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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: 279c2a4860a6fd15fbc7251e29429d21
+config: 3875ff32a449deddace46e69c7c93246
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diff --git a/docs/dev/_downloads/00d8cba1967017b728f6c0777b24e13f/plot_exp_cutoff_powerlaw.zip b/docs/dev/_downloads/00d8cba1967017b728f6c0777b24e13f/plot_exp_cutoff_powerlaw.zip
diff --git a/docs/dev/_downloads/00e16d4c09559d67d10cd2e55c12eff8/plot_template_spectral.zip b/docs/dev/_downloads/00e16d4c09559d67d10cd2e55c12eff8/plot_template_spectral.zip
diff --git a/docs/dev/_downloads/041be2d7422101460b6f928b074608da/light_curve_flare.zip b/docs/dev/_downloads/041be2d7422101460b6f928b074608da/light_curve_flare.zip
diff --git a/docs/dev/_downloads/04f3ecb20bae1e7983af36784e65887d/plot_super_exp_cutoff_powerlaw_3fgl.zip b/docs/dev/_downloads/04f3ecb20bae1e7983af36784e65887d/plot_super_exp_cutoff_powerlaw_3fgl.zip
diff --git a/docs/dev/_downloads/0595e7d3ba49081161c8f9931701216b/sed_fitting.zip b/docs/dev/_downloads/0595e7d3ba49081161c8f9931701216b/sed_fitting.zip
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/06ba2363ac6d8e23d2098c691bbc6e58/priors.zip b/docs/dev/_downloads/06ba2363ac6d8e23d2098c691bbc6e58/priors.zip
diff --git a/docs/dev/_downloads/088ccc2817ab643e12f4e39275459a14/simulate_3d.zip b/docs/dev/_downloads/088ccc2817ab643e12f4e39275459a14/simulate_3d.zip
diff --git a/docs/dev/_downloads/0c4afa10f7306271310df66616417cf8/spectral_analysis.zip b/docs/dev/_downloads/0c4afa10f7306271310df66616417cf8/spectral_analysis.zip
diff --git a/docs/dev/_downloads/0ee9d6f1024a6949ed84ed2823f76884/plot_constant_temporal.zip b/docs/dev/_downloads/0ee9d6f1024a6949ed84ed2823f76884/plot_constant_temporal.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/1370bd9099a95d65461f2a665bb9decd/catalog.zip b/docs/dev/_downloads/1370bd9099a95d65461f2a665bb9decd/catalog.zip
diff --git a/docs/dev/_downloads/16d6399e308f3f4937d0512c9c9dd023/plot_psf.pdf b/docs/dev/_downloads/16d6399e308f3f4937d0512c9c9dd023/plot_psf.pdf
diff --git a/docs/dev/_downloads/173b2147fa2a0402fd092316ab70a935/plot_smooth_broken_powerlaw.zip b/docs/dev/_downloads/173b2147fa2a0402fd092316ab70a935/plot_smooth_broken_powerlaw.zip
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/1d3f6beaf08c01b5183c9c9291d0b28a/plot_piecewise_norm_spectral.zip b/docs/dev/_downloads/1d3f6beaf08c01b5183c9c9291d0b28a/plot_piecewise_norm_spectral.zip
diff --git a/docs/dev/_downloads/21c6356dca773bb211972068bdd8edac/irfs.zip b/docs/dev/_downloads/21c6356dca773bb211972068bdd8edac/irfs.zip
diff --git a/docs/dev/_downloads/23a95fa742150b45b73b7f187c1cc8a8/plot_broken_powerlaw.zip b/docs/dev/_downloads/23a95fa742150b45b73b7f187c1cc8a8/plot_broken_powerlaw.zip
diff --git a/docs/dev/_downloads/24b4892c12e81f49f318d6c878184d3f/light_curve_flare.py b/docs/dev/_downloads/24b4892c12e81f49f318d6c878184d3f/light_curve_flare.py
@@ -86,6 +86,7 @@
 )
 from gammapy.maps import MapAxis, RegionGeom
 from gammapy.modeling.models import PowerLawSpectralModel, SkyModel
+from gammapy.modeling import Fit
 
