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|a| is the main component of the Bayesian Large Scale Structure inference
pipeline. The present version of the ARES framework is 2.1. Please consult
:ref: `CHANGES overview` for an overview of the different improvements over the
different versions.
|a| is written in C++14 and has been parallelized with OpenMP and MPI. It currently compiles with major compilers (gcc, intel, clang).
Table of contents
-----------------
.. toctree ::
:maxdepth: 1
:caption: Theory
theory/ARES
theory/BORG
theory/ARES&BORG_FFT_normalization
.. toctree ::
:maxdepth: 1
:caption: User documentation
changes
user/building
user/inputs
user/outputs
user/running
user/postprocessing
user/extras
user/clusters
.. toctree ::
:maxdepth: 1
:caption: Developer documentation
developer/development_with_git
developer/code_architecture
developer/life_cycles_of_objects
developer/ares_modules
developer/code_tutorials
developer/contributing_to_this_documentation
developer/copyright_and_authorship
Citing
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^^^^^^
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.. sectionauthor :: Florent Leclercq (last update: 20 June 2023)
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The following section gives the references for the |ares|, |hades|, and |borg| algorithms and codes (including their direct application to real data, but excluding further scientific exploitation).
For the full list of publications from the Aquila consortium, please check the
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`Aquila website <https://aquila-consortium.org/publications/> `_ .
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ARES
''''
References for the |ares| algorithm (linear data model, Wiener filtering with Gibbs sampling/messenger field)
are the following papers:
* J. Jasche, F. S. Kitaura, B. D. Wandelt, T. A. Enßlin, *Bayesian power-spectrum inference for large-scale structure data* ,
`Monthly Notices of the Royal Astronomical Society (2010) 406, 60 <http://dx.doi.org/10.1111/j.1365-2966.2010.16610.x> `_ ;
`arXiv:0911.2493 <http://arxiv.org/pdf/0911.2493> `_
(linear data model, Wiener filtering and power spectrum inference with Gibbs sampling)
* J. Jasche, B. D. Wandelt, *Methods for Bayesian Power Spectrum Inference with Galaxy Surveys* ,
`The Astrophysical Journal (2013) 779, 15 <http://dx.doi.org/10.1088/0004-637X/779/1/15> `_ ;
`arXiv:1306.1821 <http://arxiv.org/pdf/1306.1821> `_
(luminosity-dependent galaxy bias, calibration of noise levels, reversible jump algorithm)
* J. Jasche, G. Lavaux, *Matrix-free large-scale Bayesian inference in cosmology* ,
`Monthly Notices of the Royal Astronomical Society (2015) 447, 1204 <http://dx.doi.org/10.1093/mnras/stu2479> `_ ;
`arXiv:1402.1763 <http://arxiv.org/pdf/1402.1763> `_
(inference with messenger field)
* J. Jasche, G. Lavaux, *Bayesian power spectrum inference with foreground and target contamination treatment* ,
`Astronomy and Astrophysics (2017) 606, A37 <http://dx.doi.org/10.1051/0004-6361/201730909> `_ ;
`arXiv:1706.08971 <http://arxiv.org/pdf/1706.08971> `_
(joint inference of density field and known foregrounds)
HADES
'''''
References for the |hades| algorithm (log-normal data model, Hamiltonian Monte Carlo sampling, photometric redshift inference) are the following papers:
* J. Jasche, F. S. Kitaura, *Fast Hamiltonian sampling for large-scale structure inference* ,
`Monthly Notices of the Royal Astronomical Society (2010) 407, 29 <http://dx.doi.org/10.1111/j.1365-2966.2010.16897.x> `_ ;
`arXiv:0911.2496 <http://arxiv.org/pdf/0911.2496> `_
(HMC method paper)
* J. Jasche, F. S. Kitaura, C. Li, T. A. Enßlin, *Bayesian non-linear large-scale structure inference of the Sloan Digital
