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diff --git a/docs/dev/.buildinfo b/docs/dev/.buildinfo
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 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: f24a531e77cf224f324d9627ee2ee37f
+config: 8e5b1a17777fd847e8364870d57269f5
 tags: 645f666f9bcd5a90fca523b33c5a78b7
diff --git a/docs/dev/.doctrees/api/gammapy.data.EventListMetaData.doctree b/docs/dev/.doctrees/api/gammapy.data.EventListMetaData.doctree
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diff --git a/docs/dev/.doctrees/api/gammapy.estimators.FluxMetaData.doctree b/docs/dev/.doctrees/api/gammapy.estimators.FluxMetaData.doctree
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diff --git a/docs/dev/.doctrees/user-guide/model-gallery/temporal/sg_execution_times.doctree b/docs/dev/.doctrees/user-guide/model-gallery/temporal/sg_execution_times.doctree
diff --git a/docs/dev/_downloads/062bafee8fcb97ea6e8ccd016a8baf1e/plot_bkg_3d.pdf b/docs/dev/_downloads/062bafee8fcb97ea6e8ccd016a8baf1e/plot_bkg_3d.pdf
diff --git a/docs/dev/_downloads/068f4a465f9ece97104fde9df27a1c7b/model-gallery_jupyter.zip b/docs/dev/_downloads/068f4a465f9ece97104fde9df27a1c7b/model-gallery_jupyter.zip
diff --git a/docs/dev/_downloads/10f1f97c8b6423f5cd3c3dd929ba1c7d/plot_stack.pdf b/docs/dev/_downloads/10f1f97c8b6423f5cd3c3dd929ba1c7d/plot_stack.pdf
diff --git a/docs/dev/_downloads/1574af02de7a1c335664819e66ace9e7/datasets.py b/docs/dev/_downloads/1574af02de7a1c335664819e66ace9e7/datasets.py
@@ -299,9 +299,9 @@
 ######################################################################
 # To access the energy range defined by the mask you can use:
 #
-# -`~gammapy.datasets.MapDataset.energy_range_safe` : energy range defined by the `~gammapy.datasets.MapDataset.mask_safe`
-# - `~gammapy.datasets.MapDataset.energy_range_fit` : energy range defined by the `~gammapy.datasets.MapDataset.mask_fit`
-# - `~gammapy.datasets.MapDataset.energy_range` : the final energy range used in likelihood computation
+# -  `~gammapy.datasets.MapDataset.energy_range_safe` : energy range defined by the `~gammapy.datasets.MapDataset.mask_safe`
+# -  `~gammapy.datasets.MapDataset.energy_range_fit` : energy range defined by the `~gammapy.datasets.MapDataset.mask_fit`
+# -  `~gammapy.datasets.MapDataset.energy_range` : the final energy range used in likelihood computation
 #
 # These methods return two maps, with the `min` and `max` energy
 # values at each spatial pixel
@@ -324,7 +324,7 @@
 ######################################################################
 # Just as for `~gammapy.maps.Map` objects it is possible to cutout a whole
 # `~gammapy.datasets.MapDataset`, which will perform the cutout for all maps in
-# parallel.Optionally one can provide a new name to the resulting dataset:
+# parallel. Optionally one can provide a new name to the resulting dataset:
 #
 
 cutout = dataset_cta.cutout(
@@ -417,8 +417,8 @@
 # ---------------
 #
 # `~gammapy.datasets.SpectrumDataset` inherits from a `~gammapy.datasets.MapDataset`, and is specially
-# adapted for 1D spectral analysis, and uses a `RegionGeom` instead of a
-# `WcsGeom`. A `~gammapy.datasets.MapDataset` can be converted to a `~gammapy.datasets.SpectrumDataset`,
+# adapted for 1D spectral analysis, and uses a `~gammapy.maps.RegionGeom` instead of a
+# `~gammapy.maps.WcsGeom`. A `~gammapy.datasets.MapDataset` can be converted to a `~gammapy.datasets.SpectrumDataset`,
 # by summing the `counts` and `background` inside the `on_region`,
 # which can then be used for classical spectral analysis. Containment
 # correction is feasible only for circular regions.
@@ -485,7 +485,7 @@
 ######################################################################
 #
 # For an example of fitting `~gammapy.estimators.FluxPoints`, see :doc:`/tutorials/analysis-1d/sed_fitting`,
-# and for using source catalogs see :doc:`/tutorials/api/catalog`
+# and for using source catalogs see :doc:`/tutorials/api/catalog`.