 ######################################################################
 # Check setup
@@ -126,9 +127,8 @@
 # Define time intervals
 # ---------------------
 #
-# We create the list of time intervals. Each time interval is an
+# We create the list of time intervals, each of duration 10 minutes. Each time interval is an
 # `astropy.time.Time` object, containing a start and stop time.
-#
 
 t0 = Time("2006-07-29T20:30")
 duration = 10 * u.min
@@ -236,26 +236,21 @@
 
 
 ######################################################################
-# Define the Model
-# ----------------
+# Define underlying model
+# -----------------------
 #
-# The actual flux will depend on the spectral shape assumed. For
-# simplicity, we use the power law spectral model of index 3.4 used in the
+# Since we use forward folding to obtain the flux points in each bin, exact values will depend on the underlying model. In this example, we use a power law as used in the
 # `reference
 # paper <https://ui.adsabs.harvard.edu/abs/2009A%26A...502..749A/abstract>`__.
 #
-# Here we use only a spectral model in the
-# `~gammapy.modeling.models.SkyModel` object.
+# As we have are only using spectral datasets, we do not need any spatial models.
 #
+# **Note** : All time bins must have the same spectral model. To see how to investigate spectral variability,
+# see :doc:`time resolved spectroscopy notebook </tutorials/analysis-time/time_resolved_spectroscopy>`.
 
-spectral_model = PowerLawSpectralModel(
-    index=3.4, amplitude=2e-11 * u.Unit("1 / (cm2 s TeV)"), reference=1 * u.TeV
-)
-spectral_model.parameters["index"].frozen = False
-
+spectral_model = PowerLawSpectralModel(amplitude=1e-10 * u.Unit("1 / (cm2 s TeV)"))
 sky_model = SkyModel(spatial_model=None, spectral_model=spectral_model, name="pks2155")
 
-
 ######################################################################
 # Assign to model to all datasets
 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -266,22 +261,30 @@
 datasets.models = sky_model
 
 
+# %%time
+fit = Fit()
+result = fit.run(datasets)
+print(result.models.to_parameters_table())
+
 ######################################################################
 # Extract the light curve
 # -----------------------
 #
 # We first create the `~gammapy.estimators.LightCurveEstimator` for the
 # list of datasets we just produced. We give the estimator the name of the
-# source component to be fitted.
+# source component to be fitted. We can directly compute the light curve in multiple energy
+# bins by supplying a list of `energy_edges`.
+#
 # By default the likelihood scan is computed from 0.2 to 5.0.
 # Here, we increase the max value to 10.0, because we are
 # dealing with a large flare.
 
 lc_maker_1d = LightCurveEstimator(
-    energy_edges=[0.7, 20] * u.TeV,
+    energy_edges=[0.7, 1, 20] * u.TeV,
     source="pks2155",
     time_intervals=time_intervals,
     selection_optional="all",
+    n_jobs=4,
 )
 lc_maker_1d.norm.scan_max = 10
 
@@ -301,21 +304,37 @@
 # Finally we plot the result for the 1D lightcurve:
 #
 plt.figure(figsize=(8, 6))
-lc_1d.plot(marker="o")
+plt.tight_layout()
+plt.subplots_adjust(bottom=0.3)
+lc_1d.plot(marker="o", axis_name="time", sed_type="flux")
 plt.show()
 
 
 ######################################################################
-# Light curves once obtained can be rebinned.
-# Here, we rebin 4 adjacent bins together, to get 30 min bins
+# Light curves once obtained can be rebinned using the likelihood profiles.
+# Here, we rebin 3 adjacent bins together, to get 30 minute bins.
+#
+# We will first slice `lc_1d` to obtain the lightcurve in the first energy bin
 #
 
-axis_new = get_rebinned_axis(lc_1d, method="fixed-bins", group_size=3, axis_name="time")
+slices = {"energy": slice(0, 1)}
+sliced_lc = lc_1d.slice_by_idx(slices)
+print(sliced_lc)
+
+axis_new = get_rebinned_axis(
+    sliced_lc, method="fixed-bins", group_size=3, axis_name="time"
+)
 print(axis_new)
 
