We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
Strange warning when running the `SensitivityEstimator`
**Gammapy version**

Current main:

```
Gammapy support for parallelisation with ray is still a prototype and is not fully functional.

System:

	python_executable      : /home/maxnoe/.local/conda/envs/gammapy-dev/bin/python3.9 
	python_version         : 3.9.16     
	machine                : x86_64     
	system                 : Linux      


Gammapy package:

	version                : 1.2.dev201+g514451881.d20230627 
	path                   : /home/maxnoe/Projects/gammapy/gammapy 


Other packages:

	numpy                  : 1.25.0     
	scipy                  : 1.11.0     
	astropy                : 5.3        
	regions                : 0.7        
	click                  : 8.1.3      
	yaml                   : 6.0        
	IPython                : 8.14.0     
	jupyterlab             : 3.5.3      
	matplotlib             : 3.7.1      
	pandas                 : 2.0.2      
	healpy                 : 1.16.2     
	iminuit                : 2.22.0     
	sherpa                 : 4.15.1     
	naima                  : 0.10.0     
	emcee                  : 3.1.4      
	corner                 : 2.2.2      
	ray                    : 2.5.1      


Gammapy environment variables:

	GAMMAPY_DATA           : /home/maxnoe/Projects/gammapy/gammapy-datasets/dev 
```

**Bug description**

There is a warning I don't understand and that seems to be outside of my control and also is at least worded a bit strangely when running the `SensitivityEstimator`.
I guess this should also be a `GammapyDeprecationWarning` and not using the logging system.

> Table column name energy will be deprecated by e_ref since v1.2


What is the intention here? Will the `energy` column be removed? Would a better message then be:


> The column "energy" is deprecated as of Gammapy 1.x and will be removed in Gammapy 1.y. Use the `e_ref` column instead

?