Sky Survey Data Release 7*,
`Monthly Notices of the Royal Astronomical Society (2010) 409, 355 <http://dx.doi.org/10.1111/j.1365-2966.2010.17313.x> `_ ;
`arXiv:0911.2498 <http://arxiv.org/pdf/0911.2498> `_
(data application with log-normal data model)
* J. Jasche, B. D. Wandelt, *Bayesian inference from photometric redshift surveys* ,
`Monthly Notices of the Royal Astronomical Society (2012) 425, 1042 <http://dx.doi.org/10.1111/j.1365-2966.2012.21423.x> `_ ;
`arXiv:1106.2757 <http://arxiv.org/pdf/1106.2757> `_
(method paper: joint inference of density and photometric redshifts)
BORG
''''
Methodological papers that shall be cited when referring to the |borg| algorithm (inference with a structure formation model and Hamiltonian Monte Carlo) are the following:
* J. Jasche, B. D. Wandelt, *Bayesian physical reconstruction of initial conditions from large-scale structure surveys* ,
`Monthly Notices of the Royal Astronomical Society (2013) 432, 894 <http://dx.doi.org/10.1093/mnras/stt449> `_ ;
`arXiv:1203.3639 <http://arxiv.org/pdf/1203.3639> `_
(original BORG method paper with differentiable LPT data model and HMC)
* J. Jasche, F. Leclercq, B. D. Wandelt, *Past and present cosmic structure in the SDSS DR7 main sample* ,
`Journal of Cosmology and Astroparticle Physics (2015) 01, 036 <http://dx.doi.org/10.1088/1475-7516/2015/01/036> `_ ;
`arXiv:1409.6308 <http://arxiv.org/pdf/1409.6308> `_
(luminosity-dependent galaxy bias, power-law bias model, calibration of noise levels)
* G. Lavaux, J. Jasche, *Unmasking the masked Universe: the 2M++ catalogue through Bayesian eyes* ,
`Monthly Notices of the Royal Astronomical Society (2016) 455, 3169 <http://dx.doi.org/10.1093/mnras/stv2499> `_ ;
`arXiv:1509.05040 <http://arxiv.org/pdf/1509.05040> `_
(data model with redshift-space distortions)
* J. Jasche, G. Lavaux, *Physical Bayesian modelling of the non-linear matter distribution: New insights into the nearby universe* ,
`Astronomy and Astrophysics (2019) 625, A64 <http://dx.doi.org/10.1051/0004-6361/201833710> `_ ;
`arXiv:1806.11117 <http://arxiv.org/pdf/1806.11117> `_
(BORGPM: particle-mesh data model, observer velocity sampling, "heating up" the likelihood)
* G. Lavaux, J. Jasche, F. Leclercq, *Systematic-free inference of the cosmic matter density field from SDSS3-BOSS data* ,
`arXiv:1909.06396 <http://arxiv.org/pdf/1909.06396> `_
(data model with light-cone effects, quadratic form bias model)
Data application papers of |borg| are the following:
* J. Jasche, F. Leclercq, B. D. Wandelt, *Past and present cosmic structure in the SDSS DR7 main sample* ,
`Journal of Cosmology and Astroparticle Physics (2015) 01, 036 <http://dx.doi.org/10.1088/1475-7516/2015/01/036> `_ ;
`arXiv:1409.6308 <http://arxiv.org/pdf/1409.6308> `_
(application to SDSS DR7 main galaxy sample)
* G. Lavaux, J. Jasche, *Unmasking the masked Universe: the 2M++ catalogue through Bayesian eyes* ,
`Monthly Notices of the Royal Astronomical Society (2016) 455, 3169 <http://dx.doi.org/10.1093/mnras/stv2499> `_ ;
`arXiv:1509.05040 <http://arxiv.org/pdf/1509.05040> `_
(application to 2M++, LPT data model)
* J. Jasche, G. Lavaux, *Physical Bayesian modelling of the non-linear matter distribution: New insights into the nearby universe* ,
`Astronomy and Astrophysics (2019) 625, A64 <http://dx.doi.org/10.1051/0004-6361/201833710> `_ ;
`arXiv:1806.11117 <http://arxiv.org/pdf/1806.11117> `_
(application to 2M++, PM data model)
* G. Lavaux, J. Jasche, F. Leclercq, *Systematic-free inference of the cosmic matter density field from SDSS3-BOSS data* ,
`arXiv:1909.06396 <http://arxiv.org/pdf/1909.06396> `_
(application to SDSS3 BOSS, LPT data model)
