[](https://www.gnu.org/licenses/gpl-3.0) [](http://dx.doi.org/10.5281/zenodo.7060236) [](https://gwfast.readthedocs.io/en/latest/?badge=latest) [](https://badge.fury.io/py/gwfast) <a href="https://ascl.net/2212.001"><img src="https://img.shields.io/badge/ascl-2212.001-blue.svg?colorB=262255" alt="ascl:2212.001" /></a>[](https://inspirehep.net/literature/2106524) [](https://inspirehep.net/literature/2112457)
# gwfast
Fisher Information Matrix package for GW cosmology, written in Python and based on automatic differentiation.
The detail of implementations and results can be found in the papers [arXiv:2207.02771](<https://arxiv.org/abs/2207.02771>) and [arXiv:2207.06910](<https://arxiv.org/abs/2207.06910>).
Waveforms are also separatley released as [WF4Py](<https://github.com/CosmoStatGW/WF4Py>).
Developed by [Francesco Iacovelli](<https://github.com/FrancescoIacovelli>) and [Michele Mancarella](<https://github.com/Mik3M4n>).
## Code Organization
The organisation of the repository is the following:
```
gwfast/gwfast/
├── gwfastGlobals.py
Physical constants, positions and duty cycles of existing detectors
├── gwfastUtils.py
Auxiliary functions: angles and time conversions, ...
├── waveforms.py
Abstract class WaveFormModel; different sublasses for each wf model - TaylorF2, IMRPhenomD, ...
├── signal.py
A class to compute the GW signal in a single detector (L shaped or triangular), the SNR and the Fisher matrix
├── fisherTools.py
Covariance matrix and functions to perform sanity checks on the Fisher - condition number, inversion error, marginalization, localization area, plotting tools
├── network.py
A class to model a network of detectors with different locations
gwfast/psds/
Some detector Power Spectral Densities
gwfast/WFfiles/
Text files needed for waveform computation
gwfast/run/
Script to run in parallel on catalogs
gwfast/docs/
Code documentation in Sphinx
```
## Summary
* [Documentation](https://github.com/CosmoStatGW/gwfast#Documentation)
* [Installation](https://github.com/CosmoStatGW/gwfast#Installation)
* [Usage](https://github.com/CosmoStatGW/gwfast#Usage)
* [Citation](https://github.com/CosmoStatGW/gwfast#Citation)
## Documentation
gwfast has its documentation hosted on Read the Docs [here](<https://gwfast.readthedocs.io/en/latest/>), and it can also be built from the ```docs``` directory.
## Installation
To install the package without cloning the git repository, and a CPU-only version of JAX
```
pip install --upgrade pip
pip install gwfast
```
or
```
pip install --upgrade pip
pip install --upgrade "jax[cpu]"
pip install git+https://github.com/CosmoStatGW/gwfast
```
To install a JAX version for GPU or TPU proceed as explained in [https://github.com/google/jax#installation](<https://github.com/google/jax#installation>).
If willing to use numerical differentiation, a patch has to be applied to [```numdifftools```](<https://pypi.org/project/numdifftools/>). This can be done by running the following command while being in the environment ```gwfast``` has been installed into
```
patch $(python -c "import site; print(site.getsitepackages()[0])")"/numdifftools/limits.py" $(python -c "import site; print(site.getsitepackages()[0])")"/gwfast/.patch/patch_ndt_complex_0-9-41.patch
```
## Usage
All details are reported in the accompanying paper [arXiv:2207.06910](<https://arxiv.org/abs/2207.06910>) and some examples are in the [gwfast_tutorial](<https://github.com/CosmoStatGW/gwfast/blob/master/notebooks/gwfast_tutorial.ipynb>) notebook. <a target="_blank" href="https://colab.research.google.com/github/CosmoStatGW/gwfast/blob/master/notebooks/gwfast_tutorial.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
To initialise a *waveform* object simply run, e.g.
```python
mywf = waveforms.IMRPhenomD()
```
(more details on the waveforms are available in their dedicated git repository [WF4Py](<https://github.com/CosmoStatGW/WF4Py>))
and to build a *signal* object
```python
MyDet = signal.GWSignal(mywf, psd_path= 'path/to/Detector/psd',
detector_shape = 'L', det_lat=43.6,
det_long=10.5, det_xax=115.)
