Repository: gammapy/gammapy
Pull request #5462 (2024-10-03 15:25:06)

Update all the documentation with the new name of CTA : CTAO
This PR update CTA to CTAO, with some additional cleaning

Astro-Kirsty: Missing ones:
- Figure caption https://docs.gammapy.org/dev/user-guide/package.html

Patch:
diff --git a/docs/development/dev_howto.rst b/docs/development/dev_howto.rst
index 4f4d210c8..86450c64b 100644
--- a/docs/development/dev_howto.rst
+++ b/docs/development/dev_howto.rst
@@ -70,9 +70,12 @@ Notes:
   Gammapy names defined here, to the names used in the formats.
   Of course, where formats are not set in stone yet, we advocate and encourage
   the use of the names chosen here.
-* Finally, we realise that eventually probably CTA will define this, and Gammapy
-  is only a prototype. So if CTA chooses something else, probably we will follow
-  suite and do one more backward-incompatible change at some point to align with CTA.
+* Finally, CTAO has proposed a full data model associated to the needed quantities. And
+  the community is working to create a data format for very-high-energy data produced by gamma-ray
+  and neutrino experiments within the open initiative `Very-high-energy Open Data Format`_. This format
+  aims to respect the CTAO data model, to respect the `FAIR principles`_ and to follow as much as
+  possible the `IVOA`_ recommendations. In order to handle past, current and old formats,
+  Gammapy should follow the `FAIR4RS principles`_ by proposing a user-friendly interface.
 
 Clobber or overwrite?
 +++++++++++++++++++++
diff --git a/docs/development/release.rst b/docs/development/release.rst
index 097749952..bf558988b 100644
--- a/docs/development/release.rst
+++ b/docs/development/release.rst
@@ -90,9 +90,8 @@ Steps for the day to announce the release:
    (decide on a case by case basis, if it's relevant to the group of people):
 
     * https://groups.google.com/forum/#!forum/astropy-dev
-    * https://lists.nasa.gov/mailman/listinfo/open-gamma-ray-astro
-    * CTA DATA list (cta-wp-dm@cta-observatory.org)
-    * hess-analysis
+    * CTAO AS WG list (cta-wg-as [at] cta-observatory [dot] org)
+    * hess-forum list (hess-forum [at] lsw.uni-heidelberg [dot] de)
 #. Make sure the release milestone and issue is closed on GitHub
 #. Update these release notes with any useful infos / steps that you learned
    while making the release (ideally try to script / automate the task or check,
diff --git a/docs/index.rst b/docs/index.rst
index 2fe2c37e2..1339b7d81 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -18,7 +18,7 @@ Gammapy
 
 
 Gammapy is a community-developed, open-source Python package for gamma-ray
-astronomy built on Numpy, Scipy and Astropy. **It is the core library for the** `CTA`_ **Science Tools**
+astronomy built on Numpy, Scipy and Astropy. **It is the core library for the** `CTAO`_ **Science Tools**
 but can also be used to analyse data from existing imaging atmospheric Cherenkov telescopes
 (IACTs), such as `H.E.S.S.`_, `MAGIC`_ and `VERITAS`_. It also provides some support
 for `Fermi-LAT`_ and `HAWC`_ data analysis.
diff --git a/docs/references.txt b/docs/references.txt
index 439821d8f..5aa944d4f 100644
--- a/docs/references.txt
+++ b/docs/references.txt
@@ -59,7 +59,7 @@
 
 .. _Python for gamma-ray astronomy 2015: http://gammapy.github.io/PyGamma15/
 
-.. _CTA: https://www.cta-observatory.org/
+.. _CTAO: https://www.ctao.org/
 .. _H.E.S.S.: https://www.mpi-hd.mpg.de/hfm/HESS/
 .. _HAWC: https://www.hawc-observatory.org/
 .. _VERITAS: https://veritas.sao.arizona.edu/
@@ -69,6 +69,7 @@
 .. _FermiPy: https://github.com/fermiPy/fermipy
 .. _gadf: https://gamma-astro-data-formats.readthedocs.io/
 .. _Gamma Astro Data Formats: https://gamma-astro-data-formats.readthedocs.io/
+.. _Very-high-energy Open Data Format: https://vodf.readthedocs.io/en/latest/index.html
 
 .. _April 2016 IACT data meeting: https://github.com/open-gamma-ray-astro/2016-04_IACT_DL3_Meeting/
 
