diff --git a/environment.yml b/environment.yml
index 744657e..20a1c8d 100644
--- a/environment.yml
+++ b/environment.yml
@@ -4,7 +4,7 @@ channels:
   - default
 dependencies:
   - python=3.11
-  - gammapy
+  - gammapy=1.2
   - numpy
   - scipy
   - matplotlib
diff --git a/examples/Analysis.ipynb b/examples/Analysis.ipynb
index 230bb2d..cbc4cbf 100644
--- a/examples/Analysis.ipynb
+++ b/examples/Analysis.ipynb
@@ -2,10 +2,19 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 94,
    "id": "f36a7a0b-b518-46dd-8a13-8785dd37cf57",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n"
+     ]
+    }
+   ],
    "source": [
     "%load_ext autoreload\n",
     "%autoreload 2"
@@ -13,13 +22,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 95,
    "id": "c6ad9055-f2c0-48df-8f2c-a70834eed764",
    "metadata": {},
    "outputs": [],
    "source": [
     "from gammapy.datasets import MapDataset                                                                                                                                     \n",
-    "from gammapy.irf import load_cta_irfs, load_irf_dict_from_file                                                                                                                      \n",
+    "from gammapy.irf import load_irf_dict_from_file                                                                                                                      \n",
     "from gammapy.maps import MapAxis, WcsGeom, WcsNDMap                                                                                                                         \n",
     "from gammapy.modeling.models import (                                                                                                                                       \n",
     "    FoVBackgroundModel,                                                                                                                                                   \n",
@@ -59,7 +68,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 101,
    "id": "d5881554-9c21-4f8b-a9a5-0ea47e671f4a",
    "metadata": {},
    "outputs": [],
@@ -115,7 +124,7 @@
     "    geom = WcsGeom.create(\n",
     "        skydir=observation().pointing.fixed_icrs,\n",
     "        width=(4, 4),\n",
-    "        binsz=0.04,\n",
+    "        binsz=4 / 150,\n",
     "        frame=\"galactic\",\n",
     "    )\n",
     "\n",
@@ -143,10 +152,13 @@
     "        distance=ursa_major_ii_profile().DISTANCE_GC,\n",
     "    )\n",
     "    jfactor = jfactory.compute_differential_jfactor()\n",
+    "    jfact_map2 = WcsNDMap.read(\"../../phdthesis/build/magic2/dl5/data/jfactor_map.fits.gz\")\n",
+    "    jfact_map = WcsNDMap(geom=geometry2d(), data=jfact_map2.data, unit=jfactor.unit)\n",
     "    jfact_map = WcsNDMap(geom=geometry2d(), data=jfactor.value, unit=jfactor.unit)\n",
+    "\n",
     "    spatial_model = TemplateSpatialModel(jfact_map, normalize=False)\n",
     "\n",
-    "    spectral_model = DarkMatterAnnihilationSpectralModel(mass=50 * u.TeV, channel=\"b\")\n",
+    "    spectral_model = DarkMatterAnnihilationSpectralModel(mass=100 * u.TeV, channel=\"mu\")\n",
     "\n",
     "    model_simu = SkyModel(\n",
     "        spatial_model=spatial_model,\n",
@@ -187,7 +199,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 102,
    "id": "fdb09081-c75d-4f4b-ba5b-01199a6151fc",
    "metadata": {},
    "outputs": [
@@ -211,6 +223,10 @@
       "WARNING: UnitsWarning: '1/s/MeV/sr' did not parse as fits unit: Numeric factor not supported by FITS If this is meant to be a custom unit, define it with 'u.def_unit'. To have it recognized inside a file reader or other code, enable it with 'u.add_enabled_units'. For details, see https://docs.astropy.org/en/latest/units/combining_and_defining.html [astropy.units.core]\n",
       "Invalid unit found in background table! Assuming (s-1 MeV-1 sr-1)\n",
       "/Users/stefan/mambaforge/envs/titrate-dev/lib/python3.11/site-packages/gammapy/data/observations.py:226: GammapyDeprecationWarning: Pointing will be required to be provided as FixedPointingInfo\n",
+      "  warnings.warn(\n",
+      "WARNING: UnitsWarning: '1/s/MeV/sr' did not parse as fits unit: Numeric factor not supported by FITS If this is meant to be a custom unit, define it with 'u.def_unit'. To have it recognized inside a file reader or other code, enable it with 'u.add_enabled_units'. For details, see https://docs.astropy.org/en/latest/units/combining_and_defining.html [astropy.units.core]\n",
+      "Invalid unit found in background table! Assuming (s-1 MeV-1 sr-1)\n",
+      "/Users/stefan/mambaforge/envs/titrate-dev/lib/python3.11/site-packages/gammapy/data/observations.py:226: GammapyDeprecationWarning: Pointing will be required to be provided as FixedPointingInfo\n",
       "  warnings.warn(\n"
      ]
     }
@@ -221,7 +237,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 103,
    "id": "f47df37a-1d7f-46a9-a439-3599929e1e9c",
    "metadata": {},
    "outputs": [
@@ -258,7 +274,7 @@
       "  Component 0: SkyModel\n",
       "  \n",
       "    Name                      : measurement-darkmatter\n",
-      "    Datasets names            : None\n",
+      "    Datasets names            : ['measurement']\n",
       "    Spectral model type       : DarkMatterAnnihilationSpectralModel\n",
       "    Spatial  model type       : TemplateSpatialModel\n",
       "    Temporal model type       : \n",
@@ -293,7 +309,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 104,
    "id": "0385f66c-26aa-4d04-b5c8-0305903c6e22",
    "metadata": {},
    "outputs": [],
@@ -311,7 +327,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 105,
    "id": "a0d66aaf-be8a-4f9f-81b8-77461b7cc43c",
    "metadata": {},
    "outputs": [],
@@ -321,19 +337,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 106,
    "id": "105b9478-5da4-4d3b-b7ac-85558c19b5c0",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'pvalue_diff': 0.9464168591899156,\n",
-       " 'pvalue_same': 0.8073040034889607,\n",
+       "{'pvalue_diff': 0.9926684858102534,\n",
+       " 'pvalue_same': 0.43798744517687327,\n",
        " 'valid': True}"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 106,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -344,39 +360,39 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 107,
    "id": "9619d2b5-34df-4bd8-b83c-fd9c52c873c9",
    "metadata": {},
    "outputs": [],
    "source": [
-    "validator.write('/Users/stefan/Downloads/results.h5', overwrite=True)"
+    "validator.write('/Users/stefan/Downloads/results_1.2.h5', overwrite=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 108,
    "id": "8385fb4b-9573-41b4-a073-4daa9da7aec6",
    "metadata": {},
    "outputs": [],
    "source": [
-    "validator_h5 = AsymptoticValidator(meas_dataset, path='/Users/stefan/Downloads/results.h5', channel='b', mass=50*u.TeV)"
+    "validator_h5 = AsymptoticValidator(meas_dataset, path='/Users/stefan/Downloads/results_1.2.h5', channel='mu', mass=100*u.TeV)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 109,
    "id": "ed93c895-952d-47f0-9272-78e443715d3b",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'pvalue_diff': 0.9464168591899156,\n",
-       " 'pvalue_same': 0.8073040034889607,\n",
+       "{'pvalue_diff': 0.9926684858102534,\n",
+       " 'pvalue_same': 0.43798744517687327,\n",
        " 'valid': True}"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 109,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -387,7 +403,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 111,
    "id": "01b77368-82c6-4757-9504-5c5994a4a854",
    "metadata": {},
    "outputs": [
@@ -395,15 +411,15 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/stefan/Documents/projects/TITRATE/titrate/statistics.py:131: RuntimeWarning: divide by zero encountered in divide\n",
+      "/Users/stefan/Documents/projects/TITRATE/titrate/statistics.py:129: RuntimeWarning: divide by zero encountered in divide\n",
       "  1\n",
-      "/Users/stefan/Documents/projects/TITRATE/titrate/statistics.py:120: RuntimeWarning: divide by zero encountered in divide\n",
+      "/Users/stefan/Documents/projects/TITRATE/titrate/statistics.py:118: RuntimeWarning: divide by zero encountered in divide\n",
       "  1\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -416,8 +432,78 @@
     "from titrate.plotting import ValidationPlotter\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
-    "ValidationPlotter(meas_dataset, '/Users/stefan/Downloads/results.h5', statistic='qmu', channel='b', mass=50*u.TeV)\n",
-    "plt.savefig('/Users/stefan/Downloads/qmu_validation.pdf')"
+    "ValidationPlotter(meas_dataset, '/Users/stefan/Downloads/results_1.2.h5', statistic='qmu', channel='mu', mass=100*u.TeV)\n",
+    "plt.savefig('/Users/stefan/Downloads/qmu_validation_1.2.pdf')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "d3ade897-23ec-4888-848c-2945cb55a34e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from matplotlib.colors import LogNorm\n",
+    "meas_dataset.models[0].spatial_model.plot(add_cbar=True, norm=LogNorm())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ac4544c2-765c-4df0-ad84-5c6bec9d07d2",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "18c31b60-fda0-4b47-add7-dc332234f38c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "00ebc760-bfb1-4300-930a-7349c945f67a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<WCSAxes: >"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "jfact_map2 = WcsNDMap.read(\"../../phdthesis/build/magic2/dl5/data/jfactor_map.fits.gz\")\n",
+    "jfact_map2.plot(add_cbar=True, norm=LogNorm())"
    ]
   },
   {
@@ -655,18 +741,68 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 87,
    "id": "fe88ffca-1e96-44d6-957c-eda441e4fc8d",
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "m = DarkMatterAnnihilationSpectralModel(channel='mu', mass='100 TeV')\n",
+    "t = DarkMatterAnnihilationSpectralModel(channel='tau', mass='100 TeV')\n",
+    "b = DarkMatterAnnihilationSpectralModel(channel='b', mass='100 TeV')\n",
+    "w = DarkMatterAnnihilationSpectralModel(channel='W', mass='100 TeV')"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 88,
    "id": "96ea6ea3-8db3-40e6-8d33-9c09a9640d4c",
    "metadata": {},
    "outputs": [],
+   "source": [
+    "s = np.geomspace(0.05, 5) * u.TeV"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "id": "68084a9c-0529-4929-b361-b1a4026419d4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[]"
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(s,m.evaluate(s, 1))\n",
+    "plt.plot(s,t.evaluate(s, 1))\n",
+    "plt.plot(s,b.evaluate(s, 1))\n",
+    "plt.plot(s,w.evaluate(s, 1))\n",
+    "plt.loglog()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c5962927-9108-4a14-bf7a-c31c624e0e4f",
+   "metadata": {},
+   "outputs": [],
    "source": []
   }
  ],
diff --git a/titrate/datasets.py b/titrate/datasets.py
index c76462c..b76dc78 100644
--- a/titrate/datasets.py
+++ b/titrate/datasets.py
@@ -1,5 +1,5 @@
 import numpy as np
-from gammapy.datasets import MapDataset
+from gammapy.datasets import MapDataset, SpectrumDataset
 
