array(testfile['TARGETS'][f'h{i}']['pt'])
-        h_mask = np.array(testfile['TARGETS'][f'h{i}']['mask'])
+        h_pt = np.array(testfile["TARGETS"][f"h{i}"]["pt"])
+        h_mask = np.array(testfile["TARGETS"][f"h{i}"]["mask"])
         h_pts_list.append(h_pt.reshape(-1, 1))
         h_masks_list.append(h_mask.reshape(-1, 1))
-        
+
         # Collect boosted mask for each Higgs
-        bh_mask = np.array(testfile['TARGETS'][f'bh{i}']['mask'])
+        bh_mask = np.array(testfile["TARGETS"][f"bh{i}"]["mask"])
         bh_masks_list.append(bh_mask.reshape(-1, 1))
 
     # Combine masks and pt arrays for resolved and boosted Higgs
@@ -165,30 +166,30 @@ def parse_resolved_w_target(testfile, predfile, num_higgs=3, fjs_reco=None):
 
     for i in range(1, num_higgs + 1):
         # Collect target assignments for b1 and b2
-        b1_h_t = np.array(testfile['TARGETS'][f'h{i}']['b1']).astype('int')
-        b2_h_t = np.array(testfile['TARGETS'][f'h{i}']['b2']).astype('int')
+        b1_h_t = np.array(testfile["TARGETS"][f"h{i}"]["b1"]).astype("int")
+        b2_h_t = np.array(testfile["TARGETS"][f"h{i}"]["b2"]).astype("int")
         b1_ts_list.append(b1_h_t.reshape(-1, 1))
         b2_ts_list.append(b2_h_t.reshape(-1, 1))
 
         # Collect predicted assignments for b1 and b2
-        b1_h_p = np.array(predfile['TARGETS'][f'h{i}']['b1']).astype('int')
-        b2_h_p = np.array(predfile['TARGETS'][f'h{i}']['b2']).astype('int')
+        b1_h_p = np.array(predfile["TARGETS"][f"h{i}"]["b1"]).astype("int")
+        b2_h_p = np.array(predfile["TARGETS"][f"h{i}"]["b2"]).astype("int")
         b1_ps_list.append(b1_h_p.reshape(-1, 1))
         b2_ps_list.append(b2_h_p.reshape(-1, 1))
 
     # Lists for detection and assignment probabilities
     dp_list, ap_list = [], []
     for i in range(1, num_higgs + 1):
-        dp_h = np.array(predfile['TARGETS'][f'h{i}']['detection_probability'])
-        ap_h = np.array(predfile['TARGETS'][f'h{i}']['assignment_probability'])
+        dp_h = np.array(predfile["TARGETS"][f"h{i}"]["detection_probability"])
+        ap_h = np.array(predfile["TARGETS"][f"h{i}"]["assignment_probability"])
         dp_list.append(dp_h.reshape(-1, 1))
         ap_list.append(ap_h.reshape(-1, 1))
 
     # Reconstruct jet 4-momentum objects
-    j_pt = np.array(testfile['INPUTS']['Jets']['pt'])
-    j_eta = np.array(testfile['INPUTS']['Jets']['eta'])
-    j_phi = np.array(testfile['INPUTS']['Jets']['phi'])
-    j_mass = np.array(testfile['INPUTS']['Jets']['mass'])
+    j_pt = np.array(testfile["INPUTS"]["Jets"]["pt"])
+    j_eta = np.array(testfile["INPUTS"]["Jets"]["eta"])
+    j_phi = np.array(testfile["INPUTS"]["Jets"]["phi"])
+    j_mass = np.array(testfile["INPUTS"]["Jets"]["mass"])
     js = ak.zip(
         {
             "pt": j_pt,
@@ -196,10 +197,10 @@ def parse_resolved_w_target(testfile, predfile, num_higgs=3, fjs_reco=None):
             "phi": j_phi,
             "mass": j_mass,
         },
-        with_name="Momentum4D"
+        with_name="Momentum4D",
     )
     if np.max(js.layout.minmax_depth) == 1:
-        js = [js] 
+        js = [js]
 
     # Concatenate detection and assignment probabilities
     dps = np.concatenate(dp_list, axis=1)
@@ -234,10 +235,15 @@ def parse_resolved_w_target(testfile, predfile, num_higgs=3, fjs_reco=None):
     LUT_pred = gen_pred_h_LUT(
         b1_ps_selected, b2_ps_selected, b1_ts_selected, b2_ts_selected, js, goodJetIdx, bi_cat_H_selected, ak.ArrayBuilder()
     ).snapshot()
-    
+
     LUT_target = gen_target_h_LUT(
-        b1_ps_selected, b2_ps_selected, b1_ts_selected, b2_ts_selected, targetH_selected_pts, bi_cat_H_selected, ak.ArrayBuilder()
+        b1_ps_selected,
+        b2_ps_selected,
+        b1_ts_selected,
+        b2_ts_selected,
+        targetH_selected_pts,
+        bi_cat_H_selected,
+        ak.ArrayBuilder(),
     ).snapshot()
 
     return LUT_pred, LUT_target, goodJetIdx
-
diff --git a/src/analysis/utils.py b/src/analysis/utils.py
index c6cdd4b..bd23a50 100644
--- a/src/analysis/utils.py
+++ b/src/analysis/utils.py
@@ -90,9 +90,9 @@ def calc_pur(LUT_boosted_pred, LUT_resolved_pred, bins):
         - mean_per_bin
     )
 