@@ -135,3 +136,7 @@
 .. _A Whirlwind tour of Python: https://nbviewer.org/github/jakevdp/WhirlwindTourOfPython/blob/master/Index.ipynb
 .. _Python data science handbook: https://jakevdp.github.io/PythonDataScienceHandbook/
 .. _Astropy Hands-On Tutorial: https://github.com/Asterics2020-Obelics/School2019/tree/master/astropy
+
+.. _FAIR principles: https://www.go-fair.org/fair-principles/
+.. _FAIR4RS principles: https://www.rd-alliance.org/group_output/fair-principles-for-research-software-fair4rs-principles/
+.. _IVOA: https://ivoa.net/
\ No newline at end of file
diff --git a/docs/user-guide/astro/darkmatter/index.rst b/docs/user-guide/astro/darkmatter/index.rst
index 2509f74c8..daf81c33c 100644
--- a/docs/user-guide/astro/darkmatter/index.rst
+++ b/docs/user-guide/astro/darkmatter/index.rst
@@ -84,7 +84,7 @@ gamLike
 `GamLike`_ contains likelihood functions for most leading gamma-ray indirect
 searches for dark matter, including Fermi-LAT observations of dwarfs and the
 Galactic Centre (GC), HESS observations of the GC, and projected sensitivities
-for CTA observations of the GC. It is released in tandem with the `GAMBIT`_
+for CTAO observations of the GC. It is released in tandem with the `GAMBIT`_
 module `DarkBit`_.  DarkBit can be used for directly computing observables and
 likelihoods, for any combination of parameter values in some underlying
 particle model.
diff --git a/docs/user-guide/howto.rst b/docs/user-guide/howto.rst
index d7ce187f7..a058d500a 100644
--- a/docs/user-guide/howto.rst
+++ b/docs/user-guide/howto.rst
@@ -179,7 +179,7 @@ It is possible to combine Gammapy with astrophysical modeling codes, if they
 provide a Python interface. Usually this requires some glue code to be written,
 e.g. `~gammapy.modeling.models.NaimaSpectralModel` is an example of a Gammapy
 wrapper class around the Naima spectral model and radiation classes, which then
-allows modeling and fitting of Naima models within Gammapy (e.g. using CTA,
+allows modeling and fitting of Naima models within Gammapy (e.g. using CTAO,
 H.E.S.S. or Fermi-LAT data).
 
 .. accordion-footer::
@@ -327,7 +327,8 @@ Note that you can create your own style with matplotlib (see
 `here <https://matplotlib.org/stable/tutorials/introductory/customizing.html>`__ and
 `here <https://matplotlib.org/stable/tutorials/colors/colormaps.html>`__)
 
-The CTA observatory released a document describing best practices for **data visualisation in a way friendly to
+.. TODO: update the link
+The CTAO observatory released a document describing best practices for **data visualisation in a way friendly to
 color-blind people**:
 `CTAO document <https://www.cta-observatory.org/wp-content/uploads/2020/10/CTA_ColourBlindness_BestPractices2.pdf>`_. To
 use them, you should add into your notebooks or scripts the following lines after the Gammapy imports:
diff --git a/docs/user-guide/package.rst b/docs/user-guide/package.rst
index 242864800..d6e0b985c 100644
--- a/docs/user-guide/package.rst
+++ b/docs/user-guide/package.rst
@@ -42,7 +42,7 @@ the Gammapy API are explained in more detail in the following.
     Fig. 1 Data flow and sub-package structure of Gammapy. The folder icons
     represent the corresponding sub-packages. The direction of the
     the data flow is illustrated with shaded arrows. The top section
-    shows the data levels as defined by `CTA`_.
+    shows the data levels as defined by `CTAO`_.
 
 Analysis steps
 --------------
diff --git a/docs/user-guide/references.rst b/docs/user-guide/references.rst
index 576f673fa..cd4f5143e 100644
--- a/docs/user-guide/references.rst
+++ b/docs/user-guide/references.rst
@@ -91,7 +91,7 @@ Glossary
 
     GTI
       Short for "good time interval": it indicates a continuous time interval of data
-      acquisition. In CTA, it also represents a time interval in which the IRFs are
+      acquisition. In the GADF DL3 format, it also represents a time interval in which the IRFs are
       supposed to be constant.
 
     IRF
@@ -137,6 +137,10 @@ Glossary
       object, the type of plot (e.g. :math:`dN/dE`, :math:`E^2\ dN/dE`) is typically adjusted
       through the `sed_type` quantity. See :ref:`sedtypes` for a list of options.
 