 from titrate.utils import copy_models_to_dataset
 
@@ -18,7 +18,7 @@ def fake(self):
         npred = self.npred()
         data = np.nan_to_num(npred.data, copy=True, nan=0.0, posinf=0.0, neginf=0.0)
         npred.data = data
-        self.counts = npred
+        self.counts.data = npred.data.copy()
 
     @classmethod
     def from_MapDataset(self, dataset):
@@ -31,10 +31,44 @@ def from_MapDataset(self, dataset):
             deleted_entries[key] = dataset_dict.pop(key)
 
         asimov_dataset = AsimovMapDataset(**dataset_dict)
-        # copy IRFs separately
-        asimov_dataset._background_parameters_cached = deleted_entries[
-            "_background_parameters_cached"
-        ]
+        for key in deleted_entries.keys():
+            if key == "_name":
+                continue
+            setattr(asimov_dataset, key, deleted_entries[key])
+
+        copy_models_to_dataset(dataset.models, asimov_dataset)
+        asimov_dataset.fake()
+
+        return asimov_dataset
+
+
+class AsimovSpectralDataset(SpectrumDataset):
+    """
+    AsimovMapDataset is a subclass of a gammapy SpectrumDataset
+    and provides asimov-like fake method.
+    """
+
+    def fake(self):
+        """
+        This method generates Asimov like counts,
+        i.e. the counts are not drawn from a poisson distribution.
+        """
+        npred = self.npred()
+        data = np.nan_to_num(npred.data, copy=True, nan=0.0, posinf=0.0, neginf=0.0)
+        npred.data = data
+        self.counts.data = npred.data.copy()
+
+    @classmethod
+    def from_SpectralDataset(self, dataset):
+        dataset_dict = dataset.__dict__.copy()
+
+        delete_keys = [key for key in dataset_dict.keys() if key.startswith("_")]
+
+        deleted_entries = {}
+        for key in delete_keys:
+            deleted_entries[key] = dataset_dict.pop(key)
+
+        asimov_dataset = AsimovSpectralDataset(**dataset_dict)
 
         copy_models_to_dataset(dataset.models, asimov_dataset)
         asimov_dataset.fake()
diff --git a/titrate/plotting.py b/titrate/plotting.py
index 2f17115..b53f62a 100644
--- a/titrate/plotting.py
+++ b/titrate/plotting.py
@@ -3,10 +3,11 @@
 import numpy as np
 from astropy import visualization as viz
 from astropy.table import QTable, unique
-from astropy.units import Quantity
+from gammapy.modeling import Fit
 