```
More signal objects can be used to form a *network*
```python
myNet = network.DetNet({'Det1':MyDet1, 'Det2':MyDet2, ...})
```
Then computing **SNRs** and **Fisher matrices** is as easy as
```python
SNRs = myNet.SNR(events)
FisherMatrs = myNet.FisherMatr(events)
```
where ```events ``` is a dictionary containing the parameters of the chosen events.
Finally, to compute the **covariance matrices** it is sufficient to
```python
CovMatr(FisherMatrs, events)
```
#### For a list of features implemented after the publication of [arXiv:2207.06910](<https://arxiv.org/abs/2207.06910>) see the [NEW_FEATURES](<https://github.com/CosmoStatGW/gwfast/blob/master/NEW_FEATURES.md>) file and the [new\_features_tutorial](<https://github.com/CosmoStatGW/gwfast/blob/master/notebooks/new_features_tutorial.ipynb>) notebook <a target="_blank" href="https://colab.research.google.com/github/CosmoStatGW/gwfast/blob/master/notebooks/new_features_tutorial.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
## Citation
If using this software, please cite this repository and the papers [arXiv:2207.02771](<https://arxiv.org/abs/2207.02771>) and [arXiv:2207.06910](<https://arxiv.org/abs/2207.06910>). Bibtex:
```
@article{Iacovelli:2022bbs,
author = "Iacovelli, Francesco and Mancarella, Michele and Foffa, Stefano and Maggiore, Michele",
title = "{Forecasting the Detection Capabilities of Third-generation Gravitational-wave Detectors Using GWFAST}",
eprint = "2207.02771",
archivePrefix = "arXiv",
primaryClass = "gr-qc",
doi = "10.3847/1538-4357/ac9cd4",
journal = "Astrophys. J.",
volume = "941",
number = "2",
pages = "208",
year = "2022"
}
```
```
@article{Iacovelli:2022mbg,
author = "Iacovelli, Francesco and Mancarella, Michele and Foffa, Stefano and Maggiore, Michele",
title = "{GWFAST: A Fisher Information Matrix Python Code for Third-generation Gravitational-wave Detectors}",
eprint = "2207.06910",
archivePrefix = "arXiv",
primaryClass = "astro-ph.IM",
doi = "10.3847/1538-4365/ac9129",
journal = "Astrophys. J. Supp.",
volume = "263",
number = "1",
pages = "2",
year = "2022"
}
```
# gwfast documentation
## Documentation requirements
In order to build the documentation, the following packages have to be installed
* [```sphinx```](<https://www.sphinx-doc.org/en/master>)
* [```sphinx_rtd_theme```](<https://sphinx-rtd-theme.readthedocs.io/en/stable/>)
* [```nbsphinx```](<https://nbsphinx.readthedocs.io/en/0.8.11/>)
* [```myst-parser```](<https://myst-parser.readthedocs.io/en/latest/>)
* [```sphinx-copybutton```](<https://sphinx-copybutton.readthedocs.io/en/latest/?badge=latest>)
* [```readthedocs-sphinx-search```](<https://readthedocs-sphinx-search.readthedocs.io/en/latest/>)
* [```docutils```](<https://docutils.sourceforge.io>)
To install them just run in the terminal
```
pip install --upgrade pip
pip install -r docs/docs_requirements.txt
```
## Build the documentation
The HTML documentation can easily be built from the ```docs``` folder, running in the terminal
```
cd docs/
make html
```
The produced ```.html``` files will be stored in the directory ```./build/html```.
It is also possible to build a LaTex version, running in the terminal
```
make latexpdf
```
the output pdf of this command will be ```./build/latex/gwfast.pdf```.
# gwfast/psds
We here list the sources of the available Power Spectral Densities, PSDs, or Amplitude Spectral Densities, ASDs available in GWFast, in alphabetical order
### ce\_strain/
Cosmic Explorer ASDs from [*Science-Driven Tunable Design of Cosmic Explorer Detectors*](https://arxiv.org/abs/2201.10668), available at [https://dcc.cosmicexplorer.org/cgi-bin/DocDB/ShowDocument?.submit=Identifier&docid=T2000017&version=](https://dcc.cosmicexplorer.org/cgi-bin/DocDB/ShowDocument?.submit=Identifier&docid=T2000017&version=).