+..    STI
+      Short for "stable time interval": it indicates a continuous time interval of data
+      acquisition for which the instrument response files are supposed to be constant.
+
     Stacked Analysis
       In a stacked analysis individual observations are reduced to datasets which
       are then stacked to produce a single reduced dataset. The latter is then used
diff --git a/docs/user-guide/utils.rst b/docs/user-guide/utils.rst
index b9733ad3b..dcb01e7dc 100644
--- a/docs/user-guide/utils.rst
+++ b/docs/user-guide/utils.rst
@@ -39,12 +39,12 @@ In Gammapy, `astropy.time.Time` objects are used to represent times:
 Note that Astropy chose ``format='isot'`` and ``scale='utc'`` as default and in
 Gammapy these are also the recommended format and time scale.
 
-.. warning::
+.. .. warning::
 
-   Actually what's written here is not true. In CTA it hasn't been decided if
+..   Actually what's written here is not true. In CTAO, it hasn't been decided if
    times will be in ``utc`` or ``tt`` (terrestrial time) format.
 
-   Here's a reminder that this needs to be settled / updated:
+..   Here's a reminder that this needs to be settled / updated:
    https://github.com/gammapy/gammapy/issues/284
 
 
diff --git a/examples/tutorials/analysis-1d/cta_sensitivity.py b/examples/tutorials/analysis-1d/cta_sensitivity.py
index 3d755d3dd..78509d491 100644
--- a/examples/tutorials/analysis-1d/cta_sensitivity.py
+++ b/examples/tutorials/analysis-1d/cta_sensitivity.py
@@ -2,12 +2,12 @@
 Point source sensitivity
 ========================
 
-Estimate the CTA sensitivity for a point-like IRF at a fixed zenith angle and fixed offset.
+Estimate the CTAO sensitivity for a point-like IRF at a fixed zenith angle and fixed offset.
 
 Introduction
 ------------
 
-This notebook explains how to estimate the CTA sensitivity for a
+This notebook explains how to estimate the CTAO sensitivity for a
 point-like IRF at a fixed zenith angle and fixed offset, using the full
 containment IRFs distributed for the CTA 1DC. The significance is
 computed for a 1D analysis (ON-OFF regions) with the Li&Ma formula.
@@ -81,7 +81,7 @@ empty_dataset = SpectrumDataset.create(geom=geom, energy_axis_true=energy_axis_t
 # Load IRFs and prepare dataset
 # -----------------------------
 #
-# We extract the 1D IRFs from the full 3D IRFs provided by CTA.
+# We extract the 1D IRFs from the full 3D IRFs provided by CTAO.
 #
 
 irfs = load_irf_dict_from_file(
@@ -153,7 +153,7 @@ dataset_on_off = SpectrumDatasetOnOff.from_spectrum_dataset(
 #
 # We assume an alpha of 0.2 (ratio between ON and OFF area). We then run the sensitivity estimator.
 #
-# These are the conditions imposed in standard CTA sensitivity computations.
+# These are the conditions imposed in standard CTAO sensitivity computations.
 
 sensitivity_estimator = SensitivityEstimator(
     gamma_min=10,
diff --git a/examples/tutorials/analysis-1d/spectrum_simulation.py b/examples/tutorials/analysis-1d/spectrum_simulation.py
index 4bb549e63..522f073c4 100644
--- a/examples/tutorials/analysis-1d/spectrum_simulation.py
+++ b/examples/tutorials/analysis-1d/spectrum_simulation.py
@@ -25,12 +25,12 @@ using Poisson probability distribution.
 This can be done to check the feasibility of a measurement, to test
 whether fitted parameters really provide a good fit to the data etc.
 
-Here we will see how to perform a 1D spectral simulation of a CTA
+Here we will see how to perform a 1D spectral simulation of a CTAO
 observation, in particular, we will generate OFF observations following
-the template background stored in the CTA IRFs.
+the template background stored in the CTAO IRFs.
 
 **Objective: simulate a number of spectral ON-OFF observations of a
-source with a power-law spectral model with CTA using the CTA 1DC
+source with a power-law spectral model with CTAO using the CTA 1DC
 response, fit them with the assumed spectral model and check that the
 distribution of fitted parameters is consistent with the input values.**
 
diff --git a/examples/tutorials/analysis-2d/modeling_2D.py b/examples/tutorials/analysis-2d/modeling_2D.py
index 95cd68985..f1de504a3 100644
--- a/examples/tutorials/analysis-2d/modeling_2D.py
+++ b/examples/tutorials/analysis-2d/modeling_2D.py
@@ -64,7 +64,7 @@ check_tutorials_setup()
 # Now, we create a config file for out analysis. You may load this from
 # disc if you have a pre-defined config file.
 #
-# Here, we use 3 simulated CTA runs of the galactic center.
+# Here, we use 3 simulated CTAO runs of the galactic center.
 #
 
 config = AnalysisConfig()
diff --git a/examples/tutorials/analysis-3d/cta_data_analysis.py b/examples/tutorials/analysis-3d/cta_data_analysis.py
index fdecc7637..6baa312c3 100644
--- a/examples/tutorials/analysis-3d/cta_data_analysis.py
+++ b/examples/tutorials/analysis-3d/cta_data_analysis.py
@@ -8,11 +8,11 @@ Introduction
 ------------
 