 #
 
 
@@ -499,11 +499,12 @@
 #
 # For modelling and fitting of a list of `~gammapy.datasets.Dataset` objects, you can
 # either:
-# (a) Do a joint fitting of all the datasets together OR
-# (b) Stack the datasets together, and then fit them.
+#
+# -  (A) Do a joint fitting of all the datasets together **OR**
+# -  (B) Stack the datasets together, and then fit them.
 #
 # `~gammapy.datasets.Datasets` is a convenient tool to handle joint fitting of
-# simultaneous datasets. As an example, please see :doc:`/tutorials/analysis-3d/analysis_mwl`
+# simultaneous datasets. As an example, please see :doc:`/tutorials/analysis-3d/analysis_mwl`.
 #
 # To see how stacking is performed, please see :ref:`stack`.
 #

diff --git a/docs/dev/_downloads/16d6399e308f3f4937d0512c9c9dd023/plot_psf.pdf b/docs/dev/_downloads/16d6399e308f3f4937d0512c9c9dd023/plot_psf.pdf
diff --git a/docs/dev/_downloads/191e39c4584da5619df61ef94dbf1770/plot_spiral_arms.hires.png b/docs/dev/_downloads/191e39c4584da5619df61ef94dbf1770/plot_spiral_arms.hires.png
diff --git a/docs/dev/_downloads/1ccaeffb4e281faa566ce00a9de4d42f/energy_dependent_estimation.py b/docs/dev/_downloads/1ccaeffb4e281faa566ce00a9de4d42f/energy_dependent_estimation.py
@@ -247,4 +247,4 @@
 plt.show()
 
 
-# sphinx_gallery_thumbnail_number = 2
+# sphinx_gallery_thumbnail_number = 1
diff --git a/docs/dev/_downloads/26224d3995e07325df7f0b64d1681fb9/regionmap-1.pdf b/docs/dev/_downloads/26224d3995e07325df7f0b64d1681fb9/regionmap-1.pdf
diff --git a/docs/dev/_downloads/315c4c52fb68082a731b192d944e2ede/tutorials_python.zip b/docs/dev/_downloads/315c4c52fb68082a731b192d944e2ede/tutorials_python.zip
diff --git a/docs/dev/_downloads/432ee3a608125ae95df2e8d950f07716/dl3-1.pdf b/docs/dev/_downloads/432ee3a608125ae95df2e8d950f07716/dl3-1.pdf
diff --git a/docs/dev/_downloads/4c5e33d8438fd5229f2e9205d79c47b5/create_region.pdf b/docs/dev/_downloads/4c5e33d8438fd5229f2e9205d79c47b5/create_region.pdf
diff --git a/docs/dev/_downloads/51d6cfa441dab374164568c0a25aafdf/plot_snr_radius_evolution.pdf b/docs/dev/_downloads/51d6cfa441dab374164568c0a25aafdf/plot_snr_radius_evolution.pdf
diff --git a/docs/dev/_downloads/5375f8e9e54dd5a68d37e8a2fc4246b9/plot_spiral_arms.png b/docs/dev/_downloads/5375f8e9e54dd5a68d37e8a2fc4246b9/plot_spiral_arms.png
diff --git a/docs/dev/_downloads/5d24c04675414c5855908367f885d459/extended_source_spectral_analysis.py b/docs/dev/_downloads/5d24c04675414c5855908367f885d459/extended_source_spectral_analysis.py
@@ -150,12 +150,13 @@
 # Define the geometries
 # ~~~~~~~~~~~~~~~~~~~~~
 #
-# This part is especially important. - We have to define first energy
-# axes. They define the axes of the resulting
-# `~gammapy.datasets.SpectrumDatasetOnOff`. In particular, we have to be
-# careful to the true energy axis: it has to cover a larger range than the
-# reconstructed energy one. - Then we define the region geometry itself
-# from the on region.