--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `gammapy/estimators/points/sensitivity.py`
Content:
```
1 # Licensed under a 3-clause BSD style license - see LICENSE.rst
2 import logging
3 import numpy as np
4 from astropy.table import Column, Table
5 from gammapy.maps import Map
6 from gammapy.modeling.models import PowerLawSpectralModel, SkyModel
7 from gammapy.stats import WStatCountsStatistic
8 from ..core import Estimator
9 
10 __all__ = ["SensitivityEstimator"]
11 
12 
13 class SensitivityEstimator(Estimator):
14     """Estimate sensitivity.
15 
16     This class allows to determine for each reconstructed energy bin the flux
17     associated to the number of gamma-ray events for which the significance is
18     ``n_sigma``, and being larger than ``gamma_min`` and ``bkg_sys`` percent
19     larger than the number of background events in the ON region.
20 
21 
22     Parameters
23     ----------
24     spectrum : `SpectralModel`
25         Spectral model assumption. Default is Power Law with index 2.
26     n_sigma : float, optional
27         Minimum significance. Default is 5.
28     gamma_min : float, optional
29         Minimum number of gamma-rays. Default is 10.
30     bkg_syst_fraction : float, optional
31         Fraction of background counts above which the number of gamma-rays is. Default is 0.05.
32 
33     Examples
34     --------
35     For a usage example see :doc:`/tutorials/analysis-1d/cta_sensitivity` tutorial.
36 
37     """
38 
39     tag = "SensitivityEstimator"
40 
41     def __init__(
42         self,
43         spectrum=None,
44         n_sigma=5.0,
45         gamma_min=10,
46         bkg_syst_fraction=0.05,
47     ):
48 
49         if spectrum is None:
50             spectrum = PowerLawSpectralModel(index=2, amplitude="1 cm-2 s-1 TeV-1")
51 
52         self.spectrum = spectrum
53         self.n_sigma = n_sigma
54         self.gamma_min = gamma_min
55         self.bkg_syst_fraction = bkg_syst_fraction
56 
57     def estimate_min_excess(self, dataset):
58         """Estimate minimum excess to reach the given significance.
59 
60         Parameters
61         ----------
62         dataset : `SpectrumDataset`
63             Spectrum dataset.
64 
65         Returns
66         -------
67         excess : `~gammapy.maps.RegionNDMap`
68             Minimal excess.
69         """
70         n_off = dataset.counts_off.data
71 
72         stat = WStatCountsStatistic(
73             n_on=dataset.alpha.data * n_off, n_off=n_off, alpha=dataset.alpha.data
74         )
75         excess_counts = stat.n_sig_matching_significance(self.n_sigma)
76         is_gamma_limited = excess_counts < self.gamma_min
77         excess_counts[is_gamma_limited] = self.gamma_min
78         bkg_syst_limited = (
79             excess_counts < self.bkg_syst_fraction * dataset.background.data
80         )
81         excess_counts[bkg_syst_limited] = (
82             self.bkg_syst_fraction * dataset.background.data[bkg_syst_limited]
83         )
84         excess = Map.from_geom(geom=dataset._geom, data=excess_counts)
85         return excess
86 
87     def estimate_min_e2dnde(self, excess, dataset):
88         """Estimate dnde from a given minimum excess.
89 
90         Parameters
91         ----------
92         excess : `~gammapy.maps.RegionNDMap`
93             Minimal excess.
94         dataset : `~gammapy.datasets.SpectrumDataset`
95             Spectrum dataset.
96 
97         Returns
98         -------
99         e2dnde : `~astropy.units.Quantity`
100             Minimal differential flux.
101         """
102         energy = dataset._geom.axes["energy"].center
103 
104         dataset.models = SkyModel(spectral_model=self.spectrum)
105         npred = dataset.npred_signal()
106 
107         phi_0 = excess / npred
108 
109         dnde_model = self.spectrum(energy=energy)
110         dnde = phi_0.data[:, 0, 0] * dnde_model * energy**2
111         return dnde.to("erg / (cm2 s)")
112 
113     def _get_criterion(self, excess, bkg):
114         is_gamma_limited = excess == self.gamma_min
115         is_bkg_syst_limited = excess == bkg * self.bkg_syst_fraction
116         criterion = np.chararray(excess.shape, itemsize=12)
117         criterion[is_gamma_limited] = "gamma"
118         criterion[is_bkg_syst_limited] = "bkg"
119         criterion[~np.logical_or(is_gamma_limited, is_bkg_syst_limited)] = (
120             "significance"
121         )
122         return criterion
123 
124     def run(self, dataset):
125         """Run the sensitivity estimation.
126 
127         Parameters
128         ----------
129         dataset : `SpectrumDatasetOnOff`
130             Dataset to compute sensitivity for.
131 
132         Returns
133         -------
134         sensitivity : `~astropy.table.Table`
135             Sensitivity table.
136         """
137         energy = dataset._geom.axes["energy"].center
138         excess = self.estimate_min_excess(dataset)
139         e2dnde = self.estimate_min_e2dnde(excess, dataset)
140         criterion = self._get_criterion(
141             excess.data.squeeze(), dataset.background.data.squeeze()
142         )
143         logging.warning(
144             "Table column name energy will be deprecated by e_ref since v1.2"
145         )
146 
147         return Table(
148             [
149                 Column(
150                     data=energy,
151                     name="energy",
152                     format="5g",
153                     description="Reconstructed Energy",
154                 ),
155                 Column(
156                     data=energy,
157                     name="e_ref",
158                     format="5g",
159                     description="Energy center",
160                 ),
161                 Column(
162                     data=dataset._geom.axes["energy"].edges_min,
163                     name="e_min",
164                     format="5g",
165                     description="Energy edge low",
166                 ),
167                 Column(
168                     data=dataset._geom.axes["energy"].edges_max,
169                     name="e_max",
170                     format="5g",
171                     description="Energy edge high",
172                 ),
173                 Column(
174                     data=e2dnde,
175                     name="e2dnde",
176                     format="5g",
177                     description="Energy squared times differential flux",
178                 ),
179                 Column(
180                     data=np.atleast_1d(excess.data.squeeze()),
181                     name="excess",
182                     format="5g",
183                     description="Number of excess counts in the bin",
184                 ),
185                 Column(
186                     data=np.atleast_1d(dataset.background.data.squeeze()),
187                     name="background",
188                     format="5g",
189                     description="Number of background counts in the bin",
190                 ),
191                 Column(
192                     data=np.atleast_1d(criterion),
193                     name="criterion",
194                     description="Sensitivity-limiting criterion",
195                 ),
196             ]
197         )
198
```
--- END FILES ---

Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.

Here is an example:

```diff
diff --git a/examples/server_async.py b/examples/server_async.py
--- a/examples/server_async.py
+++ b/examples/server_async.py
@@ -313,4 +313,4 @@
 
 
 if __name__ == "__main__":
-    asyncio.run(run_async_server("."), debug=True)
+    asyncio.run(run_async_server(), debug=True)
diff --git a/examples/server_sync.py b/examples/server_sync.py
--- a/examples/server_sync.py
+++ b/examples/server_sync.py
@@ -313,5 +313,5 @@
 
 
 if __name__ == "__main__":
-    server = run_sync_server(".")
+    server = run_sync_server()
     server.shutdown()

```

diff --git a/gammapy/estimators/points/sensitivity.py b/gammapy/estimators/points/sensitivity.py
--- a/gammapy/estimators/points/sensitivity.py
+++ b/gammapy/estimators/points/sensitivity.py
@@ -1,5 +1,4 @@
 # Licensed under a 3-clause BSD style license - see LICENSE.rst
-import logging
 import numpy as np
 from astropy.table import Column, Table
 from gammapy.maps import Map
@@ -140,18 +139,9 @@
         criterion = self._get_criterion(
             excess.data.squeeze(), dataset.background.data.squeeze()
         )
-        logging.warning(
-            "Table column name energy will be deprecated by e_ref since v1.2"
-        )
 
         return Table(
             [
-                Column(
-                    data=energy,
-                    name="energy",
-                    format="5g",
-                    description="Reconstructed Energy",
-                ),
                 Column(
                     data=energy,
                     name="e_ref",