 **This notebook shows an example how to make a sky image and spectrum
-for simulated CTA data with Gammapy.**
+for simulated CTAO data with Gammapy.**
 
 The dataset we will use is three observation runs on the Galactic
 Center. This is a tiny (and thus quick to process and play with and
-learn) subset of the simulated CTA dataset that was produced for the
+learn) subset of the simulated CTAO dataset that was produced for the
 first data challenge in August 2017.
 
 """
@@ -401,7 +401,7 @@ plt.show()
 # What next?
 # ----------
 #
-# -  This notebook showed an example of a first CTA analysis with Gammapy,
+# -  This notebook showed an example of a first CTAO analysis with Gammapy,
 #    using simulated 1DC data.
 # -  Let us know if you have any questions or issues!
 #
diff --git a/examples/tutorials/analysis-3d/event_sampling_nrg_depend_models.py b/examples/tutorials/analysis-3d/event_sampling_nrg_depend_models.py
index 36c5e4aac..b9e9065e4 100644
--- a/examples/tutorials/analysis-3d/event_sampling_nrg_depend_models.py
+++ b/examples/tutorials/analysis-3d/event_sampling_nrg_depend_models.py
@@ -253,7 +253,7 @@ models = [model, bkg_model]
 # Define an observation and make a dataset
 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 #
-# In the following, we define an observation of 1 hr with CTA in the
+# In the following, we define an observation of 1 hr with CTAO in the
 # alpha-configuration for the south array, and we also create a dataset
 # to be passed to the event sampler. The full `SkyModel` created above
 # is passed to the dataset.
diff --git a/examples/tutorials/analysis-time/pulsar_analysis.py b/examples/tutorials/analysis-time/pulsar_analysis.py
index c031536ad..c52a63c2c 100644
--- a/examples/tutorials/analysis-time/pulsar_analysis.py
+++ b/examples/tutorials/analysis-time/pulsar_analysis.py
@@ -331,7 +331,7 @@ plt.show()
 
 
 ######################################################################
-# Note that here we are lacking statistic because we only use one run of CTA.
+# Note that here we are lacking statistic because we only use one run of CTAO.
 #
 # Phase-resolved spectrum
 # -----------------------
diff --git a/examples/tutorials/data/cta.py b/examples/tutorials/data/cta.py
index ec36908e9..afc77daeb 100644
--- a/examples/tutorials/data/cta.py
+++ b/examples/tutorials/data/cta.py
@@ -1,58 +1,58 @@
 """
-CTA with Gammapy
-================
+CTAO with Gammapy
+=================
 
-Access and inspect CTA data and instrument response functions (IRFs) using Gammapy.
+Access and inspect CTAO data and instrument response functions (IRFs) using Gammapy.
 
 Introduction
 ------------
 
-The `Cherenkov Telescope Array
-(CTA) <https://www.cta-observatory.org/>`__ is the next generation
+The `Cherenkov Telescope Array Observatory (CTAO) <https://www.ctao.org/>`__ is the next generation
 ground-based observatory for gamma-ray astronomy. Gammapy is the core
-library for the Cherenkov Telescope Array (CTA) science tools
+library for the Cherenkov Telescope Array Observatory (CTAO) science tools
 (`2017ICRC…35..766D <https://ui.adsabs.harvard.edu/abs/2017ICRC...35..766D>`__
 and `CTAO Press
-Release <https://www.cta-observatory.org/ctao-adopts-the-gammapy-software-package-for-science-analysis/>`__).
+Release <https://www.ctao.org/news/ctao-adopts-the-gammapy-software-package-for-science-analysis/>`__).
 