-from titrate.datasets import AsimovMapDataset
+from titrate.datasets import AsimovMapDataset, AsimovSpectralDataset
 from titrate.statistics import QMuTestStatistic, QTildeMuTestStatistic
+from titrate.utils import copy_dataset_with_models
 
 STATISTICS = {"qmu": QMuTestStatistic, "qtildemu": QTildeMuTestStatistic}
 
@@ -96,32 +97,30 @@ def plot_channel(
         two_sigma_plus,
     ):
         if uls is not None:
-            self.ax.plot(masses, uls, color="tab:orange", label="Upper Limits")
+            self.ax.plot(masses, uls, color="C1", label="Upper Limits")
         if median is not None:
-            self.ax.plot(
-                masses, median, color="tab:blue", label="Expected Upper Limits"
-            )
+            self.ax.plot(masses, median, color="C0", label="Expected Upper Limits")
             self.ax.fill_between(
                 masses,
                 median,
                 one_sigma_plus,
-                color="tab:blue",
+                color="C0",
                 alpha=0.75,
                 label=r"$1\sigma$-region",
             )
             self.ax.fill_between(
-                masses, median, one_sigma_minus, color="tab:blue", alpha=0.75
+                masses, median, one_sigma_minus, color="C0", alpha=0.75
             )
             self.ax.fill_between(
                 masses,
                 one_sigma_plus,
                 two_sigma_plus,
-                color="tab:blue",
+                color="C0",
                 alpha=0.5,
                 label=r"$2\sigma$-region",
             )
             self.ax.fill_between(
-                masses, one_sigma_minus, two_sigma_minus, color="tab:blue", alpha=0.5
+                masses, one_sigma_minus, two_sigma_minus, color="C0", alpha=0.5
             )
 
 
@@ -135,34 +134,47 @@ def __init__(
         statistic="qmu",
         poi_name="scale",
         ax=None,
+        analysis="3d",
+        poi_val=1e5,
     ):
         self.path = path
         self.ax = ax if ax is not None else plt.gca()
+        self.analysis = analysis
+        self.measurement_dataset = measurement_dataset
+        self.poi_name = poi_name
+        self.poi_val = poi_val
 
-        asimov_dataset = AsimovMapDataset.from_MapDataset(measurement_dataset)
+        self.d_sig = copy_dataset_with_models(self.measurement_dataset)
+        self.d_sig.models.parameters[self.poi_name].value = self.poi_val
+        self.d_sig.models.parameters[self.poi_name].frozen = True
+        fit = Fit()
+        _ = fit.run(self.d_sig)
+        self.d_sig.models.parameters[self.poi_name].frozen = False
 
-        try:
-            table_diff = QTable.read(
-                self.path, path=f"validation/{statistic}/diff/{channel}/{mass}"
+        self.d_bkg = copy_dataset_with_models(self.measurement_dataset)
+        self.d_bkg.models.parameters[self.poi_name].value = 0
+        self.d_bkg.models.parameters[self.poi_name].frozen = True
+        fit = Fit()
+        _ = fit.run(self.d_bkg)
+        self.d_bkg.models.parameters[self.poi_name].frozen = False
+
+        if self.analysis == "3d":
+            self.asimov_sig_dataset = AsimovMapDataset.from_MapDataset(self.d_sig)
+            self.asimov_bkg_dataset = AsimovMapDataset.from_MapDataset(self.d_bkg)
+        elif self.analysis == "1d":
+            self.asimov_sig_dataset = AsimovSpectralDataset.from_SpectralDataset(
+                self.d_sig
             )
-            table_same = QTable.read(
-                self.path, path=f"validation/{statistic}/same/{channel}/{mass}"
+            self.asimov_bkg_dataset = AsimovSpectralDataset.from_SpectralDataset(
+                self.d_bkg
             )
-        except OSError:
-            if channel is None:
-                channels = list(
-                    h5py.File(self.path)["validation"][statistic]["diff"].keys()
-                )
-                channels = [ch for ch in channels if "meta" not in ch]
-                raise ValueError(f"Channel must be one of {channels}")
-            if mass is None:
-                masses = list(
-                    h5py.File(self.path)["validation"][statistic]["diff"][
-                        channel
-                    ].keys()
-                )
-                masses = [Quantity(m) for m in masses if "meta" not in m]
-                raise ValueError(f"Mass must be one of {masses}")
+
+        table_diff = QTable.read(
+            self.path, path=f"validation/{statistic}/diff/{channel}/{mass}"
+        )
+        table_same = QTable.read(
+            self.path, path=f"validation/{statistic}/same/{channel}/{mass}"
+        )
 
         toys_ts_diff = table_diff["ts"]
         toys_ts_diff_valid = table_diff["valid"]
@@ -173,63 +185,98 @@ def __init__(
         toys_ts_diff = toys_ts_diff[toys_ts_diff_valid]
         toys_ts_same = toys_ts_same[toys_ts_same_valid]
 
-        max_ts = max(toys_ts_diff.max(), toys_ts_same.max())
-        bins = np.linspace(0, max_ts, 31)
-        linspace = np.linspace(0, max_ts, 1000)
-        statistic = STATISTICS[statistic](asimov_dataset, poi_name)
+        bins_same = np.linspace(0, toys_ts_same.max(), 31)
+        bins_diff = np.linspace(0, toys_ts_diff.max(), 31)
+        linspace_same = np.linspace(1e-3, bins_same[-1], 1000)
+        linspace_diff = np.linspace(1e-3, bins_diff[-1], 1000)
+        statistic_sig = STATISTICS[statistic](self.asimov_sig_dataset, poi_name)
+        statistic_bkg = STATISTICS[statistic](self.asimov_bkg_dataset, poi_name)
         statistic_math_name = (
             r"q_\mu" if isinstance(statistic, QMuTestStatistic) else r"\tilde{q}_\mu"
         )
 
+        fig, axs = plt.subplot_mosaic([["same"], ["diff"]])
+
         self.plot(
-            linspace, bins, toys_ts_same, toys_ts_diff, statistic, statistic_math_name
+            linspace_same,
+            linspace_diff,
+            bins_same,
+            bins_diff,
+            toys_ts_same,
+            toys_ts_diff,
+            statistic_sig,
+            statistic_bkg,
+            axs,
         )
 
-        self.ax.set_yscale("log")
-        self.ax.set_xlim(0, max_ts)
+        for ax in axs:
+            axs[ax].set_yscale("log")
+            axs[ax].set_xlim(
+                0,
+            )
+            axs[ax].set_ylim(1e-4, 1e2)
 