-    num_correct_pred = np.sum(predHs[:,0])
-    
-    mean_pur = num_correct_pred/predHs.shape[0]
+    num_correct_pred = np.sum(predHs[:, 0])
+
+    mean_pur = num_correct_pred / predHs.shape[0]
 
     return mean_per_bin, err_per_bin, mean_pur, num_correct_pred
 
@@ -134,12 +134,13 @@ def calc_eff(LUT_boosted_target, LUT_resolved_target, bins):
         - mean_per_bin
     )
 
-    num_reco_target = np.sum(targetHs[:,0])
-    
-    mean_eff = num_reco_target/targetHs.shape[0]
-    
+    num_reco_target = np.sum(targetHs[:, 0])
+
+    mean_eff = num_reco_target / targetHs.shape[0]
+
     return mean_per_bin, err_per_bin, mean_eff, num_reco_target
 
+
 # calculate event purity
 def calc_event_purity(LUT_boosted_pred, LUT_resolved_pred, bins):
     N_OR = 0
@@ -178,22 +179,23 @@ def calc_event_purity(LUT_boosted_pred, LUT_resolved_pred, bins):
     N_correct_event = ak.sum(correct_event)
 
     metrics = {}
-    metrics['avg_event_purity'] = N_correct_event / N_event
+    metrics["avg_event_purity"] = N_correct_event / N_event
 
     # for each number of predicted candidates
     # calculate purity
     N_pred = ak.num(pred_events, axis=1)
     N_max_pred = ak.max(N_pred)
-    for i in range(0, N_max_pred+1):
+    for i in range(0, N_max_pred + 1):
         event_sel = N_pred == i
         N_sel_event = ak.sum(event_sel)
         N_correct_sel_event = ak.sum(correct_event[event_sel])
 
-        metrics[f'{i}_candidate_event_purity'] = N_correct_sel_event / N_sel_event
-        metrics[f'{i}_candidate_event_ratio'] = N_sel_event / N_event
+        metrics[f"{i}_candidate_event_purity"] = N_correct_sel_event / N_sel_event
+        metrics[f"{i}_candidate_event_ratio"] = N_sel_event / N_event
 
     return metrics
 
+
 # calculate event efficiency
 # calculate purity
 def calc_event_efficiency(LUT_boosted_target, LUT_resolved_target, bins):
@@ -223,7 +225,6 @@ def calc_event_efficiency(LUT_boosted_target, LUT_resolved_target, bins):
         # resolved case
         target_events = [[targetH[0] for targetH in event] for event in LUT_resolved_target]
 
-
     target_events = ak.Array(target_events)
 
     # calculate average purity
@@ -233,18 +234,18 @@ def calc_event_efficiency(LUT_boosted_target, LUT_resolved_target, bins):
     N_retrieved_event = ak.sum(retrieved_event)
 
     metrics = {}
-    metrics['avg_event_efficiency'] = N_retrieved_event / N_event
+    metrics["avg_event_efficiency"] = N_retrieved_event / N_event
 
     # for each number of targets
     # calculate purity
     N_target = ak.num(target_events, axis=1)
     N_max_target = ak.max(N_target)
-    for i in range(0, N_max_target+1):
+    for i in range(0, N_max_target + 1):
         event_sel = N_target == i
         N_sel_event = ak.sum(event_sel)
         N_retrieved_sel_event = ak.sum(retrieved_event[event_sel])
 
-        metrics[f'{i}_target_event_purity'] = N_retrieved_sel_event / N_sel_event
-        metrics[f'{i}_target_event_ratio'] = N_sel_event / N_event
+        metrics[f"{i}_target_event_purity"] = N_retrieved_sel_event / N_sel_event
+        metrics[f"{i}_target_event_ratio"] = N_sel_event / N_event
 
     return metrics
diff --git a/src/models/test_baseline.py b/src/models/test_baseline.py
index 1cca5e8..8c840cb 100644
--- a/src/models/test_baseline.py
+++ b/src/models/test_baseline.py
@@ -70,7 +70,7 @@ def main(test_file, event_file, n_higgs, method):
             "eta": eta,
             "phi": phi,
             # "mass": ak.zeros_like(pt),
-            'mass': mass,
+            "mass": mass,
             "btag": btag,
         },
         with_name="Momentum4D",
@@ -171,7 +171,7 @@ def main(test_file, event_file, n_higgs, method):
 
     num_vectors = np.sum(mask, axis=-1).to_numpy()
     lines = 2
-    print('hello', masks.shape)
+    print("hello", masks.shape)
     results, jet_limits, clusters = evaluate_predictions(predictions, num_vectors, targets, masks, event_file, lines)
     display_table(results, jet_limits, clusters)