-CTA will start taking data in the coming years. For now, to learn how to
-analyse CTA data and to use Gammapy, if you are a member of the CTA
-consortium, you can use the simulated dataset from the CTA first data
-challenge which ran in 2017 and 2018.
+CTAO will start taking data in the coming years. For now, to learn how to
+analyse CTAO data and to use Gammapy, if you are a member of the CTAO
+consortium, you can use the simulated dataset from:
 
--  https://forge.in2p3.fr/projects/data-challenge-1-dc-1/wiki (CTA
-   internal)
+- the CTA first data challenge which ran in 2017 and 2018 (https://forge.in2p3.fr/projects/data-challenge-1-dc-1/wiki),
+- the CTAO Science Data Challenge of 2024 (https://ctaoobservatory.sharepoint.com/:f:/r/sites/share-open-data/Shared%20Documents/Reference%20datasets/Internal%20Science%20Data%20Challenge?csf=1&web=1&e=gNuFzI)
 
 Gammapy fully supports the FITS data formats (events, IRFs) used in CTA
-1DC. The XML sky model format is not supported, but are also not needed
+1DC and SDC. The XML sky model format is not supported, but are also not needed
 to analyse the data, you have to specify your model via the Gammapy YAML
 model format, or Python code, as shown below.
 
-You can use Gammapy to simulate CTA data and evaluate CTA performance
-using the CTA response files available here:
+You can also use Gammapy to simulate CTAO data and evaluate CTAO performance
+using the CTAO response files. Two sets of responses are available for different
+array layouts:
 
--  https://www.cta-observatory.org/science/cta-performance/
+- the Omega configuration (prod3b, 2016):  https://zenodo.org/records/5163273,
+- the Alpha configuration (prod5, 2021): https://zenodo.org/records/5499840.
+
+They are all fully supported by Gammapy.
 
-The current FITS format `CTA-Performance-prod3b-v2-FITS.tar` is fully
-supported by Gammapy, as shown below.
 
 Tutorial overview
 -----------------
 
-This notebook shows how to access CTA data and instrument response
+This notebook shows how to access CTAO data and instrument response
 functions (IRFs) using Gammapy, and gives some examples how to quick
-look the content of CTA files, especially to see the shape of CTA IRFs.
+look the content of CTAO files, especially to see the shape of CTAO IRFs.
 
 At the end of the notebooks, we give several links to other tutorial
-notebooks that show how to simulate CTA data and how to evaluate CTA
-observability and sensitivity, or how to analyse CTA data.
+notebooks that show how to simulate CTAO data and how to evaluate CTAO
+observability and sensitivity, or how to analyse CTAO data.
 
-Note that the FITS data and IRF format currently used by CTA is the one
+Note that the FITS data and IRF format currently used by CTAO is the one
 documented at https://gamma-astro-data-formats.readthedocs.io/, and is
-also used by H.E.S.S. and other imaging atmospheric Cherenkov telescopes
+also used by H.E.S.S. and other Imaging Atmospheric Cherenkov Telescopes
 (IACTs). So if you see other Gammapy tutorials using e.g. H.E.S.S.
-example data, know that they also apply to CTA, all you have to do is to
-change the loaded data or IRFs to CTA.
+example data, know that they also apply to CTAO, all you have to do is to
+change the loaded data or IRFs to CTAO.
 
 Setup
 -----
@@ -95,18 +95,18 @@ check_tutorials_setup()
 # After download, follow the instructions how to `untar` the files, and
 # set a `CTADATA` environment variable to point to the data.
 #
-# For convenience, since the 1DC data files are large, and not publicly
+# **For convenience**, since the 1DC data files are large and not publicly
 # available to anyone, we have taken a tiny subset of the CTA 1DC data,
 # four observations with the southern array from the GPS survey, pointing
-# near the Galactic center, and included them at `$GAMMAPY_DATA/cta-1dc`
+# near the Galactic center, and **included them at `$GAMMAPY_DATA/cta-1dc`**
 # which you get via `gammapy download datasets`.
 #
 # Files
 # ~~~~~
 #
 # Next we will show a quick overview of the files and how to load them,
-# and some quick look plots showing the shape of the CTA IRFs. How to do
-# CTA simulations and analyses is shown in other tutorials, see links at
+# and some quick look plots showing the shape of the CTAO IRFs. How to do
+# CTAO simulations and analyses is shown in other tutorials, see links at
 # the end of this notebook.
 #
 