-lc_new = lc_1d.resample_axis(axis_new)
+lc_new = sliced_lc.resample_axis(axis_new)
 plt.figure(figsize=(8, 6))
-ax = lc_1d.plot(label="original")
+plt.tight_layout()
+plt.subplots_adjust(bottom=0.3)
+ax = sliced_lc.plot(label="original")
 lc_new.plot(ax=ax, label="rebinned")
 plt.legend()
-plt.show()
+plt.show()
+
+#####################################################################
+# We can use the sliced lightcurve to understand the variability,
+# as shown in the doc:`/tutorials/analysis-time/variability_estimation` tutorial.
diff --git a/docs/dev/_downloads/25f9c102794e0966d636f8ca130ecabc/extended_source_spectral_analysis.zip b/docs/dev/_downloads/25f9c102794e0966d636f8ca130ecabc/extended_source_spectral_analysis.zip
diff --git a/docs/dev/_downloads/26224d3995e07325df7f0b64d1681fb9/regionmap-1.pdf b/docs/dev/_downloads/26224d3995e07325df7f0b64d1681fb9/regionmap-1.pdf
diff --git a/docs/dev/_downloads/26b47ebfce2659a40c59a7d3296b8d9a/estimators.ipynb b/docs/dev/_downloads/26b47ebfce2659a40c59a7d3296b8d9a/estimators.ipynb
@@ -123,14 +123,14 @@
       },
       "outputs": [],
       "source": [
-        "fp_estimator.norm.scan_min = 0.1\nfp_estimator.norm.scan_max = 3"
+        "fp_estimator.norm.scan_min = 0.1\nfp_estimator.norm.scan_max = 10"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Note: The default scan range of the norm parameter is between 0.1 to 10. In case the upper\nlimit values lie outside this range, nan values will be returned. It may thus be useful to\nincrease this range, specially for the computation of upper limits from weak sources.\n\nThe various quantities utilised in this tutorial are described here:\n\n-  ``source``: which source from the model to compute the flux points for\n-  ``energy_edges``: edges of the flux points energy bins\n-  ``n_sigma``: number of sigma for the flux error\n-  ``n_sigma_ul``: the number of sigma for the flux upper limits\n-  ``selection_optional``: what additional maps to compute\n-  ``fit``: the fit instance (as defined above)\n-  ``reoptimize``: whether to reoptimize the flux points with other model parameters, aside from the ``norm``\n-  ``norm``: normalisation parameter for the fit\n\n**Important note**: the output ``energy_edges`` are taken from the parent dataset energy bins,\nselecting the bins closest to the requested ``energy_edges``. To match the input bins directly,\nspecific binning must be defined based on the parent dataset geometry. This could be done in the following way:\n`energy_edges = datasets[0].geoms[\"geom\"].axes[\"energy\"].downsample(factor=5).edges`\n\n\n"
+        "Note: The default scan range of the norm parameter is between 0.2 to 5. In case the upper\nlimit values lie outside this range, nan values will be returned. It may thus be useful to\nincrease this range, specially for the computation of upper limits from weak sources.\n\nThe various quantities utilised in this tutorial are described here:\n\n-  ``source``: which source from the model to compute the flux points for\n-  ``energy_edges``: edges of the flux points energy bins\n-  ``n_sigma``: number of sigma for the flux error\n-  ``n_sigma_ul``: the number of sigma for the flux upper limits\n-  ``selection_optional``: what additional maps to compute\n-  ``fit``: the fit instance (as defined above)\n-  ``reoptimize``: whether to reoptimize the flux points with other model parameters, aside from the ``norm``\n-  ``norm``: normalisation parameter for the fit\n\n**Important note**: the output ``energy_edges`` are taken from the parent dataset energy bins,\nselecting the bins closest to the requested ``energy_edges``. To match the input bins directly,\nspecific binning must be defined based on the parent dataset geometry. This could be done in the following way:\n`energy_edges = datasets[0].geoms[\"geom\"].axes[\"energy\"].downsample(factor=5).edges`\n\n\n"
       ]
     },
     {