{"golden_diff": "diff --git a/gammapy/estimators/points/sensitivity.py b/gammapy/estimators/points/sensitivity.py\n--- a/gammapy/estimators/points/sensitivity.py\n+++ b/gammapy/estimators/points/sensitivity.py\n@@ -1,5 +1,4 @@\n # Licensed under a 3-clause BSD style license - see LICENSE.rst\n-import logging\n import numpy as np\n from astropy.table import Column, Table\n from gammapy.maps import Map\n@@ -140,18 +139,9 @@\n         criterion = self._get_criterion(\n             excess.data.squeeze(), dataset.background.data.squeeze()\n         )\n-        logging.warning(\n-            \"Table column name energy will be deprecated by e_ref since v1.2\"\n-        )\n \n         return Table(\n             [\n-                Column(\n-                    data=energy,\n-                    name=\"energy\",\n-                    format=\"5g\",\n-                    description=\"Reconstructed Energy\",\n-                ),\n                 Column(\n                     data=energy,\n                     name=\"e_ref\",\n", "issue": "Strange warning when running the `SensitivityEstimator`\n**Gammapy version**\r\n\r\nCurrent main:\r\n\r\n```\r\nGammapy support for parallelisation with ray is still a prototype and is not fully functional.\r\n\r\nSystem:\r\n\r\n\tpython_executable      : /home/maxnoe/.local/conda/envs/gammapy-dev/bin/python3.9 \r\n\tpython_version         : 3.9.16     \r\n\tmachine                : x86_64     \r\n\tsystem                 : Linux      \r\n\r\n\r\nGammapy package:\r\n\r\n\tversion                : 1.2.dev201+g514451881.d20230627 \r\n\tpath                   : /home/maxnoe/Projects/gammapy/gammapy \r\n\r\n\r\nOther packages:\r\n\r\n\tnumpy                  : 1.25.0     \r\n\tscipy                  : 1.11.0     \r\n\tastropy                : 5.3        \r\n\tregions                : 0.7        \r\n\tclick                  : 8.1.3      \r\n\tyaml                   : 6.0        \r\n\tIPython                : 8.14.0     \r\n\tjupyterlab             : 3.5.3      \r\n\tmatplotlib             : 3.7.1      \r\n\tpandas                 : 2.0.2      \r\n\thealpy                 : 1.16.2     \r\n\timinuit                : 2.22.0     \r\n\tsherpa                 : 4.15.1     \r\n\tnaima                  : 0.10.0     \r\n\temcee                  : 3.1.4      \r\n\tcorner                 : 2.2.2      \r\n\tray                    : 2.5.1      \r\n\r\n\r\nGammapy environment variables:\r\n\r\n\tGAMMAPY_DATA           : /home/maxnoe/Projects/gammapy/gammapy-datasets/dev \r\n```\r\n\r\n**Bug description**\r\n\r\nThere is a warning I don't understand and that seems to be outside of my control and also is at least worded a bit strangely when running the `SensitivityEstimator`.\r\nI guess this should also be a `GammapyDeprecationWarning` and not using the logging system.\r\n\r\n> Table column name energy will be deprecated by e_ref since v1.2\r\n\r\n\r\nWhat is the intention here? Will the `energy` column be removed? Would a better message then be:\r\n\r\n\r\n> The column \"energy\" is deprecated as of Gammapy 1.x and will be removed in Gammapy 1.y. Use the `e_ref` column instead\r\n\r\n?\n", "before_files": [{"content": "# Licensed under a 3-clause BSD style license - see LICENSE.rst\nimport logging\nimport numpy as np\nfrom astropy.table import Column, Table\nfrom gammapy.maps import Map\nfrom gammapy.modeling.models import PowerLawSpectralModel, SkyModel\nfrom gammapy.stats import WStatCountsStatistic\nfrom ..core import Estimator\n\n__all__ = [\"SensitivityEstimator\"]\n\n\nclass SensitivityEstimator(Estimator):\n    \"\"\"Estimate sensitivity.\n\n    This class allows to determine for each reconstructed energy bin the flux\n    associated to the number of gamma-ray events for which the significance is\n    ``n_sigma``, and being larger than ``gamma_min`` and ``bkg_sys`` percent\n    larger than the number of background events in the ON region.\n\n\n    Parameters\n    ----------\n    spectrum : `SpectralModel`\n        Spectral model assumption. Default is Power Law with index 2.\n    n_sigma : float, optional\n        Minimum significance. Default is 5.