@@ -164,9 +164,9 @@ print(observation)
 # equivalently via the `~gammapy.data.EventList` class by specifying the
 # EVENTS filename.
 #
-# The quick-look `events.peek()` plot below shows that CTA has a field
+# The quick-look `events.peek()` plot below shows that CTAO has a field
 # of view of a few degrees, and two energy thresholds, one significantly
-# below 100 GeV where the CTA large-size telescopes (LSTs) detect events,
+# below 100 GeV where the CTAO large-size telescopes (LSTs) detect events,
 # and a second one near 100 GeV where the mid-sized telescopes (MSTs)
 # start to detect events.
 #
@@ -206,7 +206,7 @@ plt.show()
 # IRFs
 # ----
 #
-# The CTA instrument response functions (IRFs) are given as FITS files in
+# The CTAO instrument response functions (IRFs) are given as FITS files in
 # the `caldb` folder, the following IRFs are available:
 #
 # -  effective area
@@ -216,12 +216,12 @@ plt.show()
 #
 # Notes:
 #
-# -  The IRFs contain the energy and offset dependence of the CTA response
-# -  CTA 1DC was based on an early version of the CTA FITS responses
+# -  The IRFs contain the energy and offset dependence of the CTAO response
+# -  CTA 1DC was based on an early version of the CTAO FITS responses
 #    produced in 2017, improvements have been made since.
 # -  The point spread function was approximated by a Gaussian shape
 # -  The background is from hadronic and electron air shower events that
-#    pass CTA selection cuts. It was given as a function of field of view
+#    pass CTAO selection cuts. It was given as a function of field of view
 #    coordinates, although it is radially symmetric.
 # -  The energy dispersion in CTA 1DC is noisy at low energy, leading to
 #    unreliable spectral points for some analyses.
@@ -355,28 +355,28 @@ plt.show()
 
 
 ######################################################################
-# CTA performance files
-# ---------------------
+# Latest CTAO performance files
+# -----------------------------
 #
-# CTA 1DC is useful to learn how to analyse CTA data. But to do
-# simulations and studies for CTA now, you should get the most recent CTA
-# IRFs in FITS format from
-# https://www.cta-observatory.org/science/cta-performance/
+# CTA 1DC is useful to learn how to analyse CTAO data. But to do
+# simulations and studies for CTAO now, you should get the most recent CTAO
+# IRFs in FITS format from https://www.ctao.org/for-scientists/performance/.
 #
-# If you want to run the download and examples in the next code cells,
-# remove the # to uncomment.
+# If you want to use other response files, the following code cells (remove the # to uncomment)
+# explain how to proceed. This example is made with the Alpha configuration (Prod5).
 #
 
-# !curl -O https://www.cta-observatory.org/wp-content/uploads/2019/04/CTA-Performance-prod3b-v2-FITS.tar.gz
-
-# !tar xf CTA-Performance-prod3b-v2-FITS.tar.gz
+# !curl -o cta-prod5-zenodo-fitsonly-v0.1.zip https://zenodo.org/records/5499840/files/cta-prod5-zenodo-fitsonly-v0.1.zip
+# !unzip cta-prod5-zenodo-fitsonly-v0.1.zip
+# !ls fits/
 
-# !ls caldb/data/cta/prod3b-v2/bcf
+# !tar xf fits/CTA-Performance-prod5-v0.1-South-40deg.FITS.tar.gz -C fits/.
+# !ls fits/*.fits.gz
 
-# irfs1 = load_irf_dict_from_file("caldb/data/cta/prod3b-v2/bcf/South_z20_50h/irf_file.fits")
+# irfs1 = load_irf_dict_from_file("fits/Prod5-South-40deg-SouthAz-14MSTs37SSTs.180000s-v0.1.fits.gz")
 # irfs1["aeff"].plot_energy_dependence()
 
-# irfs2 = load_irf_dict_from_file("caldb/data/cta/prod3b-v2/bcf/South_z40_50h/irf_file.fits")
+# irfs2 = load_irf_dict_from_file("fits/Prod5-South-40deg-SouthAz-14MSTs37SSTs.1800s-v0.1.fits.gz")
 # irfs2["aeff"].plot_energy_dependence()
 
 
@@ -391,11 +391,11 @@ plt.show()
 # -  Use `~gammapy.data.EventList.pointing_radec` to find the pointing position of this
 #    observation, and use `astropy.coordinates.SkyCoord` methods to find
 #    the field of view offset of the highest-energy event.
-# -  What is the effective area and PSF 68% containment radius of CTA at 1
+# -  What is the effective area and PSF 68% containment radius of CTAO at 1
 #    TeV for the `South_z20_50h` configuration used for the CTA 1DC
 #    simulation?
-# -  Get the latest CTA FITS performance files from
-#    https://www.cta-observatory.org/science/cta-performance/ and run the
+# -  Get the latest CTAO FITS performance files from
+#    https://www.ctao.org/for-scientists/performance/ and run the
 #    code example above. Make an effective area ratio plot of 40 deg
 #    zenith versus 20 deg zenith for the `South_z40_50h` and
 #    `South_z20_50h` configurations.
@@ -412,7 +412,7 @@ plt.show()
 #    :doc:`/tutorials/starting/analysis_1` and
 #    :doc:`/tutorials/starting/analysis_2` or any other
 #    Gammapy analysis tutorial.
-# -  Learn how to evaluate CTA observability and sensitivity with
+# -  Learn how to evaluate CTAO observability and sensitivity with
 #    :doc:`/tutorials/analysis-3d/simulate_3d`,
 #    :doc:`/tutorials/analysis-1d/spectrum_simulation`
 #    or :doc:`/tutorials/analysis-1d/cta_sensitivity`.
diff --git a/examples/tutorials/data/hawc.py b/examples/tutorials/data/hawc.py
index e3c85fa87..ac567d26e 100644
--- a/examples/tutorials/data/hawc.py
+++ b/examples/tutorials/data/hawc.py
@@ -28,7 +28,7 @@ proceeding.
 