-        self.ax.set_ylabel("pdf")
-        self.ax.set_xlabel(rf"${statistic_math_name}$")
-        self.ax.set_title(statistic.__class__.__name__)
-        self.ax.legend()
+            if ax == "same":
+                axs[ax].set_ylabel(
+                    r"$f(\tilde{q}_\mu\vert\mu^\prime,\theta_{\mu,\text{obs}})$ \\"
+                    r"$\mu=10^5$, $\mu^\prime=10^5$"
+                )
+            else:
+                axs[ax].set_ylabel(
+                    r"$f(\tilde{q}_\mu\vert\mu^\prime,\theta_{\mu,\text{obs}})$ \\"
+                    r"$\mu=10^5$, $\mu^\prime=0$"
+                )
+            axs[ax].set_xlabel(rf"${statistic_math_name}$")
+            axs[ax].set_title(statistic.__class__.__name__)
+            axs[ax].legend()
+        self.fig = fig
+        self.axs = axs
 
     def plot(
-        self, linspace, bins, toys_ts_same, toys_ts_diff, statistic, statistic_math_name
+        self,
+        linspace_same,
+        linspace_diff,
+        bins_same,
+        bins_diff,
+        toys_ts_same,
+        toys_ts_diff,
+        statistic_sig,
+        statistic_bkg,
+        axs,
     ):
-        plt.hist(
+        print(self.poi_val)
+        axs["diff"].hist(
             toys_ts_diff,
-            bins=bins,
+            bins=bins_diff,
             density=True,
             histtype="step",
-            color="tab:blue",
-            label=(
-                rf"$f({statistic_math_name}\vert\mu^\prime)$, "
-                r"$\mu=1$, $\mu^\prime=0$"
-            ),
+            color="C0",
+            label=(r"MC"),
         )
-        plt.hist(
+        axs["same"].hist(
             toys_ts_same,
-            bins=bins,
+            bins=bins_same,
             density=True,
             histtype="step",
-            color="tab:orange",
-            label=(
-                rf"$f({statistic_math_name}\vert\mu^\prime)$, "
-                r"$\mu=1$, $\mu^\prime=1$"
-            ),
+            color="C1",
+            label=(r"MC"),
         )
 
-        plt.plot(
-            linspace,
-            statistic.asympotic_approximation_pdf(
-                poi_val=1, same=False, poi_true_val=0, ts_val=linspace
+        axs["diff"].plot(
+            linspace_diff,
+            statistic_bkg.asympotic_approximation_pdf(
+                poi_val=self.poi_val, same=False, poi_true_val=0, ts_val=linspace_diff
             ),
-            color="tab:blue",
-            label=rf"$f({statistic_math_name}\vert\mu^\prime)$, asympotic",
+            color="C0",
+            ls="--",
+            label=r"Asymptotic",
         )
-        plt.plot(
-            linspace,
-            statistic.asympotic_approximation_pdf(poi_val=1, ts_val=linspace),
-            color="tab:orange",
-            label=rf"$f({statistic_math_name}\vert\mu^\prime)$, asympotic",
+        axs["same"].plot(
+            linspace_same,
+            statistic_sig.asympotic_approximation_pdf(
+                poi_val=self.poi_val, ts_val=linspace_same
+            ),
+            color="C1",
+            ls="--",
+            label=r"Asymptotic",
         )
diff --git a/titrate/statistics.py b/titrate/statistics.py
index 784353c..b9bd1ec 100644
--- a/titrate/statistics.py
+++ b/titrate/statistics.py
@@ -4,7 +4,7 @@
 from gammapy.modeling import Fit
 from scipy.stats import kstwo, norm
 
-from titrate.datasets import AsimovMapDataset
+from titrate.datasets import AsimovMapDataset, AsimovSpectralDataset
 
 
 class POIError(IndexError):
@@ -58,7 +58,7 @@ def __init__(self, dataset, poi_name=""):
             )
 
         self.fit = Fit()
-        self.fit_result = self.fit.run(datasets=[self.dataset])
+        self.fit_result = self.fit.run(datasets=self.dataset)
         self.poi_best = self.fit_result.parameters[self.poi_name].value
         self.likelihood_minimum = self.dataset.stat_sum()
 
@@ -105,11 +105,6 @@ def check_for_pois(self):
         return pois
 
     def sigma(self):
-        if not isinstance(self.dataset, AsimovMapDataset):
-            raise AsimovApproximationError(
-                "`dataset` must be an `AsimovMapDataset` in order to calculate"
-                " `sigma`"
-            )
         return np.sqrt(self.fit_result.covariance_result.matrix[0, 0])
 
     def asympotic_approximation_pdf(
@@ -122,11 +117,6 @@ def asympotic_approximation_pdf(
                 * np.exp(-0.5 * (np.sqrt(ts_val)) ** 2)
             )
 
-        if not isinstance(self.dataset, AsimovMapDataset):
-            raise AsimovApproximationError(
-                "`dataset` must be an `AsimovMapDataset` in order to use the"
-                " `asympotic_approximation`"
-            )
         return (
             1
             / (2 * np.sqrt(2 * np.pi * ts_val))
@@ -141,12 +131,6 @@ def asympotic_approximation_cdf(
         if same:
             return norm.cdf(np.sqrt(ts_val))
 
-        if not isinstance(self.dataset, AsimovMapDataset):
-            raise AsimovApproximationError(
-                "`dataset` must be an `AsimovMapDataset` in order to use the"
-                " `asympotic_approximation`"
-            )
-
         return norm.cdf(np.sqrt(ts_val) - (poi_val - poi_true_val) / self.sigma())
 
     def pvalue(self, poi_val, same=True, poi_true_val=None, ts_val=None):
@@ -186,15 +170,18 @@ def __init__(self, dataset, poi_name=""):
             )
 
         self.fit = Fit()
-        self.fit_result = self.fit.run(datasets=[self.dataset])
+        self.fit_result = self.fit.run(datasets=self.dataset)
         self.poi_best = self.fit_result.parameters[self.poi_name].value
-        if self.poi_best < 0:
-            self.dataset.models.parameters[self.poi_name].scan_values = [0]
-            self.likelihood_constant = self.fit.stat_profile(
-                self.dataset, self.poi_name, reoptimize=True
-            )["stat_scan"]
-        else:
-            self.likelihood_constant = self.dataset.stat_sum()
+        if self.poi_best < 0 and not (
+            isinstance(self.dataset, AsimovMapDataset)
+            or isinstance(self.dataset, AsimovSpectralDataset)
+        ):
+            self.dataset.models.parameters[self.poi_name].value = 0
+            self.dataset.models.parameters[self.poi_name].frozen = True
+            self.fit_result = self.fit.run(datasets=self.dataset)
+            self.dataset.models.parameters[self.poi_name].frozen = False
+
+        self.likelihood_constant = self.dataset.stat_sum()
 
     def evaluate(self, poi_val):
         """
@@ -211,21 +198,17 @@ def evaluate(self, poi_val):
         global_fit_valid: bool
             True if the global fit is valid, False otherwise.
         """
-        global_fit_valid = True
         if self.poi_best > poi_val:
-            return np.array([0]), global_fit_valid
+            return np.array([0])
 
         self.dataset.models.parameters[self.poi_name].scan_values = [poi_val]
         stats = self.fit.stat_profile(self.dataset, self.poi_name, reoptimize=True)
         ts = stats["stat_scan"] - self.likelihood_constant
 