\n    gamma_min : float, optional\n        Minimum number of gamma-rays. Default is 10.\n    bkg_syst_fraction : float, optional\n        Fraction of background counts above which the number of gamma-rays is. Default is 0.05.\n\n    Examples\n    --------\n    For a usage example see :doc:`/tutorials/analysis-1d/cta_sensitivity` tutorial.\n\n    \"\"\"\n\n    tag = \"SensitivityEstimator\"\n\n    def __init__(\n        self,\n        spectrum=None,\n        n_sigma=5.0,\n        gamma_min=10,\n        bkg_syst_fraction=0.05,\n    ):\n\n        if spectrum is None:\n            spectrum = PowerLawSpectralModel(index=2, amplitude=\"1 cm-2 s-1 TeV-1\")\n\n        self.spectrum = spectrum\n        self.n_sigma = n_sigma\n        self.gamma_min = gamma_min\n        self.bkg_syst_fraction = bkg_syst_fraction\n\n    def estimate_min_excess(self, dataset):\n        \"\"\"Estimate minimum excess to reach the given significance.\n\n        Parameters\n        ----------\n        dataset : `SpectrumDataset`\n            Spectrum dataset.\n\n        Returns\n        -------\n        excess : `~gammapy.maps.RegionNDMap`\n            Minimal excess.\n        \"\"\"\n        n_off = dataset.counts_off.data\n\n        stat = WStatCountsStatistic(\n            n_on=dataset.alpha.data * n_off, n_off=n_off, alpha=dataset.alpha.data\n        )\n        excess_counts = stat.n_sig_matching_significance(self.n_sigma)\n        is_gamma_limited = excess_counts < self.gamma_min\n        excess_counts[is_gamma_limited] = self.gamma_min\n        bkg_syst_limited = (\n            excess_counts < self.bkg_syst_fraction * dataset.background.data\n        )\n        excess_counts[bkg_syst_limited] = (\n            self.bkg_syst_fraction * dataset.background.data[bkg_syst_limited]\n        )\n        excess = Map.from_geom(geom=dataset._geom, data=excess_counts)\n        return excess\n\n    def estimate_min_e2dnde(self, excess, dataset):\n        \"\"\"Estimate dnde from a given minimum excess.\n\n        Parameters\n        ----------\n        excess : `~gammapy.maps.RegionNDMap`\n            Minimal excess.\n        dataset : `~gammapy.datasets.SpectrumDataset`\n            Spectrum dataset.\n\n        Returns\n        -------\n        e2dnde : `~astropy.units.Quantity`\n            Minimal differential flux.\n        \"\"\"\n        energy = dataset._geom.axes[\"energy\"].center\n\n        dataset.models = SkyModel(spectral_model=self.spectrum)\n        npred = dataset.npred_signal()\n\n        phi_0 = excess / npred\n\n        dnde_model = self.spectrum(energy=energy)\n        dnde = phi_0.data[:, 0, 0] * dnde_model * energy**2\n        return dnde.to(\"erg / (cm2 s)\")\n\n    def _get_criterion(self, excess, bkg):\n        is_gamma_limited = excess == self.gamma_min\n        is_bkg_syst_limited = excess == bkg * self.bkg_syst_fraction\n        criterion = np.chararray(excess.shape, itemsize=12)\n        criterion[is_gamma_limited] = \"gamma\"\n        criterion[is_bkg_syst_limited] = \"bkg\"\n        criterion[~np.logical_or(is_gamma_limited, is_bkg_syst_limited)] = (\n            \"significance\"\n        )\n        return criterion\n\n    def run(self, dataset):\n        \"\"\"Run the sensitivity estimation.\n\n        Parameters\n        ----------\n        dataset : `SpectrumDatasetOnOff`\n            Dataset to compute sensitivity for.\n\n        Returns\n        -------\n        sensitivity : `~astropy.table.Table`\n            Sensitivity table.\n        \"\"\"\n        energy = dataset._geom.axes[\"energy\"].center\n        excess = self.estimate_min_excess(dataset)\n        e2dnde = self.estimate_min_e2dnde(excess, dataset)\n        criterion = self._get_criterion(\n            excess.data.squeeze(), dataset.background.data.squeeze()\n        )\n        logging.warning(\n            \"Table column name energy will be deprecated by e_ref since v1.2\"\n        )\n\n        return Table(\n            [\n                Column(\n                    data=energy,\n                    name=\"energy\",\n                    format=\"5g\",\n                    description=\"Reconstructed Energy\",\n                ),\n                Column(\n                    data=energy,\n                    name=\"e_ref\",\n                    format=\"5g\",\n                    description=\"Energy center\",\n                ),\n                Column(\n                    data=dataset._geom.axes[\"energy\"].edges_min,\n                    name=\"e_min\",\n                    format=\"5g\",\n                    description=\"Energy edge low\",\n                ),\n                Column(\n                    data=dataset._geom.axes[\"energy\"].edges_max,\n                    name=\"e_max\",\n                    format=\"5g\",\n                    description=\"Energy edge high\",\n                ),\n                Column(\n                    data=e2dnde,\n                    name=\"e2dnde\",\n                    format=\"5g\",\n                    description=\"Energy squared times differential flux\",\n                ),\n                Column(\n                    data=np.atleast_1d(excess.data.squeeze()),\n                    name=\"excess\",\n                    format=\"5g\",\n                    description=\"Number of excess counts in the bin\",\n                ),\n                Column(\n                    data=np.atleast_1d(dataset.background.data.squeeze()),\n                    name=\"background\",\n                    format=\"5g\",\n                    description=\"Number of background counts in the bin\",\n                ),\n                Column(\n                    data=np.atleast_1d(criterion),\n                    name=\"criterion\",\n                    description=\"Sensitivity-limiting criterion\",\n                ),\n            ]\n        )\n", "path": "gammapy/estimators/points/sensitivity.py"}], "after_files": [{"content": "# Licensed under a 3-clause BSD style license - see LICENSE.rst\nimport numpy as np\nfrom astropy.table import Column, Table\nfrom gammapy.maps import Map\nfrom gammapy.modeling.models import PowerLawSpectralModel, SkyModel\nfrom gammapy.stats import WStatCountsStatistic\nfrom ..core import Estimator\n\n__all__ = [\"SensitivityEstimator\"]\n\n\nclass SensitivityEstimator(Estimator):\n    \"\"\"Estimate sensitivity.\n\n    This class allows to determine for each reconstructed energy bin the flux\n    associated to the number of gamma-ray events for which the significance is\n    ``n_sigma``, and being larger than ``gamma_min`` and ``bkg_sys`` percent\n    larger than the number of background events in the ON region.\n\n\n    Parameters\n    ----------\n    spectrum : `SpectralModel`\n        Spectral model assumption. Default is Power Law with index 2.\n    n_sigma : float, optional\n        Minimum significance. Default is 5.\n    gamma_min : float, optional\n        Minimum number of gamma-rays. Default is 10.\n    bkg_syst_fraction : float, optional\n        Fraction of background counts above which the number of gamma-rays is. Default is 0.05.\n\n    Examples\n    --------\n    For a usage example see :doc:`/tutorials/analysis-1d/cta_sensitivity` tutorial.\n\n    \"\"\"\n\n    tag = \"SensitivityEstimator\"\n\n    def __init__(\n        self,\n        spectrum=None,\n        n_sigma=5.0,\n        gamma_min=10,\n        bkg_syst_fraction=0.05,\n    ):\n\n        if spectrum is None:\n            spectrum = PowerLawSpectralModel(index=2, amplitude=\"1 cm-2 s-1 TeV-1\")\n\n        self.spectrum = spectrum\n        self.n_sigma = n_sigma\n        self.gamma_min = gamma_min\n        self.bkg_syst_fraction = bkg_syst_fraction\n\n    def estimate_min_excess(self, dataset):\n        \"\"\"Estimate minimum excess to reach the given significance.\n\n        Parameters\n        ----------\n        dataset : `SpectrumDataset`\n            Spectrum dataset.\n\n        Returns\n        -------\n        excess : `~gammapy.maps.RegionNDMap`\n            Minimal excess.