+# This part is especially important.
+#
+# -  We have to define first energy axes. They define the axes of the resulting
+#    `~gammapy.datasets.SpectrumDatasetOnOff`. In particular, we have to be
+#    careful to the true energy axis: it has to cover a larger range than the
+#    reconstructed energy one.
+# -  Then we define the region geometry itself from the on region.
 #
 
 # The binning of the final spectrum is defined here.
@@ -212,12 +213,12 @@
 # Perform the data reduction loop.
 # --------------------------------
 #
-# We can now run over selected observations. For each of them, we: -
-# create the `~gammapy.datasets.SpectrumDataset` - Compute the OFF via
-# the reflected background method and create a
-# `~gammapy.datasets.SpectrumDatasetOnOff` object - Run the safe mask
-# maker on it - Add the `~gammapy.datasets.SpectrumDatasetOnOff` to the
-# list.
+# We can now run over selected observations. For each of them, we:
+#
+# -   Create the `~gammapy.datasets.SpectrumDataset`
+# -  Compute the OFF via the reflected background method and create a `~gammapy.datasets.SpectrumDatasetOnOff` object
+# -  Run the safe mask maker on it
+# -  Add the `~gammapy.datasets.SpectrumDatasetOnOff` to the list.
 #
 
 # %%time

diff --git a/docs/dev/_downloads/624f67a6339c97aafd7b74d77d3635a5/datasets.ipynb b/docs/dev/_downloads/624f67a6339c97aafd7b74d77d3635a5/datasets.ipynb
@@ -389,7 +389,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "To access the energy range defined by the mask you can use:\n\n-`~gammapy.datasets.MapDataset.energy_range_safe` : energy range defined by the `~gammapy.datasets.MapDataset.mask_safe`\n- `~gammapy.datasets.MapDataset.energy_range_fit` : energy range defined by the `~gammapy.datasets.MapDataset.mask_fit`\n- `~gammapy.datasets.MapDataset.energy_range` : the final energy range used in likelihood computation\n\nThese methods return two maps, with the `min` and `max` energy\nvalues at each spatial pixel\n\n\n"
+        "To access the energy range defined by the mask you can use:\n\n-  `~gammapy.datasets.MapDataset.energy_range_safe` : energy range defined by the `~gammapy.datasets.MapDataset.mask_safe`\n-  `~gammapy.datasets.MapDataset.energy_range_fit` : energy range defined by the `~gammapy.datasets.MapDataset.mask_fit`\n-  `~gammapy.datasets.MapDataset.energy_range` : the final energy range used in likelihood computation\n\nThese methods return two maps, with the `min` and `max` energy\nvalues at each spatial pixel\n\n\n"
       ]
     },
     {
@@ -425,7 +425,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Just as for `~gammapy.maps.Map` objects it is possible to cutout a whole\n`~gammapy.datasets.MapDataset`, which will perform the cutout for all maps in\nparallel.Optionally one can provide a new name to the resulting dataset:\n\n\n"
+        "Just as for `~gammapy.maps.Map` objects it is possible to cutout a whole\n`~gammapy.datasets.MapDataset`, which will perform the cutout for all maps in\nparallel. Optionally one can provide a new name to the resulting dataset:\n\n\n"
       ]
     },
     {
@@ -569,7 +569,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## SpectrumDataset\n\n`~gammapy.datasets.SpectrumDataset` inherits from a `~gammapy.datasets.MapDataset`, and is specially\nadapted for 1D spectral analysis, and uses a `RegionGeom` instead of a\n`WcsGeom`. A `~gammapy.datasets.MapDataset` can be converted to a `~gammapy.datasets.SpectrumDataset`,\nby summing the `counts` and `background` inside the `on_region`,\nwhich can then be used for classical spectral analysis. Containment\ncorrection is feasible only for circular regions.\n\n\n"