-        # catch the case when the test statistic is negative
-        # happens when the best fit value of the POI is not the global minimum
-        # of the likelihood
-        if ts < 0:
-            global_fit_valid = False
+        if ts < 0 and np.isclose(ts, 0, atol=1e-03):
+            ts = np.array([0])
 
-        return ts, global_fit_valid
+        return ts
 
     def check_for_pois(self):
         """POI must be a norm parameter by definition since
@@ -237,83 +220,80 @@ def check_for_pois(self):
                 pois.append(parameter.name)
         return pois
 
-    def sigma(self):
-        if not isinstance(self.dataset, AsimovMapDataset):
-            raise AsimovApproximationError(
-                "`dataset` must be an `AsimovMapDataset` in order to calculate"
-                " `sigma`"
-            )
-        return np.sqrt(self.fit_result.covariance_result.matrix[0, 0])
+    def sigma(self, poi_val, poi_true_val, same=False):
+        if poi_val == 0:
+            return np.sqrt(self.fit_result.covariance_result.matrix[0, 0])
+        if same:
+            return 0
+
+        ts = self.evaluate(poi_val)
+        return (poi_val - poi_true_val) / np.sqrt(ts)
 
     def asympotic_approximation_pdf(
         self, ts_val, poi_val, same=True, poi_true_val=None
     ):
-        if not isinstance(self.dataset, AsimovMapDataset):
-            raise AsimovApproximationError(
-                "`dataset` must be an `AsimovMapDataset` in order to use the"
-                " `asympotic_approximation`"
-            )
-
-        sigma = self.sigma()
+        nc = self.evaluate(poi_val)
 
         if same:
+            sigma = np.sqrt(self.fit_result.covariance_result.matrix[0, 0])
+            mu_sigma = poi_val**2 / sigma**2
             return np.where(
-                ts_val > poi_val**2 / sigma**2,
-                1
-                / (np.sqrt(2 * np.pi) * 2 * poi_val / sigma)
-                * np.exp(
-                    -0.5
-                    * (ts_val + poi_val**2 / sigma**2) ** 2
-                    / (2 * poi_val / sigma) ** 2
+                (ts_val > 0) & (ts_val <= mu_sigma),
+                (
+                    1
+                    / (2 * np.sqrt(2 * np.pi) * np.sqrt(ts_val))
+                    * np.exp(-0.5 * (np.sqrt(ts_val)) ** 2)
+                ),
+                (
+                    1
+                    / (np.sqrt(2 * np.pi) * (2 * np.sqrt(mu_sigma)))
+                    * np.exp(
+                        -0.5 * (ts_val + mu_sigma) ** 2 / ((2 * np.sqrt(mu_sigma)) ** 2)
+                    )
                 ),
-                1 / (2 * np.sqrt(2 * np.pi * ts_val)) * np.exp(-0.5 * ts_val),
             )
 
+        sigma = poi_val / np.sqrt(nc)
+        mu_sigma = poi_val**2 / sigma**2
         return np.where(
-            ts_val > poi_val**2 / sigma**2,
-            1
-            / (np.sqrt(2 * np.pi) * 2 * poi_val / sigma)
-            * np.exp(
-                -0.5
-                * (ts_val - (poi_val**2 - 2 * poi_val * poi_true_val) / sigma**2)
-                ** 2
-                / (2 * poi_val / sigma) ** 2
+            (ts_val > 0) & (ts_val <= mu_sigma),
+            (
+                1
+                / (2 * np.sqrt(2 * np.pi) * np.sqrt(ts_val))
+                * np.exp(-0.5 * (np.sqrt(ts_val) - np.sqrt(nc)) ** 2)
+            ),
+            (
+                1
+                / (np.sqrt(2 * np.pi) * (2 * np.sqrt(mu_sigma)))
+                * np.exp(-0.5 * (ts_val - nc) ** 2 / ((2 * np.sqrt(mu_sigma)) ** 2))
             ),
-            1
-            / (2 * np.sqrt(2 * np.pi * ts_val))
-            * np.exp(-0.5 * (np.sqrt(ts_val) - (poi_val - poi_true_val) / sigma) ** 2),
         )
 
     def asympotic_approximation_cdf(
         self, ts_val, poi_val, same=True, poi_true_val=None
     ):
-        if not isinstance(self.dataset, AsimovMapDataset):
-            raise AsimovApproximationError(
-                "`dataset` must be an `AsimovMapDataset` in order to use the"
-                " `asympotic_approximation`"
-            )
+        nc = self.evaluate(poi_val)
 
-        sigma = self.sigma()
         if same:
+            sigma = np.sqrt(self.fit_result.covariance_result.matrix[0, 0])
+            mu_sigma = poi_val**2 / sigma**2
             return np.where(
-                ts_val > poi_val**2 / sigma**2,
-                norm.cdf((ts_val + poi_val**2 / sigma**2) / (2 * poi_val / sigma)),
+                (ts_val > 0) & (ts_val <= mu_sigma),
                 norm.cdf(np.sqrt(ts_val)),
+                norm.cdf((ts_val + mu_sigma) / (2 * np.sqrt(mu_sigma))),
             )
 
+        sigma = poi_val / np.sqrt(nc)
+        mu_sigma = poi_val**2 / sigma**2
         return np.where(
-            ts_val > poi_val**2 / sigma**2,
-            norm.cdf(
-                (ts_val - (poi_val**2 - 2 * poi_val * poi_true_val) / sigma**2)
-                / (2 * poi_val / sigma)
-            ),
-            norm.cdf(np.sqrt(ts_val) - (poi_val - poi_true_val) / sigma),
+            (ts_val > 0) & (ts_val <= mu_sigma),
+            norm.cdf(np.sqrt(ts_val) - np.sqrt(nc)),
+            norm.cdf((ts_val - nc) / (2 * np.sqrt(mu_sigma))),
         )
 
     def pvalue(self, poi_val, same=True, poi_true_val=None, ts_val=None):
         if ts_val is None:
-            ts_val, valid = self.evaluate(poi_val)
-            ts_val = ts_val if valid else 0
+            ts_val = self.evaluate(poi_val)
         if same:
             return 1 - self.asympotic_approximation_cdf(ts_val, poi_val)
         return 1 - self.asympotic_approximation_cdf(
@@ -325,7 +305,7 @@ def significance(self, poi_val, same=True, poi_true_val=None, ts_val=None):
             ts_val, valid = self.evaluate(poi_val)
             ts_val = ts_val if valid else 0
         if same:
-            sigma = self.sigma()
+            sigma = self.sigma(poi_val, poi_true_val)
             if ts_val > poi_val**2 / sigma**2:
                 return (ts_val + poi_val**2 / sigma**2) / (2 * poi_val / sigma)
             else:
diff --git a/titrate/upperlimits.py b/titrate/upperlimits.py
index bffc336..a1e63fc 100644
--- a/titrate/upperlimits.py
+++ b/titrate/upperlimits.py
@@ -4,7 +4,8 @@
 import numpy as np
 from astropy.table import QTable
 from gammapy.astro.darkmatter import DarkMatterAnnihilationSpectralModel
-from gammapy.modeling import Fit
+from gammapy.modeling import Covariance, Fit
+from gammapy.modeling.fit import OptimizeResult
 from gammapy.modeling.models import (
     FoVBackgroundModel,
     Models,
@@ -15,9 +16,9 @@
 from scipy.optimize import brentq
 from scipy.stats import norm
 