diff --git a/docs/dev/_downloads/277e8d586c77a3ac9f6ad21ecdfc3485/detect.zip b/docs/dev/_downloads/277e8d586c77a3ac9f6ad21ecdfc3485/detect.zip
diff --git a/docs/dev/_downloads/296ebb35e9dd7deb47d7997d9fed9214/event_sampling.zip b/docs/dev/_downloads/296ebb35e9dd7deb47d7997d9fed9214/event_sampling.zip
diff --git a/docs/dev/_downloads/315c4c52fb68082a731b192d944e2ede/tutorials_python.zip b/docs/dev/_downloads/315c4c52fb68082a731b192d944e2ede/tutorials_python.zip
diff --git a/docs/dev/_downloads/33bb560bd3c93b9dd08e3984c7eb1b58/plot_disk.zip b/docs/dev/_downloads/33bb560bd3c93b9dd08e3984c7eb1b58/plot_disk.zip
diff --git a/docs/dev/_downloads/341317315e7e4253fe9cdaa5bbc2c6e2/plot_constant.zip b/docs/dev/_downloads/341317315e7e4253fe9cdaa5bbc2c6e2/plot_constant.zip
diff --git a/docs/dev/_downloads/346fe536153ddf4e1f253ab559e610d4/survey_map.zip b/docs/dev/_downloads/346fe536153ddf4e1f253ab559e610d4/survey_map.zip
diff --git a/docs/dev/_downloads/3887251ea091cf5a495cbd0984520eec/hawc.zip b/docs/dev/_downloads/3887251ea091cf5a495cbd0984520eec/hawc.zip
diff --git a/docs/dev/_downloads/388f0fd944eebe1e0103655d9014c555/plot_gauss.zip b/docs/dev/_downloads/388f0fd944eebe1e0103655d9014c555/plot_gauss.zip
diff --git a/docs/dev/_downloads/39983d17a2a11f56c5f5cc42710364f2/analysis_1.zip b/docs/dev/_downloads/39983d17a2a11f56c5f5cc42710364f2/analysis_1.zip
diff --git a/docs/dev/_downloads/3b10ba4ac6696d908bde0bbc7a89c9d7/plot_powerlaw_temporal.zip b/docs/dev/_downloads/3b10ba4ac6696d908bde0bbc7a89c9d7/plot_powerlaw_temporal.zip
diff --git a/docs/dev/_downloads/3edea76cb3aefe6dfce230edb07b24e2/models.zip b/docs/dev/_downloads/3edea76cb3aefe6dfce230edb07b24e2/models.zip
diff --git a/docs/dev/_downloads/408398be7d2e8fce69b0c175dfb7db6c/hess.zip b/docs/dev/_downloads/408398be7d2e8fce69b0c175dfb7db6c/hess.zip
diff --git a/docs/dev/_downloads/42884adaf5dec1438e6da837d7acfad0/cta.zip b/docs/dev/_downloads/42884adaf5dec1438e6da837d7acfad0/cta.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/440149f3931d426b0d9c554d4bb23c5b/light_curve_simulation.zip b/docs/dev/_downloads/440149f3931d426b0d9c554d4bb23c5b/light_curve_simulation.zip
diff --git a/docs/dev/_downloads/440e264b73eac3e4291763a82299dcb1/model_management.zip b/docs/dev/_downloads/440e264b73eac3e4291763a82299dcb1/model_management.zip
diff --git a/docs/dev/_downloads/471a23ea48282aca3ed18c0dab2c428f/plot_shell2.zip b/docs/dev/_downloads/471a23ea48282aca3ed18c0dab2c428f/plot_shell2.zip
diff --git a/docs/dev/_downloads/4722787c7fa105f0833ba203665f472d/maps.zip b/docs/dev/_downloads/4722787c7fa105f0833ba203665f472d/maps.zip
diff --git a/docs/dev/_downloads/486b9bbab34388e381bccada3a10c772/fermi_lat.py b/docs/dev/_downloads/486b9bbab34388e381bccada3a10c772/fermi_lat.py
@@ -309,7 +309,7 @@
 
 ######################################################################
 # To get an idea of the size of the PSF we check how the containment radii
-# of the Fermi-LAT PSF vari with energy and different containment
+# of the Fermi-LAT PSF vary with energy and different containment
 # fractions:
 #
 

diff --git a/docs/dev/_downloads/4c5e33d8438fd5229f2e9205d79c47b5/create_region.pdf b/docs/dev/_downloads/4c5e33d8438fd5229f2e9205d79c47b5/create_region.pdf
diff --git a/docs/dev/_downloads/4ea9a264083bf99663b11b14fd6b5368/plot_constant_spectral.zip b/docs/dev/_downloads/4ea9a264083bf99663b11b14fd6b5368/plot_constant_spectral.zip
diff --git a/...ev/_downloads/4f3c47ff11347e8788ce002c9c32adc3/plot_exp_cutoff_powerlaw_norm_spectral.zip b/...ev/_downloads/4f3c47ff11347e8788ce002c9c32adc3/plot_exp_cutoff_powerlaw_norm_spectral.zip
diff --git a/docs/dev/_downloads/5195feb3ff54e1188c53522657e57ed1/variability_estimation.zip b/docs/dev/_downloads/5195feb3ff54e1188c53522657e57ed1/variability_estimation.zip
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/52215788338df5a5cca4ad9f70681d84/cta.py b/docs/dev/_downloads/52215788338df5a5cca4ad9f70681d84/cta.py
@@ -280,6 +280,7 @@
 # %%
 # This is how for analysis you could slice out the PSF
 # at a given field of view offset
+plt.figure(figsize=(8, 5))
 irfs["psf"].plot_containment_radius_vs_energy(
     offset=[1] * u.deg, fraction=[0.68, 0.8, 0.95]
 )