The folder contains ASDs for:
* the baseline 40km detector (```cosmic_explorer```)
* the baseline 20 km detector compact binary tuned (```cosmic_explorer_20km```)
* the 20 km detector tuned for post-merger signals (```cosmic_explorer_20km_pm```)
* the 40 km detector tuned for low-freqency signals (```cosmic_explorer_40km_lf```)
### ET\_designs\_comparison\_paper/
Einstein Telescope ASDs from [*Science with the Einstein Telescope: a comparison of different designs*](https://arxiv.org/abs/2303.15923), available at [https://apps.et-gw.eu/tds/?content=3&r=18213](https://apps.et-gw.eu/tds/?content=3&r=18213).
The folder contains two subfolders with ASDs for:
* the high frequency (HF) only ET instrument with a length of 10 km (```ETLength10km```), 15 km (```ETLength15km```) and 20 km (```ETLength20km```), in the **HF_only/** folder
* the full high frequency (HF) and low frequency (LF) ET instrument in the cryogenic design with a length of 10 km (```ETLength10km```), 15 km (```ETLength15km```) and 20 km (```ETLength20km```), in the **HFLF_cryo/** folder
### ET-0000A-18.txt
Public [ET-D](https://arxiv.org/abs/1012.0908) sensnitivity curve.
Available at [https://apps.et-gw.eu/tds/?content=3&r=14065](https://apps.et-gw.eu/tds/?content=3&r=14065). Notice that we kept only the first and last column of the file, corresponding to the frequencies and the total ET-D sensitivity, obtained combining the LF and HF instruments.
### LVC_O1O2O3/
The folder contains ASDs for the LIGO and Virgo detectors during their O1, O2 and O3 observing runs, extracted in specific moment from actual data.
Available at [https://dcc.ligo.org/P1800374/public/](https://dcc.ligo.org/P1800374/public/) for O1 and O2, [https://dcc.ligo.org/LIGO-P2000251/public](https://dcc.ligo.org/LIGO-P2000251/public) for O3a, and computed using [PyCBC](https://pycbc.org) around the times indicated in the caption of Fig. 2 of [https://arxiv.org/abs/2111.03606](https://arxiv.org/abs/2111.03606).
### observing\_scenarios\_paper/
ASDs used for the paper [*Prospects for observing and localizing gravitational-wave transients with Advanced LIGO, Advanced Virgo and KAGRA*, KAGRA Collaboration, LIGO Scientific Collaboration and Virgo Collaboration](https://link.springer.com/article/10.1007/s41114-020-00026-9).
Available at [https://dcc.ligo.org/LIGO-T2000012/public](https://dcc.ligo.org/LIGO-T2000012/public).
The folder contains ASDs for the Advanced LIGO, Advanced Virgo and KAGRA detectors during the O3, O4 and O5 observing runs.
### unofficial\_curves\_all\_dets/
Public ASDs for both the current and future generation of detectors (last update in January 2020).
Available at [https://dcc.ligo.org/LIGO-T1500293/public](https://dcc.ligo.org/LIGO-T1500293/public), in the *curves\_Jan\_2020.zip* file.
The folder contains ASDs for:
* Advanced LIGO and Advanced Virgo during both the O1, O2 and O3 runs, at design sensitivity and in the *Advanced plus* stage;
* KAGRA;
* LIGO Voyager;
* ET-D;
* CE1 and CE2.