+        "## SpectrumDataset\n\n`~gammapy.datasets.SpectrumDataset` inherits from a `~gammapy.datasets.MapDataset`, and is specially\nadapted for 1D spectral analysis, and uses a `~gammapy.maps.RegionGeom` instead of a\n`~gammapy.maps.WcsGeom`. A `~gammapy.datasets.MapDataset` can be converted to a `~gammapy.datasets.SpectrumDataset`,\nby summing the `counts` and `background` inside the `on_region`,\nwhich can then be used for classical spectral analysis. Containment\ncorrection is feasible only for circular regions.\n\n\n"
       ]
     },
     {
@@ -663,14 +663,14 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "For an example of fitting `~gammapy.estimators.FluxPoints`, see :doc:`/tutorials/analysis-1d/sed_fitting`,\nand for using source catalogs see :doc:`/tutorials/api/catalog`\n\n\n"
+        "For an example of fitting `~gammapy.estimators.FluxPoints`, see :doc:`/tutorials/analysis-1d/sed_fitting`,\nand for using source catalogs see :doc:`/tutorials/api/catalog`.\n\n\n"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Datasets\n\n`~gammapy.datasets.Datasets` are a collection of `~gammapy.datasets.Dataset` objects. They can be of the\nsame type, or of different types, eg: mix of `~gammapy.datasets.FluxPointsDataset`,\n`~gammapy.datasets.MapDataset` and `~gammapy.datasets.SpectrumDataset`.\n\nFor modelling and fitting of a list of `~gammapy.datasets.Dataset` objects, you can\neither:\n(a) Do a joint fitting of all the datasets together OR\n(b) Stack the datasets together, and then fit them.\n\n`~gammapy.datasets.Datasets` is a convenient tool to handle joint fitting of\nsimultaneous datasets. As an example, please see :doc:`/tutorials/analysis-3d/analysis_mwl`\n\nTo see how stacking is performed, please see `stack`.\n\nTo create a `~gammapy.datasets.Datasets` object, pass a list of `~gammapy.datasets.Dataset` on init, eg\n\n\n"
+        "## Datasets\n\n`~gammapy.datasets.Datasets` are a collection of `~gammapy.datasets.Dataset` objects. They can be of the\nsame type, or of different types, eg: mix of `~gammapy.datasets.FluxPointsDataset`,\n`~gammapy.datasets.MapDataset` and `~gammapy.datasets.SpectrumDataset`.\n\nFor modelling and fitting of a list of `~gammapy.datasets.Dataset` objects, you can\neither:\n\n-  (A) Do a joint fitting of all the datasets together **OR**\n-  (B) Stack the datasets together, and then fit them.\n\n`~gammapy.datasets.Datasets` is a convenient tool to handle joint fitting of\nsimultaneous datasets. As an example, please see :doc:`/tutorials/analysis-3d/analysis_mwl`.\n\nTo see how stacking is performed, please see `stack`.\n\nTo create a `~gammapy.datasets.Datasets` object, pass a list of `~gammapy.datasets.Dataset` on init, eg\n\n\n"
       ]
     },
     {

diff --git a/docs/dev/_downloads/6cbb580c802d458206b6a0123d909650/energy_dependent_estimation.ipynb b/docs/dev/_downloads/6cbb580c802d458206b6a0123d909650/energy_dependent_estimation.ipynb
@@ -184,7 +184,7 @@
       },
       "outputs": [],
       "source": [