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/5d70733460edff98657b0fd5d0eb69e4/modeling_2D.zip b/docs/dev/_downloads/5d70733460edff98657b0fd5d0eb69e4/modeling_2D.zip
diff --git a/docs/dev/_downloads/5f7885af4d03116bd8091cb6252eae48/analysis_3d.zip b/docs/dev/_downloads/5f7885af4d03116bd8091cb6252eae48/analysis_3d.zip
diff --git a/docs/dev/_downloads/60399e3ce9aa0bfe3b7ca8f286fad1ce/plot_powerlaw_norm_spectral.zip b/docs/dev/_downloads/60399e3ce9aa0bfe3b7ca8f286fad1ce/plot_powerlaw_norm_spectral.zip
diff --git a/docs/dev/_downloads/66ad9a060e067da15dee5cf2536ae998/spectral_analysis_rad_max.zip b/docs/dev/_downloads/66ad9a060e067da15dee5cf2536ae998/spectral_analysis_rad_max.zip
diff --git a/docs/dev/_downloads/682e4483582d9fdb778dbd09a795d104/plot_compound.zip b/docs/dev/_downloads/682e4483582d9fdb778dbd09a795d104/plot_compound.zip
diff --git a/docs/dev/_downloads/692bf70e2aa9caeb987bbb35050f71d2/astro_dark_matter.zip b/docs/dev/_downloads/692bf70e2aa9caeb987bbb35050f71d2/astro_dark_matter.zip
diff --git a/docs/dev/_downloads/6e47fca124ba94afacd2e1f76cf61081/plot_template_phase_temporal.zip b/docs/dev/_downloads/6e47fca124ba94afacd2e1f76cf61081/plot_template_phase_temporal.zip
diff --git a/docs/dev/_downloads/70db13717c83b8e636f99ad0018eeb90/plot_template.zip b/docs/dev/_downloads/70db13717c83b8e636f99ad0018eeb90/plot_template.zip
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/760eb275a0d8a9d11ea857eb956132f2/plot_naima.zip b/docs/dev/_downloads/760eb275a0d8a9d11ea857eb956132f2/plot_naima.zip
diff --git a/docs/dev/_downloads/7757318506e3a6721d12df18db780f37/plot_sine_temporal.zip b/docs/dev/_downloads/7757318506e3a6721d12df18db780f37/plot_sine_temporal.zip
diff --git a/docs/dev/_downloads/7f0f5ef8ba9bdfd9da1248f73ad010fe/overview.zip b/docs/dev/_downloads/7f0f5ef8ba9bdfd9da1248f73ad010fe/overview.zip
diff --git a/docs/dev/_downloads/800707065c222d9fe3c88a38845901ae/regionmap-2.pdf b/docs/dev/_downloads/800707065c222d9fe3c88a38845901ae/regionmap-2.pdf
diff --git a/docs/dev/_downloads/82a17c692c56b9472c842596eac69717/ring_background.ipynb b/docs/dev/_downloads/82a17c692c56b9472c842596eac69717/ring_background.ipynb
@@ -22,7 +22,7 @@
       },
       "outputs": [],
       "source": [
-        "import logging\n\n# %matplotlib inline\nimport astropy.units as u\nfrom astropy.coordinates import SkyCoord\nfrom regions import CircleSkyRegion\nimport matplotlib.pyplot as plt\nfrom gammapy.analysis import Analysis, AnalysisConfig\nfrom gammapy.datasets import MapDatasetOnOff\nfrom gammapy.estimators import ExcessMapEstimator\nfrom gammapy.makers import RingBackgroundMaker\nfrom gammapy.visualization import plot_distribution\nfrom gammapy.utils.check import check_tutorials_setup\nlog = logging.getLogger(__name__)"
+        "import logging\n\n# %matplotlib inline\nimport astropy.units as u\nfrom astropy.coordinates import SkyCoord\nfrom regions import CircleSkyRegion\nimport matplotlib.pyplot as plt\nfrom gammapy.analysis import Analysis, AnalysisConfig\nfrom gammapy.datasets import MapDatasetOnOff\nfrom gammapy.estimators import ExcessMapEstimator\nfrom gammapy.makers import RingBackgroundMaker\nfrom gammapy.visualization import plot_distribution\nfrom gammapy.utils.check import check_tutorials_setup\n\nlog = logging.getLogger(__name__)"
       ]
     },
     {
@@ -58,7 +58,7 @@
       },
       "outputs": [],
       "source": [
-        "# source_pos = SkyCoord.from_name(\"MSH 15-52\")\nsource_pos = SkyCoord(228.32, -59.08, unit=\"deg\")\n\nconfig = AnalysisConfig()\n# Select observations - 2.5 degrees from the source position\nconfig.observations.datastore = \"$GAMMAPY_DATA/hess-dl3-dr1/\"\nconfig.observations.obs_cone = {\n    \"frame\": \"icrs\",\n    \"lon\": source_pos.ra,\n    \"lat\": source_pos.dec,\n    \"radius\": 2.5 * u.deg,\n}\n\nconfig.datasets.type = \"3d\"\nconfig.datasets.geom.wcs.skydir = {\n    \"lon\": source_pos.ra,\n    \"lat\": source_pos.dec,\n    \"frame\": \"icrs\",\n}  # The WCS geometry - centered on MSH 15-52\nconfig.datasets.geom.wcs.width = {\"width\": \"3 deg\", \"height\": \"3 deg\"}\nconfig.datasets.geom.wcs.binsize = \"0.02 deg\"\n\n# Cutout size (for the run-wise event selection)\nconfig.datasets.geom.selection.offset_max = 3.5 * u.deg\n\n# We now fix the energy axis for the counts map - (the reconstructed energy binning)\nconfig.datasets.geom.axes.energy.min = \"0.5 TeV\"\nconfig.datasets.geom.axes.energy.max = \"5 TeV\"\nconfig.datasets.geom.axes.energy.nbins = 10\n\n# We need to extract the ring for each observation separately, hence, no stacking at this stage\nconfig.datasets.stack = False\n\nprint(config)"