Raw data
{
"_id": null,
"home_page": "https://github.com/CosmoStatGW/gwfast",
"name": "gwfast",
"maintainer": "Niccolo' Muttoni",
"docs_url": null,
"requires_python": ">=3.9",
"maintainer_email": "niccolo.muttoni@unige.ch",
"keywords": "python, automatic-differentiation, gravitational-waves, fisher-information, jax",
"author": "Francesco Iacovelli",
"author_email": "francesco.iacovelli@unige.ch",
"download_url": "https://files.pythonhosted.org/packages/95/ab/d8ed94c4a328af060ca35f4ddb1a428ba3f5d283f02d7764012cd8fb124f/gwfast-1.1.2.tar.gz",
"platform": null,
"description": "[](https://www.gnu.org/licenses/gpl-3.0) [](http://dx.doi.org/10.5281/zenodo.7060236) [](https://gwfast.readthedocs.io/en/latest/?badge=latest) [](https://badge.fury.io/py/gwfast) <a href=\"https://ascl.net/2212.001\"><img src=\"https://img.shields.io/badge/ascl-2212.001-blue.svg?colorB=262255\" alt=\"ascl:2212.001\" /></a>[](https://inspirehep.net/literature/2106524) [](https://inspirehep.net/literature/2112457)\n\n\n\n# gwfast\nFisher Information Matrix package for GW cosmology, written in Python and based on automatic differentiation.\n\nThe detail of implementations and results can be found in the papers [arXiv:2207.02771](<https://arxiv.org/abs/2207.02771>) and [arXiv:2207.06910](<https://arxiv.org/abs/2207.06910>).\n\nWaveforms are also separatley released as [WF4Py](<https://github.com/CosmoStatGW/WF4Py>).\n\nDeveloped by [Francesco Iacovelli](<https://github.com/FrancescoIacovelli>) and [Michele Mancarella](<https://github.com/Mik3M4n>).\n\n## Code Organization\nThe organisation of the repository is the following:\n\n```\ngwfast/gwfast/\n\t\t\t\u251c\u2500\u2500 gwfastGlobals.py \n\t\t\t\t\tPhysical constants, positions and duty cycles of existing detectors\n\t\t\t\u251c\u2500\u2500 gwfastUtils.py\n\t\t\t\t\tAuxiliary functions: angles and time conversions, ...\n\t\t\t\u251c\u2500\u2500 waveforms.py\n\t\t\t\t\tAbstract class WaveFormModel; different sublasses for each wf model - TaylorF2, IMRPhenomD, ...\n\t\t\t\u251c\u2500\u2500 signal.py\n\t\t\t\t\tA class to compute the GW signal in a single detector (L shaped or triangular), the SNR and the Fisher matrix\n\t\t\t\u251c\u2500\u2500 fisherTools.py\n\t\t\t\t\tCovariance matrix and functions to perform sanity checks on the Fisher - condition number, inversion error, marginalization, localization area, plotting tools\n\t\t\t\u251c\u2500\u2500 network.py\n\t\t\t\t\tA class to model a network of detectors with different locations\n\ngwfast/psds/ \n\t\t\tSome detector Power Spectral Densities \n\t\t\t\ngwfast/WFfiles/ \n\t\t\tText files needed for waveform computation\n\t\t\t\ngwfast/run/\n\t\t\tScript to run in parallel on catalogs\n\t\t\t\ngwfast/docs/ \n\t\t\tCode documentation in Sphinx\n\t\t\t\t\t\t\n```\n\n## Summary\n\n* [Documentation](https://github.com/CosmoStatGW/gwfast#Documentation)\n* [Installation](https://github.com/CosmoStatGW/gwfast#Installation)\n* [Usage](https://github.com/CosmoStatGW/gwfast#Usage)\n* [Citation](https://github.com/CosmoStatGW/gwfast#Citation)\n\n## Documentation\n\ngwfast has its documentation hosted on Read the Docs [here](<https://gwfast.readthedocs.io/en/latest/>), and it can also be built from the ```docs``` directory.\n\n## Installation\nTo install the package without cloning the git repository, and a CPU-only version of JAX \n\n```\npip install --upgrade pip\npip install gwfast\n```\n\nor \n\n```\npip install --upgrade pip\npip install --upgrade \"jax[cpu]\" \npip install git+https://github.com/CosmoStatGW/gwfast\n```\n\nTo install a JAX version for GPU or TPU proceed as explained in [https://github.com/google/jax#installation](<https://github.com/google/jax#installation>).