-from titrate.datasets import AsimovMapDataset
+from titrate.datasets import AsimovMapDataset, AsimovSpectralDataset
 from titrate.statistics import QMuTestStatistic, QTildeMuTestStatistic
-from titrate.utils import copy_models_to_dataset
+from titrate.utils import copy_dataset_with_models, copy_models_to_dataset
 
 STATISTICS = {"qmu": QMuTestStatistic, "qtildemu": QTildeMuTestStatistic}
 CS = DarkMatterAnnihilationSpectralModel.THERMAL_RELIC_CROSS_SECTION
@@ -31,77 +32,96 @@ def __init__(
         poi_name="scale",
         cl=0.95,
         cl_type="s",
+        analysis="3d",
+        mu_guess=None,
     ):
         self.measurement_dataset = measurement_dataset
         self.poi_name = poi_name
+        self.stat_class_name = statistic
         if statistic not in STATISTICS.keys():
             raise ValueError(
                 "Statistic must be one of {}".format(list(STATISTICS.keys()))
             )
         self.statistic = STATISTICS[statistic](
-            self.measurement_dataset, poi_name=self.poi_name
+            copy_dataset_with_models(self.measurement_dataset), poi_name=self.poi_name
         )
 
         if cl_type not in ["s+b", "s"]:
             raise ValueError('cl_type must be either "s+b" or "s"')
         self.cl_type = cl_type
         self.cl = cl
+        self.analysis = analysis
+        self.mu_guess = mu_guess
+
+        # probaably need this dataset anyways
+        self.d_no_bkg = copy_dataset_with_models(self.measurement_dataset)
+        self.d_no_bkg.models.parameters[self.poi_name].value = 0
+        self.d_no_bkg.models.parameters[self.poi_name].frozen = True
+        fit = Fit()
+        _ = fit.run(self.d_no_bkg)
+        self.d_no_bkg.models.parameters[self.poi_name].frozen = False
+        if self.analysis == "3d":
+            self.no_signal_asimov_dataset = AsimovMapDataset.from_MapDataset(
+                self.d_no_bkg
+            )
+        elif self.analysis == "1d":
+            self.no_signal_asimov_dataset = AsimovSpectralDataset.from_SpectralDataset(
+                self.d_no_bkg
+            )
+        self.no_signal_statistic = STATISTICS[self.stat_class_name](
+            self.no_signal_asimov_dataset, poi_name=self.poi_name
+        )
+        # store the poi_ul somewhere
+        self.poi_ul = None
 
     def compute(self):
         poi_best = self.statistic.poi_best
 
         if poi_best < 0:
-            poi_ul = 1e-2
+            if self.mu_guess is not None:
+                poi_ul = self.mu_guess
+            else:
+                poi_ul = 1e-2
         else:
-            poi_ul = poi_best + 0.01 * poi_best
-        while self.pvalue(poi_ul, cl_type=self.cl_type) > 1 - self.cl or np.isnan(
-            self.pvalue(poi_ul, cl_type=self.cl_type)
-        ):
+            poi_ul = poi_best
+        prev_pval = 0
+        while (
+            (pval := self.pvalue(poi_ul, cl_type=self.cl_type)) > 1 - self.cl
+        ) or np.isnan(pval):
+            prev_pval = pval
             poi_ul *= 2
 
-        interp_ul_points = np.linspace(poi_ul / 2, poi_ul, 10)
+        interp_ul_points = np.linspace(poi_ul / 2, poi_ul, 5)
         interp_pvalues = np.array(
             [
                 self.pvalue(interp_ul, cl_type=self.cl_type)
                 for interp_ul in interp_ul_points
             ]
         ).ravel()
-        interpolation = interp1d(interp_ul_points, interp_pvalues - 1 + self.cl)
+
+        interpolation = interp1d(
+            interp_ul_points, interp_pvalues - 1 + self.cl, kind="quadratic"
+        )
+
         poi_ul = brentq(interpolation, poi_ul / 2, poi_ul)
 
+        print("FOUND:", poi_ul)
+        self.poi_ul = poi_ul
+
         return poi_ul
 
     def pvalue(self, poi_ul, cl_type):
         pval_bkg = 0
         if self.statistic.__class__.__name__ == "QTildeMuTestStatistic":
-            asimov_dataset = AsimovMapDataset.from_MapDataset(self.measurement_dataset)
-            asimov_dataset.models.parameters[self.poi_name].value = poi_ul
-            asimov_dataset.fake()
-            statistic = self.statistic.__class__(asimov_dataset, poi_name=self.poi_name)
-            ts_val, valid = self.statistic.evaluate(
-                poi_ul
-            )  # ts_val on measurement_dataset
-            if not valid:
-                ts_val = 0
-
-            pval_sig_bkg = statistic.pvalue(poi_ul, ts_val=ts_val)
+            ts_val = self.statistic.evaluate(poi_ul)  # ts_val on measurement_dataset
 
             if cl_type == "s":
-                no_signal_asimov_dataset = AsimovMapDataset.from_MapDataset(
-                    self.measurement_dataset
+                ts_val_bkg_asi = self.no_signal_statistic.evaluate(poi_ul)
+                return (1 - norm.cdf(np.sqrt(ts_val))) / norm.cdf(
+                    np.sqrt(ts_val_bkg_asi) - np.sqrt(ts_val)
                 )
-                no_signal_asimov_dataset.models.parameters[self.poi_name].value = 0
-                no_signal_asimov_dataset.fake()
-                no_signal_statistic = self.statistic.__class__(
-                    no_signal_asimov_dataset, poi_name=self.poi_name
-                )
-                ts_val_no_signal, valid_no_signal = self.statistic.evaluate(
-                    0
-                )  # ts_val on measurement_dataset with no signal
-                if not valid_no_signal:
-                    ts_val_no_signal = 0
-
-                pval_bkg = no_signal_statistic.pvalue(0, ts_val=ts_val_no_signal)
+            else:
+                return 1 - norm.cdf(np.sqrt(ts_val))
 
         else:
             pval_sig_bkg = self.statistic.pvalue(poi_ul)
@@ -112,15 +132,32 @@ def pvalue(self, poi_ul, cl_type):
         return pval_sig_bkg / (1 - pval_bkg)
 
     def expected_uls(self):
-        # Create asimov dataset
-        asimov_dataset = AsimovMapDataset.from_MapDataset(self.measurement_dataset)
-        asimov_dataset.models.parameters[self.poi_name].value = 0
-        asimov_dataset.fake()
+        # scan for poi_ul median
+        if self.cl_type == "s+b":
+            target_ts = norm.ppf(1 - (1 - self.cl)) ** 2
+        if self.cl_type == "s":
+            target_ts = norm.ppf(1 - 0.5 * (1 - self.cl)) ** 2
+
+        poi_ul = 1e-2
+        while (ts := self.no_signal_statistic.evaluate(poi_ul)) < target_ts:
+            poi_ul *= 2
 