+        "# source_pos = SkyCoord.from_name(\"MSH 15-52\")\nsource_pos = SkyCoord(228.32, -59.08, unit=\"deg\")\n\nconfig = AnalysisConfig()\n# Select observations - 2.5 degrees from the source position\nconfig.observations.datastore = \"$GAMMAPY_DATA/hess-dl3-dr1/\"\nconfig.observations.obs_cone = {\n    \"frame\": \"icrs\",\n    \"lon\": source_pos.ra,\n    \"lat\": source_pos.dec,\n    \"radius\": 2.5 * u.deg,\n}\n\nconfig.datasets.type = \"3d\"\nconfig.datasets.geom.wcs.skydir = {\n    \"lon\": source_pos.ra,\n    \"lat\": source_pos.dec,\n    \"frame\": \"icrs\",\n}  # The WCS geometry - centered on MSH 15-52\nconfig.datasets.geom.wcs.width = {\"width\": \"3 deg\", \"height\": \"3 deg\"}\nconfig.datasets.geom.wcs.binsize = \"0.02 deg\"\n\n# Cutout size (for the run-wise event selection)\nconfig.datasets.geom.selection.offset_max = 2.5 * u.deg\n\n# We now fix the energy axis for the counts map - (the reconstructed energy binning)\nconfig.datasets.geom.axes.energy.min = \"0.5 TeV\"\nconfig.datasets.geom.axes.energy.max = \"10 TeV\"\nconfig.datasets.geom.axes.energy.nbins = 10\n\n# We need to extract the ring for each observation separately, hence, no stacking at this stage\nconfig.datasets.stack = False\n\nprint(config)"
       ]
     },
     {
@@ -126,7 +126,7 @@
       },
       "outputs": [],
       "source": [
-        "# get the geom that we use\ngeom = analysis.datasets[0].counts.geom\nenergy_axis = analysis.datasets[0].counts.geom.axes[\"energy\"]\ngeom_image = geom.to_image().to_cube([energy_axis.squash()])\n\n# Make the exclusion mask\nregions = CircleSkyRegion(center=source_pos, radius=0.3 * u.deg)\nexclusion_mask = ~geom_image.region_mask([regions])\nexclusion_mask.sum_over_axes().plot()\nplt.show()"
+        "# get the geom that we use\ngeom = analysis.datasets[0].counts.geom\nenergy_axis = analysis.datasets[0].counts.geom.axes[\"energy\"]\ngeom_image = geom.to_image().to_cube([energy_axis.squash()])\n\n# Make the exclusion mask\nregions = CircleSkyRegion(center=source_pos, radius=0.4 * u.deg)\nexclusion_mask = ~geom_image.region_mask([regions])\nexclusion_mask.sum_over_axes().plot()\nplt.show()"
       ]
     },
     {
@@ -162,7 +162,18 @@
       },
       "outputs": [],
       "source": [
-        "energy_axis_true = analysis.datasets[0].exposure.geom.axes[\"energy_true\"]\nstacked_on_off = MapDatasetOnOff.create(\n    geom=geom_image, energy_axis_true=energy_axis_true, name=\"stacked\"\n)\n\nfor dataset in analysis.datasets:\n    # Ring extracting makes sense only for 2D analysis\n    dataset_on_off = ring_maker.run(dataset.to_image())\n    stacked_on_off.stack(dataset_on_off)"
+        "energy_axis_true = analysis.datasets[0].exposure.geom.axes[\"energy_true\"]\nstacked_on_off = MapDatasetOnOff.create(\n    geom=geom_image, energy_axis_true=energy_axis_true, name=\"stacked\"\n)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "for dataset in analysis.datasets:\n    # Ring extracting makes sense only for 2D analysis\n    dataset_on_off = ring_maker.run(dataset.to_image())\n    stacked_on_off.stack(dataset_on_off)"
       ]
     },
     {
@@ -187,7 +198,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Compute correlated significance and correlated excess maps\n\nWe need to convolve our maps with an appropriate smoothing kernel. The\nsignificance is computed according to the Li & Ma expression for ON and\nOFF Poisson measurements, see\n[here](https://ui.adsabs.harvard.edu/abs/1983ApJ...272..317L/abstract)_.\nSince astropy convolution kernels only accept integers, we first convert\nour required size in degrees to int depending on our pixel size.\n\n\n"