\n\nIf willing to use numerical differentiation, a patch has to be applied to [```numdifftools```](<https://pypi.org/project/numdifftools/>). This can be done by running the following command while being in the environment ```gwfast``` has been installed into\n\n```\npatch $(python -c \"import site; print(site.getsitepackages()[0])\")\"/numdifftools/limits.py\" $(python -c \"import site; print(site.getsitepackages()[0])\")\"/gwfast/.patch/patch_ndt_complex_0-9-41.patch\n```\n\n\n## Usage\n\nAll details are reported in the accompanying paper [arXiv:2207.06910](<https://arxiv.org/abs/2207.06910>) and some examples are in the [gwfast_tutorial](<https://github.com/CosmoStatGW/gwfast/blob/master/notebooks/gwfast_tutorial.ipynb>) notebook. <a target=\"_blank\" href=\"https://colab.research.google.com/github/CosmoStatGW/gwfast/blob/master/notebooks/gwfast_tutorial.ipynb\"> <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n</a>\n\nTo initialise a *waveform* object simply run, e.g.\n\n```python\nmywf = waveforms.IMRPhenomD()\n```\n(more details on the waveforms are available in their dedicated git repository [WF4Py](<https://github.com/CosmoStatGW/WF4Py>))\n\nand to build a *signal* object \n\n```python\nMyDet = signal.GWSignal(mywf, psd_path= 'path/to/Detector/psd',\n \t\t\t\t\t\tdetector_shape = 'L', det_lat=43.6, \n \t\t\t\t\t\tdet_long=10.5, det_xax=115.) \n```\n\nMore signal objects can be used to form a *network*\n\n```python\nmyNet = network.DetNet({'Det1':MyDet1, 'Det2':MyDet2, ...}) \n```\n\nThen computing **SNRs** and **Fisher matrices** is as easy as\n\n```python\nSNRs = myNet.SNR(events) \nFisherMatrs = myNet.FisherMatr(events) \n```\nwhere ```events ``` is a dictionary containing the parameters of the chosen events.\n\nFinally, to compute the **covariance matrices** it is sufficient to\n\n```python\nCovMatr(FisherMatrs, events) \n```\n\n#### For a list of features implemented after the publication of [arXiv:2207.06910](<https://arxiv.org/abs/2207.06910>) see the [NEW_FEATURES](<https://github.com/CosmoStatGW/gwfast/blob/master/NEW_FEATURES.md>) file and the [new\\_features_tutorial](<https://github.com/CosmoStatGW/gwfast/blob/master/notebooks/new_features_tutorial.ipynb>) notebook <a target=\"_blank\" href=\"https://colab.research.google.com/github/CosmoStatGW/gwfast/blob/master/notebooks/new_features_tutorial.ipynb\"> <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n</a>\n\n## Citation\n\nIf using this software, please cite this repository and the papers [arXiv:2207.02771](<https://arxiv.org/abs/2207.02771>) and [arXiv:2207.06910](<https://arxiv.org/abs/2207.06910>). Bibtex:\n\n```\n@article{Iacovelli:2022bbs,\n author = \"Iacovelli, Francesco and Mancarella, Michele and Foffa, Stefano and Maggiore, Michele\",\n title = \"{Forecasting the Detection Capabilities of Third-generation Gravitational-wave Detectors Using GWFAST}\",\n eprint = \"2207.02771\",\n archivePrefix = \"arXiv\",\n primaryClass = \"gr-qc\",\n doi = \"10.3847/1538-4357/ac9cd4\",\n journal = \"Astrophys. J.\",\n volume = \"941\",\n number = \"2\",\n pages = \"208\",\n year = \"2022\"\n}\n```\n\n```\n@article{Iacovelli:2022mbg,\n author = \"Iacovelli, Francesco and Mancarella, Michele and Foffa, Stefano and Maggiore, Michele\",\n title = \"{GWFAST: A Fisher Information Matrix Python Code for Third-generation Gravitational-wave Detectors}\",\n eprint = \"2207.06910\",\n archivePrefix = \"arXiv\",\n primaryClass = \"astro-ph.IM\",\n doi = \"10.3847/1538-4365/ac9129\",\n journal = \"Astrophys. J. Supp.\",\n volume = \"263\",\n number = \"1\",\n pages = \"2\",\n year = \"2022\"\n}\n```\n# gwfast documentation\n\n## Documentation requirements\n\nIn order to build the documentation, the following packages have to be installed\n\n* [```sphinx```](<https://www.sphinx-doc.org/en/master>)\n* [```sphinx_rtd_theme```](<https://sphinx-rtd-theme.readthedocs.io/en/stable/>)\n* [```nbsphinx```](<https://nbsphinx.readthedocs.io/en/0.8.11/>)\n* [```myst-parser```](<https://myst-parser.readthedocs.io/en/latest/>)\n* [```sphinx-copybutton```](<https://sphinx-copybutton.readthedocs.io/en/latest/?badge=latest>)\n* [```readthedocs-sphinx-search```](<https://readthedocs-sphinx-search.readthedocs.io/en/latest/>)\n* [```docutils```](<https://docutils.sourceforge.io>)\n\nTo install them just run in the terminal \n\n```\npip install --upgrade pip\npip install -r docs/docs_requirements.txt\n```\n\n## Build the documentation\n\nThe HTML documentation can easily be built from the ```docs``` folder, running in the terminal \n\n```\ncd docs/\nmake html\n```\n\nThe produced ```.html``` files will be stored in the directory ```./build/html```.