-        fit = Fit()
-        fit_result = fit.run(datasets=[asimov_dataset])
+        interp_ul_points = np.linspace(poi_ul / 2, poi_ul, 10)
+        interp_ts = np.array(
+            [
+                self.no_signal_statistic.evaluate(interp_ul)
+                for interp_ul in interp_ul_points
+            ]
+        ).ravel()
+
+        interpolation = interp1d(
+            interp_ul_points, interp_ts - target_ts, kind="quadratic"
+        )
+
+        poi_ul = brentq(interpolation, poi_ul / 2, poi_ul)
+
+        sigma = poi_ul / np.sqrt(target_ts)
 
-        sigma = np.sqrt(fit_result.covariance_result.matrix[0, 0])
         med_ul = self.compute_band(sigma, 0, self.cl_type)
 
         one_sig_plus = self.compute_band(sigma, 1, self.cl_type)
@@ -137,9 +174,9 @@ def expected_uls(self):
 
     def compute_band(self, sigma, n_sigma, cl_type):
         if cl_type == "s+b":
-            return sigma * (norm.ppf(self.cl) + n_sigma)
+            return sigma * (norm.ppf(1 - (1 - self.cl)) + n_sigma)
         elif cl_type == "s":
-            return sigma * (norm.ppf(1 - (1 - self.cl) * norm.pdf(n_sigma)) + n_sigma)
+            return sigma * (norm.ppf(1 - (1 - self.cl) * norm.cdf(n_sigma)) + n_sigma)
 
 
 class ULFactory:
@@ -150,7 +187,8 @@ def __init__(
         mass_min,
         mass_max,
         n_steps,
-        jfactor_map,
+        jfactor,
+        analysis="3d",
         cl_type="s",
         cl=0.95,
         max_workers=None,
@@ -161,31 +199,56 @@ def __init__(
         self.masses = np.geomspace(
             mass_min.to_value("TeV"), mass_max.to_value("TeV"), n_steps
         )
-        self.jfactor_map = jfactor_map
+        self.jfactor = jfactor
+        self.analysis = analysis
         self.cl_type = cl_type
         self.cl = cl
         self.kwargs = kwargs
         self.max_workers = max_workers
         self.kwargs["cl"] = self.cl
         self.kwargs["cl_type"] = self.cl_type
+        self.kwargs["analysis"] = self.analysis
         self.uls = None
         self.expected_uls = None
 
     def setup_models(self):
         models = []
-        spatial_model = TemplateSpatialModel(self.jfactor_map, normalize=False)
-        bkg_model = FoVBackgroundModel(dataset_name="foo")
-        for channel in self.channels:
-            for mass in self.masses:
-                spectral_model = DarkMatterAnnihilationSpectralModel(
-                    mass=mass * u.TeV, channel=channel
-                )
-                sky_model = SkyModel(
-                    spatial_model=spatial_model,
-                    spectral_model=spectral_model,
-                    name=f"mass_{mass:.2f}TeV_channel_{channel}",
-                )
-                models.append(Models([sky_model, bkg_model]))
+        if self.analysis == "3d":
+            spatial_model = TemplateSpatialModel(self.jfactor, normalize=False)
+
+            if self.measurement_dataset.background_model:
+                bkg_model = self.measurement_dataset.background_model.copy()
+            else:
+                bkg_model = FoVBackgroundModel(dataset_name="foo")
+
+            for channel in self.channels:
+                for mass in self.masses:
+                    spectral_model = DarkMatterAnnihilationSpectralModel(
+                        mass=mass * u.TeV, channel=channel
+                    )
+                    sky_model = SkyModel(
+                        spatial_model=spatial_model,
+                        spectral_model=spectral_model,
+                        name=f"mass_{mass:.2f}TeV_channel_{channel}",
+                    )
+                    models.append(Models([sky_model, bkg_model]))
+
+        elif self.analysis == "1d":
+            if self.measurement_dataset.background_model:
+                bkg_model = self.measurement_dataset.background_model.copy()
+            else:
+                bkg_model = FoVBackgroundModel(dataset_name="foo")
+
+            for channel in self.channels:
+                for mass in self.masses:
+                    spectral_model = DarkMatterAnnihilationSpectralModel(
+                        mass=mass * u.TeV, channel=channel, jfactor=self.jfactor
+                    )
+                    sky_model = SkyModel(
+                        spectral_model=spectral_model,
+                        name=f"mass_{mass:.2f}TeV_channel_{channel}",
+                    )
+                    models.append(Models([sky_model, bkg_model]))
 
         return models
 
@@ -195,7 +258,7 @@ def setup_calculator(self, models):
         return ULCalculator(measurement_copy, **self.kwargs)
 
     def compute_uls(self):
-        with ProcessPoolExecutor(self.max_workers) as pool:
+        with ProcessPoolExecutor(max_workers=self.max_workers) as pool:
             futures = [
                 pool.submit(self.setup_calculator(models).compute)
                 for models in self.setup_models()
@@ -204,7 +267,7 @@ def compute_uls(self):
         self.uls = uls
 
     def compute_expected(self):
-        with ProcessPoolExecutor(self.max_workers) as pool:
+        with ProcessPoolExecutor(max_workers=self.max_workers) as pool:
             futures = [
                 pool.submit(self.setup_calculator(models).expected_uls)
                 for models in self.setup_models()
diff --git a/titrate/utils.py b/titrate/utils.py
index bcd695f..c9f1712 100644
--- a/titrate/utils.py
+++ b/titrate/utils.py
@@ -1,3 +1,6 @@
+from gammapy.datasets import Datasets
+
+
 def CopyModelError(AttributeError):
     """Something went wrong during copying a model."""
 