 Once the data has been reduced to a MapDataset, there are no differences
 in the way that HAWC data is handled with respect to data from any other
-observatory, such as H.E.S.S. or CTA.
+observatory, such as H.E.S.S. or CTAO.
 
 
 HAWC data access and reduction
diff --git a/examples/tutorials/data/hess.py b/examples/tutorials/data/hess.py
index e86583a85..6e9c40ec1 100644
--- a/examples/tutorials/data/hess.py
+++ b/examples/tutorials/data/hess.py
@@ -10,7 +10,7 @@ current `open data level 3
 format <https://gamma-astro-data-formats.readthedocs.io/>`__.
 
 The H.E.S.S. data is private, and H.E.S.S. analysis is mostly documented
-and discussed at https://hess-confluence.desy.de/ and in
+and discussed in the internal Wiki pages and in
 H.E.S.S.-internal communication channels. However, in 2018, a small
 sub-set of archival H.E.S.S. data was publicly released, called the
 `H.E.S.S. DL3
@@ -26,9 +26,8 @@ simple exploration of the data with the creation of theta-squared plot.
 
 H.E.S.S. members can find details on the DL3 FITS production on this
 `Confluence
-page <https://hess-confluence.desy.de/confluence/display/HESS/HESS+FITS+data>`__
-and access more detailed tutorials in this
-`repository <https://bitbucket.org/hess_software/hess-open-source-tools/src/master/>`__
+page <https://cchesswiki.in2p3.fr/en/hess/working_groups/analysis_and_reconstruction_working_group/ar_active_tasks/hess_fits_data>`__
+and access more detailed tutorials in the BitBucket `hess-open-tools` repository.
 
 DL3 DR1
 -------
@@ -146,6 +145,6 @@ plt.show()
 # ----------
 #
 # Now you know how to access and work with H.E.S.S. data. All other
-# tutorials and documentation apply to H.E.S.S. and CTA or any other IACT
+# tutorials and documentation apply to H.E.S.S. and CTAO or any other IACT
 # that provides DL3 data and IRFs in the standard format.
 #
diff --git a/gammapy/data/data_store.py b/gammapy/data/data_store.py
index 545c629c0..4191be1ef 100644
--- a/gammapy/data/data_store.py
+++ b/gammapy/data/data_store.py
@@ -197,11 +197,6 @@ class DataStore:
         - ``CALDB`` (example: ``CALDB = 1dc``)
         - ``IRF`` (example: ``IRF = South_z20_50h``)
 
-        This method is useful specifically if you want to load data simulated
-        with `ctobssim`_.
-
-        .. _ctobssim: http://cta.irap.omp.eu/ctools/users/reference_manual/ctobssim.html
-
         Parameters
         ----------
         events_paths : list of str or `~pathlib.Path`
@@ -732,7 +727,6 @@ class DataStoreMaker:
         yield dict(HDU_TYPE="bkg", HDU_CLASS="bkg_3d", HDU_NAME="BACKGROUND", **info)
 
 
-# TODO: load IRF file, and infer HDU_CLASS from IRF file contents!
 class CalDBIRF:
     """Helper class to work with IRFs in CALDB format."""
 
diff --git a/gammapy/irf/edisp/map.py b/gammapy/irf/edisp/map.py
index 3115d5133..53cb1571c 100644
--- a/gammapy/irf/edisp/map.py
+++ b/gammapy/irf/edisp/map.py
@@ -35,7 +35,7 @@ class EDispMap(IRFMap):
     --------
     ::
 
-        # Energy dispersion map for CTA data
+        # Energy dispersion map for CTAO data
         import numpy as np
         from astropy import units as u
         from astropy.coordinates import SkyCoord
diff --git a/gammapy/irf/effective_area.py b/gammapy/irf/effective_area.py
index 291ec9915..851f2b16a 100644
--- a/gammapy/irf/effective_area.py
+++ b/gammapy/irf/effective_area.py
@@ -1,4 +1,5 @@
 # Licensed under a 3-clause BSD style license - see LICENSE.rst
+import warnings
 import numpy as np
 import astropy.units as u
 from astropy.visualization import quantity_support
@@ -7,6 +8,7 @@ from gammapy.maps import MapAxes, MapAxis
 from gammapy.maps.axes import UNIT_STRING_FORMAT
 from gammapy.visualization.utils import add_colorbar
 from .core import IRF
+from gammapy.utils.deprecation import GammapyDeprecationWarning
 