-        "empty_map = Map.create(\n    skydir=spatial_model.position, frame=spatial_model.frame, width=1, binsz=0.02\n)\n\ncolors = [\"red\", \"blue\", \"green\", \"magenta\"]\n\nfig = plt.figure(figsize=(6, 4))\nax = empty_map.plot()\n\nlat_0 = results[\"energy_dependence\"][\"result\"][\"lat_0\"][1:]\nlat_0_err = results[\"energy_dependence\"][\"result\"][\"lat_0_err\"][1:]\nlon_0 = results[\"energy_dependence\"][\"result\"][\"lon_0\"][1:]\nlon_0_err = results[\"energy_dependence\"][\"result\"][\"lon_0_err\"][1:]\nsigma = results[\"energy_dependence\"][\"result\"][\"sigma\"][1:]\nsigma_err = results[\"energy_dependence\"][\"result\"][\"sigma_err\"][1:]\n\nfor i in range(len(lat_0)):\n    model_plot = GaussianSpatialModel(\n        lat_0=lat_0[i], lon_0=lon_0[i], sigma=sigma[i], frame=spatial_model.frame\n    )\n    model_plot.lat_0.error = lat_0_err[i]\n    model_plot.lon_0.error = lon_0_err[i]\n    model_plot.sigma.error = sigma_err[i]\n\n    model_plot.plot_error(\n        ax=ax,\n        which=\"all\",\n        kwargs_extension={\"facecolor\": colors[i], \"edgecolor\": colors[i]},\n        kwargs_position={\"color\": colors[i]},\n    )\nplt.show()\n\n\n# sphinx_gallery_thumbnail_number = 2"
+        "empty_map = Map.create(\n    skydir=spatial_model.position, frame=spatial_model.frame, width=1, binsz=0.02\n)\n\ncolors = [\"red\", \"blue\", \"green\", \"magenta\"]\n\nfig = plt.figure(figsize=(6, 4))\nax = empty_map.plot()\n\nlat_0 = results[\"energy_dependence\"][\"result\"][\"lat_0\"][1:]\nlat_0_err = results[\"energy_dependence\"][\"result\"][\"lat_0_err\"][1:]\nlon_0 = results[\"energy_dependence\"][\"result\"][\"lon_0\"][1:]\nlon_0_err = results[\"energy_dependence\"][\"result\"][\"lon_0_err\"][1:]\nsigma = results[\"energy_dependence\"][\"result\"][\"sigma\"][1:]\nsigma_err = results[\"energy_dependence\"][\"result\"][\"sigma_err\"][1:]\n\nfor i in range(len(lat_0)):\n    model_plot = GaussianSpatialModel(\n        lat_0=lat_0[i], lon_0=lon_0[i], sigma=sigma[i], frame=spatial_model.frame\n    )\n    model_plot.lat_0.error = lat_0_err[i]\n    model_plot.lon_0.error = lon_0_err[i]\n    model_plot.sigma.error = sigma_err[i]\n\n    model_plot.plot_error(\n        ax=ax,\n        which=\"all\",\n        kwargs_extension={\"facecolor\": colors[i], \"edgecolor\": colors[i]},\n        kwargs_position={\"color\": colors[i]},\n    )\nplt.show()\n\n\n# sphinx_gallery_thumbnail_number = 1"
       ]
     }
   ],

diff --git a/docs/dev/_downloads/73e595818811c6987149d60742eef584/make_reflected_regions.pdf b/docs/dev/_downloads/73e595818811c6987149d60742eef584/make_reflected_regions.pdf
diff --git a/...dev/_downloads/73e7de11148f573eeb51f279652f7b9b/make_rectangular_reflected_background.pdf b/...dev/_downloads/73e7de11148f573eeb51f279652f7b9b/make_rectangular_reflected_background.pdf
diff --git a/docs/dev/_downloads/74468ce448910c5a873be0e51cee13fb/plot_pwn_evolution.pdf b/docs/dev/_downloads/74468ce448910c5a873be0e51cee13fb/plot_pwn_evolution.pdf
diff --git a/docs/dev/_downloads/800707065c222d9fe3c88a38845901ae/regionmap-2.pdf b/docs/dev/_downloads/800707065c222d9fe3c88a38845901ae/regionmap-2.pdf
diff --git a/docs/dev/_downloads/83da3445f53cc2258537f3ea6b2b0970/regionmap-5.pdf b/docs/dev/_downloads/83da3445f53cc2258537f3ea6b2b0970/regionmap-5.pdf
diff --git a/docs/dev/_downloads/84424d59ec624940199b69d87a792d50/index-1.pdf b/docs/dev/_downloads/84424d59ec624940199b69d87a792d50/index-1.pdf