\n        \"\"\"\n        n_off = dataset.counts_off.data\n\n        stat = WStatCountsStatistic(\n            n_on=dataset.alpha.data * n_off, n_off=n_off, alpha=dataset.alpha.data\n        )\n        excess_counts = stat.n_sig_matching_significance(self.n_sigma)\n        is_gamma_limited = excess_counts < self.gamma_min\n        excess_counts[is_gamma_limited] = self.gamma_min\n        bkg_syst_limited = (\n            excess_counts < self.bkg_syst_fraction * dataset.background.data\n        )\n        excess_counts[bkg_syst_limited] = (\n            self.bkg_syst_fraction * dataset.background.data[bkg_syst_limited]\n        )\n        excess = Map.from_geom(geom=dataset._geom, data=excess_counts)\n        return excess\n\n    def estimate_min_e2dnde(self, excess, dataset):\n        \"\"\"Estimate dnde from a given minimum excess.\n\n        Parameters\n        ----------\n        excess : `~gammapy.maps.RegionNDMap`\n            Minimal excess.\n        dataset : `~gammapy.datasets.SpectrumDataset`\n            Spectrum dataset.\n\n        Returns\n        -------\n        e2dnde : `~astropy.units.Quantity`\n            Minimal differential flux.\n        \"\"\"\n        energy = dataset._geom.axes[\"energy\"].center\n\n        dataset.models = SkyModel(spectral_model=self.spectrum)\n        npred = dataset.npred_signal()\n\n        phi_0 = excess / npred\n\n        dnde_model = self.spectrum(energy=energy)\n        dnde = phi_0.data[:, 0, 0] * dnde_model * energy**2\n        return dnde.to(\"erg / (cm2 s)\")\n\n    def _get_criterion(self, excess, bkg):\n        is_gamma_limited = excess == self.gamma_min\n        is_bkg_syst_limited = excess == bkg * self.bkg_syst_fraction\n        criterion = np.chararray(excess.shape, itemsize=12)\n        criterion[is_gamma_limited] = \"gamma\"\n        criterion[is_bkg_syst_limited] = \"bkg\"\n        criterion[~np.logical_or(is_gamma_limited, is_bkg_syst_limited)] = (\n            \"significance\"\n        )\n        return criterion\n\n    def run(self, dataset):\n        \"\"\"Run the sensitivity estimation.\n\n        Parameters\n        ----------\n        dataset : `SpectrumDatasetOnOff`\n            Dataset to compute sensitivity for.\n\n        Returns\n        -------\n        sensitivity : `~astropy.table.Table`\n            Sensitivity table.\n        \"\"\"\n        energy = dataset._geom.axes[\"energy\"].center\n        excess = self.estimate_min_excess(dataset)\n        e2dnde = self.estimate_min_e2dnde(excess, dataset)\n        criterion = self._get_criterion(\n            excess.data.squeeze(), dataset.background.data.squeeze()\n        )\n\n        return Table(\n            [\n                Column(\n                    data=energy,\n                    name=\"e_ref\",\n                    format=\"5g\",\n                    description=\"Energy center\",\n                ),\n                Column(\n                    data=dataset._geom.axes[\"energy\"].edges_min,\n                    name=\"e_min\",\n                    format=\"5g\",\n                    description=\"Energy edge low\",\n                ),\n                Column(\n                    data=dataset._geom.axes[\"energy\"].edges_max,\n                    name=\"e_max\",\n                    format=\"5g\",\n                    description=\"Energy edge high\",\n                ),\n                Column(\n                    data=e2dnde,\n                    name=\"e2dnde\",\n                    format=\"5g\",\n                    description=\"Energy squared times differential flux\",\n                ),\n                Column(\n                    data=np.atleast_1d(excess.data.squeeze()),\n                    name=\"excess\",\n                    format=\"5g\",\n                    description=\"Number of excess counts in the bin\",\n                ),\n                Column(\n                    data=np.atleast_1d(dataset.background.data.squeeze()),\n                    name=\"background\",\n                    format=\"5g\",\n                    description=\"Number of background counts in the bin\",\n                ),\n                Column(\n                    data=np.atleast_1d(criterion),\n                    name=\"criterion\",\n                    description=\"Sensitivity-limiting criterion\",\n                ),\n            ]\n        )\n", "path": "gammapy/estimators/points/sensitivity.py"}]}