\n\nIt is also possible to build a LaTex version, running in the terminal \n\n```\nmake latexpdf\n```\n\nthe output pdf of this command will be ```./build/latex/gwfast.pdf```.\n\n# gwfast/psds\nWe here list the sources of the available Power Spectral Densities, PSDs, or Amplitude Spectral Densities, ASDs available in GWFast, in alphabetical order\n\n### ce\\_strain/\n\nCosmic Explorer ASDs from [*Science-Driven Tunable Design of Cosmic Explorer Detectors*](https://arxiv.org/abs/2201.10668), available at [https://dcc.cosmicexplorer.org/cgi-bin/DocDB/ShowDocument?.submit=Identifier&docid=T2000017&version=](https://dcc.cosmicexplorer.org/cgi-bin/DocDB/ShowDocument?.submit=Identifier&docid=T2000017&version=).\n\nThe folder contains ASDs for:\n\n* the baseline 40km detector (```cosmic_explorer```)\n* the baseline 20 km detector compact binary tuned (```cosmic_explorer_20km```)\n* the 20 km detector tuned for post-merger signals (```cosmic_explorer_20km_pm```)\n* the 40 km detector tuned for low-freqency signals (```cosmic_explorer_40km_lf```)\n\n### ET\\_designs\\_comparison\\_paper/\n\nEinstein Telescope ASDs from [*Science with the Einstein Telescope: a comparison of different designs*](https://arxiv.org/abs/2303.15923), available at [https://apps.et-gw.eu/tds/?content=3&r=18213](https://apps.et-gw.eu/tds/?content=3&r=18213).\n\nThe folder contains two subfolders with ASDs for:\n\n* the high frequency (HF) only ET instrument with a length of 10 km (```ETLength10km```), 15 km (```ETLength15km```) and 20 km (```ETLength20km```), in the **HF_only/** folder\n* the full high frequency (HF) and low frequency (LF) ET instrument in the cryogenic design with a length of 10 km (```ETLength10km```), 15 km (```ETLength15km```) and 20 km (```ETLength20km```), in the **HFLF_cryo/** folder\n\n\n### ET-0000A-18.txt\n\nPublic [ET-D](https://arxiv.org/abs/1012.0908) sensnitivity curve. \n\nAvailable at [https://apps.et-gw.eu/tds/?content=3&r=14065](https://apps.et-gw.eu/tds/?content=3&r=14065). Notice that we kept only the first and last column of the file, corresponding to the frequencies and the total ET-D sensitivity, obtained combining the LF and HF instruments.\n\n### LVC_O1O2O3/\n\nThe folder contains ASDs for the LIGO and Virgo detectors during their O1, O2 and O3 observing runs, extracted in specific moment from actual data.\n\nAvailable at [https://dcc.ligo.org/P1800374/public/](https://dcc.ligo.org/P1800374/public/) for O1 and O2, [https://dcc.ligo.org/LIGO-P2000251/public](https://dcc.ligo.org/LIGO-P2000251/public) for O3a, and computed using [PyCBC](https://pycbc.org) around the times indicated in the caption of Fig. 2 of [https://arxiv.org/abs/2111.03606](https://arxiv.org/abs/2111.03606).\n\n### observing\\_scenarios\\_paper/\n\nASDs used for the paper [*Prospects for observing and localizing gravitational-wave transients with Advanced LIGO, Advanced Virgo and KAGRA*, KAGRA Collaboration, LIGO Scientific Collaboration and Virgo Collaboration](https://link.springer.com/article/10.1007/s41114-020-00026-9).\n\nAvailable at [https://dcc.ligo.org/LIGO-T2000012/public](https://dcc.ligo.org/LIGO-T2000012/public). \n\nThe folder contains ASDs for the Advanced LIGO, Advanced Virgo and KAGRA detectors during the O3, O4 and O5 observing runs.\n\n### unofficial\\_curves\\_all\\_dets/\n\nPublic ASDs for both the current and future generation of detectors (last update in January 2020). \n\nAvailable at [https://dcc.ligo.org/LIGO-T1500293/public](https://dcc.ligo.org/LIGO-T1500293/public), in the *curves\\_Jan\\_2020.zip* file.\n \nThe folder contains ASDs for:\n\n* Advanced LIGO and Advanced Virgo during both the O1, O2 and O3 runs, at design sensitivity and in the *Advanced plus* stage;\n* KAGRA;\n* LIGO Voyager;\n* ET-D;\n* CE1 and CE2.\n",
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