diff --git a/...downloads/8a9ae62d4481bef2294fabff4afc70cf/gammapy-irf-EnergyDependentMultiGaussPSF-1.pdf b/...downloads/8a9ae62d4481bef2294fabff4afc70cf/gammapy-irf-EnergyDependentMultiGaussPSF-1.pdf
diff --git a/docs/dev/_downloads/8d795d04d6c1419b268a0a4676041f36/model-gallery_python.zip b/docs/dev/_downloads/8d795d04d6c1419b268a0a4676041f36/model-gallery_python.zip
diff --git a/docs/dev/_downloads/8f7cba6e147ac68586bc14a007ba4cc5/plot_spiral_arms.pdf b/docs/dev/_downloads/8f7cba6e147ac68586bc14a007ba4cc5/plot_spiral_arms.pdf
diff --git a/docs/dev/_downloads/90e01209a5243d5c972882c04584c8e3/plot_aeff_param.pdf b/docs/dev/_downloads/90e01209a5243d5c972882c04584c8e3/plot_aeff_param.pdf
diff --git a/docs/dev/_downloads/9784111b1a9a5c8f07b727039a6e8b82/plot_radial_distributions.pdf b/docs/dev/_downloads/9784111b1a9a5c8f07b727039a6e8b82/plot_radial_distributions.pdf
diff --git a/docs/dev/_downloads/a5659940aa3f8f568547d47752a43172/tutorials_jupyter.zip b/docs/dev/_downloads/a5659940aa3f8f568547d47752a43172/tutorials_jupyter.zip
diff --git a/docs/dev/_downloads/a5b2ee0329e727a4750cc6807854a799/plot_snr_brightness_evolution.pdf b/docs/dev/_downloads/a5b2ee0329e727a4750cc6807854a799/plot_snr_brightness_evolution.pdf
diff --git a/docs/dev/_downloads/a60ac3367f6b8b4daf62f7767737c935/regionmap-3.pdf b/docs/dev/_downloads/a60ac3367f6b8b4daf62f7767737c935/regionmap-3.pdf
diff --git a/docs/dev/_downloads/a647e703f1c14b3b2be137109a402268/plot_wstat_significance.pdf b/docs/dev/_downloads/a647e703f1c14b3b2be137109a402268/plot_wstat_significance.pdf
diff --git a/docs/dev/_downloads/bfccf196c2d1f29042d7c4be0b7abe79/extended_source_spectral_analysis.ipynb b/docs/dev/_downloads/bfccf196c2d1f29042d7c4be0b7abe79/extended_source_spectral_analysis.ipynb
@@ -83,7 +83,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Define the geometries\n\nThis part is especially important. - We have to define first energy\naxes. They define the axes of the resulting\n`~gammapy.datasets.SpectrumDatasetOnOff`. In particular, we have to be\ncareful to the true energy axis: it has to cover a larger range than the\nreconstructed energy one. - Then we define the region geometry itself\nfrom the on region.\n\n\n"
+        "### Define the geometries\n\nThis part is especially important.\n\n-  We have to define first energy axes. They define the axes of the resulting\n   `~gammapy.datasets.SpectrumDatasetOnOff`. In particular, we have to be\n   careful to the true energy axis: it has to cover a larger range than the\n   reconstructed energy one.\n-  Then we define the region geometry itself from the on region.\n\n\n"
       ]
     },
     {
@@ -155,7 +155,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Perform the data reduction loop.\n\nWe can now run over selected observations. For each of them, we: -\ncreate the `~gammapy.datasets.SpectrumDataset` - Compute the OFF via\nthe reflected background method and create a\n`~gammapy.datasets.SpectrumDatasetOnOff` object - Run the safe mask\nmaker on it - Add the `~gammapy.datasets.SpectrumDatasetOnOff` to the\nlist.\n\n\n"
+        "## Perform the data reduction loop.\n\nWe can now run over selected observations. For each of them, we:\n\n-   Create the `~gammapy.datasets.SpectrumDataset`\n-  Compute the OFF via the reflected background method and create a `~gammapy.datasets.SpectrumDatasetOnOff` object\n-  Run the safe mask maker on it\n-  Add the `~gammapy.datasets.SpectrumDatasetOnOff` to the list.\n\n\n"