+        "## Compute correlated significance and correlated excess maps\n\nWe need to convolve our maps with an appropriate smoothing kernel. The\nsignificance is computed according to the Li & Ma expression for ON and\nOFF Poisson measurements, see\n[here](https://ui.adsabs.harvard.edu/abs/1983ApJ...272..317L/abstract)_.\n\n\nAlso, since the off counts are obtained with a ring background estimation, and are thus already correlated, we specify `correlate_off=False`\nto avoid over correlation.\n\n"
       ]
     },
     {
@@ -198,14 +209,14 @@
       },
       "outputs": [],
       "source": [
-        "# Using a convolution radius of 0.04 degrees\nestimator = ExcessMapEstimator(0.04 * u.deg, selection_optional=[])\nlima_maps = estimator.run(stacked_on_off)\n\nsignificance_map = lima_maps[\"sqrt_ts\"]\nexcess_map = lima_maps[\"npred_excess\"]\n\n# We can plot the excess and significance maps\nfig, (ax1, ax2) = plt.subplots(\n    figsize=(11, 4), subplot_kw={\"projection\": lima_maps.geom.wcs}, ncols=2\n)\n\nax1.set_title(\"Significance map\")\nsignificance_map.plot(ax=ax1, add_cbar=True)\n\nax2.set_title(\"Excess map\")\nexcess_map.plot(ax=ax2, add_cbar=True)\nplt.show()"
+        "# Using a convolution radius of 0.04 degrees\nestimator = ExcessMapEstimator(0.04 * u.deg, selection_optional=[], correlate_off=False)\nlima_maps = estimator.run(stacked_on_off)\n\nsignificance_map = lima_maps[\"sqrt_ts\"]\nexcess_map = lima_maps[\"npred_excess\"]\n\n# We can plot the excess and significance maps\nfig, (ax1, ax2) = plt.subplots(\n    figsize=(11, 4), subplot_kw={\"projection\": lima_maps.geom.wcs}, ncols=2\n)\nax1.set_title(\"Significance map\")\nsignificance_map.plot(ax=ax1, add_cbar=True)\nax2.set_title(\"Excess map\")\nexcess_map.plot(ax=ax2, add_cbar=True)\nplt.show()"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "It is often important to look at the significance distribution outside\nthe exclusion region to check that the background estimation is not\ncontaminated by gamma-ray events. This can be the case when exclusion\nregions are not large enough. Typically, we expect the off distribution\nto be a standard normal distribution.\n\n\n"
+        "It is often important to look at the significance distribution outside\nthe exclusion region to check that the background estimation is not\ncontaminated by gamma-ray events. This can be the case when exclusion\nregions are not large enough. Typically, we expect the off distribution\nto be a standard normal distribution. To compute the significance distribution outside the exclusion region,\nwe can recompute the maps after adding a `mask_fit` to our dataset.\n\n\n"
       ]
     },
     {
@@ -216,7 +227,7 @@
       },
       "outputs": [],
       "source": [
-        "# create a 2D mask for the images\nsignificance_map_off = significance_map * exclusion_mask\n\nkwargs_axes = {\"xlabel\": \"Significance\", \"yscale\": \"log\", \"ylim\": (1e-5, 1)}\n\nax, _ = plot_distribution(\n    significance_map,\n    kwargs_hist={\n        \"density\": True,\n        \"alpha\": 0.5,\n        \"color\": \"red\",\n        \"label\": \"all bins\",\n        \"bins\": 21,\n    },\n    kwargs_axes=kwargs_axes,\n)\n\nax, res = plot_distribution(\n    significance_map_off,\n    ax=ax,\n    func=\"norm\",\n    kwargs_hist={\n        \"density\": True,\n        \"alpha\": 0.5,\n        \"color\": \"blue\",\n        \"label\": \"off bins\",\n        \"bins\": 21,\n    },\n    kwargs_axes=kwargs_axes,\n)\n\nplt.show()\n\n# sphinx_gallery_thumbnail_number = 2"
+        "# Mask the regions with gamma-ray emission\nstacked_on_off.mask_fit = exclusion_mask\nlima_maps2 = estimator.run(stacked_on_off)\nsignificance_map_off = lima_maps2[\"sqrt_ts\"]\n\n\n# region\nkwargs_axes = {\"xlabel\": \"Significance\", \"yscale\": \"log\", \"ylim\": (1e-3, 1)}\nax, _ = plot_distribution(\n    significance_map,\n    kwargs_hist={\n        \"density\": True,\n        \"alpha\": 0.5,\n        \"color\": \"red\",\n        \"label\": \"all bins\",\n        \"bins\": 51,\n    },\n    kwargs_axes=kwargs_axes,\n)\n\nax, res = plot_distribution(\n    significance_map_off,\n    ax=ax,\n    func=\"norm\",\n    kwargs_hist={\n        \"density\": True,\n        \"alpha\": 0.5,\n        \"color\": \"blue\",\n        \"label\": \"off bins\",\n        \"bins\": 51,\n    },\n    kwargs_axes=kwargs_axes,\n)\n\nplt.show()\n# endregion\n\n# sphinx_gallery_thumbnail_number = 2"
       ]
     }
   ],