@@ -27,15 +30,20 @@ def copy_dataset_with_models(dataset):
 def copy_models_to_dataset(models, dataset):
     """Copies models and assigns them to dataset."""
     model_copies = models.copy()
-    for model in model_copies:
-        if hasattr(model, "_name"):
-            model._name = f"{dataset.name}-{model._name}"
-        elif hasattr(model, "datasets_names"):
-            model.datasets_names = [f"{dataset.name}"]
-        else:
-            raise CopyModelError(
-                f"{model.__class__.__name__} doesn't provided `._name`"
-                f"nor `.datasets_names`."
-            )
-
-    dataset.models = model_copies
+    if isinstance(dataset, Datasets):
+        for d in dataset:
+            b = model_copies[1].copy()
+            b.datasets_names = [d.name]
+            d.models = [model_copies[0], b]
+    else:
+        for model in model_copies:
+            if hasattr(model, "_name"):
+                model._name = f"{dataset.name}-{model._name}"
+            if hasattr(model, "datasets_names"):
+                model.datasets_names = [dataset.name]
+            else:
+                raise CopyModelError(
+                    f"{model.__class__.__name__} doesn't provided `._name`"
+                    f"nor `.datasets_names`."
+                )
+        dataset.models = model_copies
diff --git a/titrate/validation.py b/titrate/validation.py
index 757043a..97a2133 100644
--- a/titrate/validation.py
+++ b/titrate/validation.py
@@ -6,11 +6,12 @@
 from astropy.table import QTable
 from astropy.units import Quantity
 from gammapy.astro.darkmatter import DarkMatterAnnihilationSpectralModel
+from gammapy.modeling import Fit
 from gammapy.modeling.models import SkyModel
 
-from titrate.datasets import AsimovMapDataset
+from titrate.datasets import AsimovMapDataset, AsimovSpectralDataset
 from titrate.statistics import QMuTestStatistic, QTildeMuTestStatistic, kstest
-from titrate.utils import calc_ts_toyMC
+from titrate.utils import calc_ts_toyMC, copy_dataset_with_models
 
 STATISTICS = {"qmu": QMuTestStatistic, "qtildemu": QTildeMuTestStatistic}
 
@@ -25,6 +26,8 @@ def __init__(
         channel=None,
         mass=None,
         max_workers=None,
+        analysis="3d",
+        poi_val=1e5,
     ):
         if statistic not in STATISTICS.keys():
             raise ValueError(
@@ -32,9 +35,35 @@ def __init__(
             )
         self.statistic_key = statistic
         self.statistic = STATISTICS[statistic]
+        self.poi_name = poi_name
+        self.poi_val = poi_val
 
         self.measurement_dataset = measurement_dataset
-        self.asimov_dataset = AsimovMapDataset.from_MapDataset(self.measurement_dataset)
+        self.d_sig = copy_dataset_with_models(self.measurement_dataset)
+        self.d_sig.models.parameters[self.poi_name].value = self.poi_val
+        self.d_sig.models.parameters[self.poi_name].frozen = True
+        fit = Fit()
+        _ = fit.run(self.d_sig)
+        self.d_sig.models.parameters[self.poi_name].frozen = False
+
+        self.d_bkg = copy_dataset_with_models(self.measurement_dataset)
+        self.d_bkg.models.parameters[self.poi_name].value = 0
+        self.d_bkg.models.parameters[self.poi_name].frozen = True
+        fit = Fit()
+        _ = fit.run(self.d_bkg)
+        self.d_bkg.models.parameters[self.poi_name].frozen = False
+
+        self.analysis = analysis
+        if self.analysis == "3d":
+            self.asimov_sig_dataset = AsimovMapDataset.from_MapDataset(self.d_sig)
+            self.asimov_bkg_dataset = AsimovMapDataset.from_MapDataset(self.d_bkg)
+        elif self.analysis == "1d":
+            self.asimov_sig_dataset = AsimovSpectralDataset.from_SpectralDataset(
+                self.d_sig
+            )
+            self.asimov_bkg_dataset = AsimovSpectralDataset.from_SpectralDataset(
+                self.d_bkg
+            )
 
         self.path = path
         self.channel = channel
@@ -54,7 +83,6 @@ def __init__(
             masses = [Quantity(m) for m in masses if "meta" not in m]
             raise ValueError(f"Mass must be one of {masses}")
 
-        self.poi_name = poi_name
         self.max_workers = max_workers
 
         self.toys_ts_diff = None
@@ -65,17 +93,23 @@ def __init__(
     def validate(self, n_toys=1000):
         self.generate_datasets(n_toys)
 
-        stat = self.statistic(self.asimov_dataset, self.poi_name)
+        stat_sig = self.statistic(self.asimov_sig_dataset, self.poi_name)
+        stat_bkg = self.statistic(self.asimov_bkg_dataset, self.poi_name)
+        from scipy.stats import ks_1samp as kstest
+
         ks_diff = kstest(
             self.toys_ts_diff[self.toys_ts_diff_valid],
-            lambda x: stat.asympotic_approximation_cdf(
-                poi_val=1, same=False, poi_true_val=0, ts_val=x
+            lambda x: stat_bkg.asympotic_approximation_cdf(
+                poi_val=self.poi_val, same=False, poi_true_val=0, ts_val=x
             ),
-        )
+        ).pvalue
         ks_same = kstest(
             self.toys_ts_same[self.toys_ts_same_valid],
-            lambda x: stat.asympotic_approximation_cdf(poi_val=1, ts_val=x),
-        )
+            lambda x: stat_sig.asympotic_approximation_cdf(
+                poi_val=self.poi_val, ts_val=x
+            ),
+        ).pvalue
+        print(ks_diff, ks_same)
 
         valid = ks_diff > 0.05 and ks_same > 0.05
 
@@ -83,8 +117,12 @@ def validate(self, n_toys=1000):
 
     def generate_datasets(self, n_toys):
         if self.path is None:
-            toys_ts_diff, toys_ts_diff_valid = self.toys_ts(n_toys, 1, 0)
-            toys_ts_same, toys_ts_same_valid = self.toys_ts(n_toys, 1, 1)
+            toys_ts_diff, toys_ts_diff_valid = self.toys_ts(
+                n_toys, self.poi_val, 0, self.d_bkg
+            )
+            toys_ts_same, toys_ts_same_valid = self.toys_ts(
+                n_toys, self.poi_val, self.poi_val, self.d_sig
+            )
         else:
             (
                 toys_ts_diff,
@@ -99,12 +137,12 @@ def generate_datasets(self, n_toys):
         self.toys_ts_same_valid = toys_ts_same_valid
 
     @lru_cache
-    def toys_ts(self, n_toys, poi_val, poi_true_val):
+    def toys_ts(self, n_toys, poi_val, poi_true_val, dataset):
         with ProcessPoolExecutor(self.max_workers) as pool:
             futures = [
                 pool.submit(
                     calc_ts_toyMC,
-                    self.measurement_dataset,
+                    dataset,
                     self.statistic,
                     poi_val,
                     poi_true_val,
@@ -112,8 +150,8 @@ def toys_ts(self, n_toys, poi_val, poi_true_val):
                 )
                 for _ in range(n_toys)
             ]
-            toys_ts = [future.result()[0] for future in futures]
-            toys_valid = [future.result()[1] for future in futures]
+            toys_ts = [future.result() for future in futures]
+            toys_valid = [True for _ in range(len(toys_ts))]
 
         # to ndarray
         toys_ts = np.array(toys_ts).ravel()