       ]
     },
     {

diff --git a/docs/dev/_downloads/bfe6aa14bdebe4f65370b05f400755e5/plot_cash_significance.pdf b/docs/dev/_downloads/bfe6aa14bdebe4f65370b05f400755e5/plot_cash_significance.pdf
diff --git a/docs/dev/_downloads/c2775618fc6e3f5a7b28e723a9132470/plot_aeff.pdf b/docs/dev/_downloads/c2775618fc6e3f5a7b28e723a9132470/plot_aeff.pdf
diff --git a/docs/dev/_downloads/c2b61900ea448b3f1bbacb5848c7ae6e/plot_pulsar_spindown.pdf b/docs/dev/_downloads/c2b61900ea448b3f1bbacb5848c7ae6e/plot_pulsar_spindown.pdf
diff --git a/docs/dev/_downloads/c6bad13b7bd034ce07497e4d5025d5d2/edisp-1.pdf b/docs/dev/_downloads/c6bad13b7bd034ce07497e4d5025d5d2/edisp-1.pdf
diff --git a/docs/dev/_downloads/c8dacdfbdf94c97cce7a5ee9555295b5/plot_cash_errors.pdf b/docs/dev/_downloads/c8dacdfbdf94c97cce7a5ee9555295b5/plot_cash_errors.pdf
diff --git a/docs/dev/_downloads/ccdc06d762dc1d0b38c832e8f42c4b5b/fitting.ipynb b/docs/dev/_downloads/ccdc06d762dc1d0b38c832e8f42c4b5b/fitting.ipynb
@@ -321,14 +321,14 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Confidence contours\n\nIn most studies, one wishes to estimate parameters distribution using\nobserved sample data. A 1-dimensional confidence interval gives an\nestimated range of values which is likely to include an unknown\nparameter. A confidence contour is a 2-dimensional generalization of a\nconfidence interval, often represented as an ellipsoid around the\nbest-fit value.\n\nGammapy offers two ways of computing confidence contours, in the\ndedicated methods `~gammapy.modeling.Fit.minos_contour` and `~gammapy.modeling.Fit.stat_profile`. In\nthe following sections we will describe them.\n\n\n"
+        "## Confidence contours\n\nIn most studies, one wishes to estimate parameters distribution using\nobserved sample data. A 1-dimensional confidence interval gives an\nestimated range of values which is likely to include an unknown\nparameter. A confidence contour is a 2-dimensional generalization of a\nconfidence interval, often represented as an ellipsoid around the\nbest-fit value.\n\nGammapy offers two ways of computing confidence contours, in the\ndedicated methods `~gammapy.modeling.Fit.stat_contour` and `~gammapy.modeling.Fit.stat_profile`. In\nthe following sections we will describe them.\n\n\n"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "An important point to keep in mind is: *what does a $N\\sigma$\nconfidence contour really mean?* The answer is it represents the points\nof the parameter space for which the model likelihood is $N\\sigma$\nabove the minimum. But one always has to keep in mind that **1 standard\ndeviation in two dimensions has a smaller coverage probability than\n68%**, and similarly for all other levels. In particular, in\n2-dimensions the probability enclosed by the $N\\sigma$ confidence\ncontour is $P(N)=1-e^{-N^2/2}$.\n\n\n"
+        "An important point to keep in mind is: *what does a* $N\\sigma$\n*confidence contour really mean?* The answer is it represents the points\nof the parameter space for which the model likelihood is $N\\sigma$\nabove the minimum. But one always has to keep in mind that **1 standard\ndeviation in two dimensions has a smaller coverage probability than\n68%**, and similarly for all other levels. In particular, in\n2-dimensions the probability enclosed by the $N\\sigma$ confidence\ncontour is $P(N)=1-e^{-N^2/2}$.\n\n\n"