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/docs/dev/_downloads/8755395ab1ca43f7e8444bdd5355f7fb/cta_sensitivity.zip b/docs/dev/_downloads/8755395ab1ca43f7e8444bdd5355f7fb/cta_sensitivity.zip
diff --git a/docs/dev/_downloads/879fb38cf085340a15de2c5c775d1fa2/pulsar_analysis.zip b/docs/dev/_downloads/879fb38cf085340a15de2c5c775d1fa2/pulsar_analysis.zip
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/920df746e67257657f5d44e868b95ff9/fitting.zip b/docs/dev/_downloads/920df746e67257657f5d44e868b95ff9/fitting.zip
diff --git a/docs/dev/_downloads/922821654c8ba9bfa3b550cbf4e44e37/estimators.zip b/docs/dev/_downloads/922821654c8ba9bfa3b550cbf4e44e37/estimators.zip
diff --git a/docs/dev/_downloads/947d48cd2a213f701fd804045a8e07ba/plot_template_temporal.zip b/docs/dev/_downloads/947d48cd2a213f701fd804045a8e07ba/plot_template_temporal.zip
diff --git a/docs/dev/_downloads/97689216e51c6b88714828e3017abcd6/plot_expdecay_temporal.zip b/docs/dev/_downloads/97689216e51c6b88714828e3017abcd6/plot_expdecay_temporal.zip
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/9a86f782b420f47c383e0ccef22286f8/fermi_lat.ipynb b/docs/dev/_downloads/9a86f782b420f47c383e0ccef22286f8/fermi_lat.ipynb
@@ -299,7 +299,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "To get an idea of the size of the PSF we check how the containment radii\nof the Fermi-LAT PSF vari with energy and different containment\nfractions:\n\n\n"
+        "To get an idea of the size of the PSF we check how the containment radii\nof the Fermi-LAT PSF vary with energy and different containment\nfractions:\n\n\n"
       ]
     },
     {

diff --git a/docs/dev/_downloads/9b87dc4ad92a01355cc5cdce7c2881d3/ring_background.zip b/docs/dev/_downloads/9b87dc4ad92a01355cc5cdce7c2881d3/ring_background.zip
diff --git a/docs/dev/_downloads/9f14531a4d302f2223d1303690da28de/estimators.py b/docs/dev/_downloads/9f14531a4d302f2223d1303690da28de/estimators.py
@@ -116,10 +116,10 @@
 #
 
 fp_estimator.norm.scan_min = 0.1
-fp_estimator.norm.scan_max = 3
+fp_estimator.norm.scan_max = 10
 
 ######################################################################
-# Note: The default scan range of the norm parameter is between 0.1 to 10. In case the upper
+# Note: The default scan range of the norm parameter is between 0.2 to 5. In case the upper
 # limit values lie outside this range, nan values will be returned. It may thus be useful to
 # increase this range, specially for the computation of upper limits from weak sources.
 #

diff --git a/docs/dev/_downloads/a28867a6c5c2c6d9e3f31c1c79412c84/plot_gen_gauss.zip b/docs/dev/_downloads/a28867a6c5c2c6d9e3f31c1c79412c84/plot_gen_gauss.zip