 __all__ = ["EffectiveAreaTable2D"]
 
@@ -239,7 +241,7 @@ class EffectiveAreaTable2D(IRF):
         energy_axis_true : `MapAxis`, optional
             Energy binning, analytic function is evaluated at log centers.
             Default is None.
-        instrument : {'HESS', 'HESS2', 'CTA'}
+        instrument : {'HESS', 'HESS2', 'CTAO'}
             Instrument name. Default is 'HESS'.
 
         Returns
@@ -252,9 +254,15 @@ class EffectiveAreaTable2D(IRF):
         pars = {
             "HESS": [6.85e9, 0.0891, 5e5],
             "HESS2": [2.05e9, 0.0891, 1e5],
-            "CTA": [1.71e11, 0.0891, 1e5],
+            "CTAO": [1.71e11, 0.0891, 1e5],
         }
 
+        if instrument == "CTA":
+            instrument = "CTAO"
+            warnings.warn(
+                "Since v1.3, the value 'CTA' is replaced by 'CTAO' for the argument instrument.",
+                GammapyDeprecationWarning,
+            )
         if instrument not in pars.keys():
             ss = f"Unknown instrument: {instrument}\n"
             ss += f"Valid instruments: {list(pars.keys())}"
diff --git a/gammapy/makers/map.py b/gammapy/makers/map.py
index 13257f985..d8d39cb99 100644
--- a/gammapy/makers/map.py
+++ b/gammapy/makers/map.py
@@ -34,7 +34,7 @@ class MapDatasetMaker(Maker):
         Background evaluation oversampling factor in energy.
     background_interp_missing_data : bool, optional
         Interpolate missing values in background 3d map.
-        Default is True, have to be set to True for CTA IRF.
+        Default is True, have to be set to True for CTAO IRF.
     background_pad_offset : bool, optional
         Pad one bin in offset for 2d background map.
         This avoids extrapolation at edges and use the nearest value.
diff --git a/gammapy/modeling/models/spectral_cosmic_ray.py b/gammapy/modeling/models/spectral_cosmic_ray.py
index 364f14d35..65573fc2b 100644
--- a/gammapy/modeling/models/spectral_cosmic_ray.py
+++ b/gammapy/modeling/models/spectral_cosmic_ray.py
@@ -2,7 +2,7 @@
 """Simple models for cosmic ray spectra at Earth.
 
 For measurements, the "Database of Charged Cosmic Rays (CRDB)" is a great resource:
-http://lpsc.in2p3.fr/cosmic-rays-db/
+https://lpsc.in2p3.fr/crdb/
 """
 import numpy as np
 from astropy import units as u
@@ -37,7 +37,7 @@ class _LogGaussianSpectralModel(SpectralModel):
 def create_cosmic_ray_spectral_model(particle="proton"):
     """Cosmic a cosmic ray spectral model at Earth.
 
-    These are the spectra assumed in this CTA study:
+    These are the spectra assumed in this CTAO study:
     Table 3 in https://ui.adsabs.harvard.edu/abs/2013APh....43..171B
 
     The spectrum given is a differential flux ``dnde`` in units of


Test patch:
diff --git a/gammapy/irf/tests/test_effective_area.py b/gammapy/irf/tests/test_effective_area.py
index f986c995f..90bab7df0 100644
--- a/gammapy/irf/tests/test_effective_area.py
+++ b/gammapy/irf/tests/test_effective_area.py
@@ -10,6 +10,7 @@ from gammapy.utils.testing import (
     mpl_plot_check,
     requires_data,
 )
+from gammapy.utils.deprecation import GammapyDeprecationWarning
 
 
 @pytest.fixture(scope="session")
@@ -54,6 +55,13 @@ def test_from_parametrization():
     assert area.unit == area_ref.unit
     assert area.meta["TELESCOP"] == "HESS"
 
+    with pytest.warns(GammapyDeprecationWarning):
+        area2 = EffectiveAreaTable2D.from_parametrization(axis, "CTA")
+        assert area2.unit == area_ref.unit
+
+    with pytest.raises(ValueError):
+        area2 = EffectiveAreaTable2D.from_parametrization(axis, "SWIFT")
+
 
 @requires_data()
 def test_plot(aeff):