       ]
     },
     {
@@ -407,7 +407,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Computing contours using `~gammapy.modeling.Fit.stat_surface`\n\nThis alternative method for the computation of confidence contours,\nalthough more time consuming than `~gammapy.modeling.Fit.minos_contour()`, is expected\nto be more stable. It consists of a generalization of\n`~gammapy.modeling.Fit.stat_profile()` to a 2-dimensional parameter space. The algorithm\nis very simple: - First, passing two arrays of parameters values, a\n2-dimensional discrete parameter space is defined; - For each node of\nthe parameter space, the two parameters of interest are frozen. This\nway, a likelihood value ($-2\\mathrm{ln}\\,\\mathcal{L}$, actually)\nis computed, by either freezing (default) or fitting all nuisance\nparameters; - Finally, a 2-dimensional surface of\n$-2\\mathrm{ln}(\\mathcal{L})$ values is returned. Using that\nsurface, one can easily compute a surface of\n$TS = -2\\Delta\\mathrm{ln}(\\mathcal{L})$ and compute confidence\ncontours.\n\nLet\u2019s see it step by step.\n\nFirst of all, we can notice that this method is \u201cbackend-agnostic\u201d,\nmeaning that it can be run with MINUIT, sherpa or scipy as fitting\ntools. Here we will stick with MINUIT, which is the default choice:\n\nAs an example, we can compute the confidence contour for the `alpha`\nand `beta` parameters of the `dataset_hess`. Here we define the\nparameter space:\n\n\n"
+        "### Computing contours using `~gammapy.modeling.Fit.stat_surface`\n\nThis alternative method for the computation of confidence contours,\nalthough more time consuming than `~gammapy.modeling.Fit.stat_contour()`, is expected\nto be more stable. It consists of a generalization of\n`~gammapy.modeling.Fit.stat_profile()` to a 2-dimensional parameter space. The algorithm\nis very simple: - First, passing two arrays of parameters values, a\n2-dimensional discrete parameter space is defined; - For each node of\nthe parameter space, the two parameters of interest are frozen. This\nway, a likelihood value ($-2\\mathrm{ln}\\,\\mathcal{L}$, actually)\nis computed, by either freezing (default) or fitting all nuisance\nparameters; - Finally, a 2-dimensional surface of\n$-2\\mathrm{ln}(\\mathcal{L})$ values is returned. Using that\nsurface, one can easily compute a surface of\n$TS = -2\\Delta\\mathrm{ln}(\\mathcal{L})$ and compute confidence\ncontours.\n\nLet\u2019s see it step by step.\n\nFirst of all, we can notice that this method is \u201cbackend-agnostic\u201d,\nmeaning that it can be run with MINUIT, sherpa or scipy as fitting\ntools. Here we will stick with MINUIT, which is the default choice:\n\nAs an example, we can compute the confidence contour for the `alpha`\nand `beta` parameters of the `dataset_hess`. Here we define the\nparameter space:\n\n\n"
       ]
     },
     {

diff --git a/docs/dev/_downloads/d46f4cc1fb54f9148150a46cabce732a/plot_spiral_arm_models.pdf b/docs/dev/_downloads/d46f4cc1fb54f9148150a46cabce732a/plot_spiral_arm_models.pdf
diff --git a/docs/dev/_downloads/d47b4b3bccd5fc1699bbc9406a3c4035/plot_edisp.pdf b/docs/dev/_downloads/d47b4b3bccd5fc1699bbc9406a3c4035/plot_edisp.pdf
Original file line number	Diff line number	Diff line change
Expand Up		@@ -247,4 +247,4 @@
		plt.show()


		# sphinx_gallery_thumbnail_number = 2
		# sphinx_gallery_thumbnail_number = 1