Additional papers extend the |borg| algorithm and shall be cited depending on the context. The list includes (but may not be limited to):
* Foregrounds/Systematic effects:
* J. Jasche, G. Lavaux, *Bayesian power spectrum inference with foreground and target contamination treatment* ,
`Astronomy and Astrophysics (2017) 606, A37 <http://dx.doi.org/10.1051/0004-6361/201730909> `_ ;
`arXiv:1706.08971 <http://arxiv.org/pdf/1706.08971> `_
(joint inference of density field and known foregrounds)
* N. Porqueres, D. Kodi Ramanah, J. Jasche, G. Lavaux, *Explicit Bayesian treatment of unknown foreground contaminations
in galaxy surveys*,
`Astronomy and Astrophysics (2019) 624, A115 <http://dx.doi.org/10.1051/0004-6361/201834844> `_ ;
`arXiv:1812.05113 <http://arxiv.org/pdf/1812.05113> `_
(robust likelihood for unknown foregrounds effects)
* Cosmic expansion model (Alcock-Paczynski effect):
* D. Kodi Ramanah, G. Lavaux, J. Jasche, B. Wandelt, *Cosmological inference from Bayesian forward modelling
of deep galaxy redshift surveys*,
`Astronomy and Astrophysics (2019) 621, A69 <http://dx.doi.org/10.1051/0004-6361/201834117> `_ ;
`arXiv:1808.07496 <http://arxiv.org/pdf/1808.07496> `_
(Alcock-Paczynski expansion test)
* Lyman-α
* N. Porqueres, J. Jasche, G. Lavaux, T. Enßlin, *Inferring high-redshift large-scale structure dynamics from the Lyman-α ,
`Astronomy and Astrophysics (2019) 630, A151 <http://dx.doi.org/10.1051/0004-6361/201936245> `_ ;
`arXiv:1907.02973 <http://arxiv.org/pdf/1907.02973> `_ (Lyman alpha data model)
* N. Porqueres, O. Hahn, J. Jasche, G. Lavaux, *A hierarchical field-level inference approach to reconstruction from
sparse Lyman-α
`Astronomy and Astrophysics (2020) 642, A139 <http://dx.doi.org/10.1051/0004-6361/202038482> `_ ;
`arXiv:2005.12928 <http://arxiv.org/pdf/2005.12928> `_
* Weak lensing (cosmic shear):
* N. Porqueres, A. Heavens, D. Mortlock, G. Lavaux, *Bayesian forward modelling of cosmic shear data* ,
`Monthly Notices of the Royal Astronomical Society (2021) 502, 3035 <http://dx.doi.org/10.1093/mnras/stab204> `_ ;
`arXiv:2011.07722 <http://arxiv.org/pdf/2011.07722> `_
(original BORG-WL paper)
* N. Porqueres, A. Heavens, D. Mortlock, G. Lavaux, *Lifting weak lensing degeneracies with a field-based likelihood* ,
`Monthly Notices of the Royal Astronomical Society (2022) 509, 3194 <http://dx.doi.org/10.1093/mnras/stab3234> `_ ;
`arXiv:2108.04825 <http://arxiv.org/pdf/2108.04825> `_
(cosmological parameter inference)
* N. Porqueres, A. Heavens, D. Mortlock, G. Lavaux, T. L. Makinen, *Field-level inference of cosmic shear with intrinsic
alignments and baryons*,
`arXiv:2304.04785 <http://arxiv.org/pdf/2304.04785> `_
(intrinsic alignments and baryons)
* Cosmic velocity field:
* S. S. Boruah, G. Lavaux, M. J. Hudson, *Bayesian reconstruction of dark matter distribution from peculiar velocities:
accounting for inhomogeneous Malmquist bias*,
`Monthly Notices of the Royal Astronomical Society (2022) 517, 4529 <http://dx.doi.org/10.1093/mnras/stac2985> `_ ;
`arXiv:2111.15535 <http://arxiv.org/pdf/2111.15535> `_
(linear model for the velocity field, inhomogeneous Malmquist bias, observational effects)
* J. Prideaux-Ghee, F. Leclercq, G. Lavaux, A. Heavens, J. Jasche, *Field-Based Physical Inference From Peculiar Velocity Tracers* ,
`Monthly Notices of the Royal Astronomical Society (2023) 518, 4191 <http://dx.doi.org/10.1093/mnras/stac3346> `_ ;
`arXiv:2204.00023 <http://arxiv.org/pdf/2204.00023> `_
(LPT structure formation model in the data model)
* Primordial non-Gaussianity:
* A. Andrews, J. Jasche, G. Lavaux, F. Schmidt, *Bayesian field-level inference of primordial
non-Gaussianity using next-generation galaxy surveys*,
`Monthly Notices of the Royal Astronomical Society (2023) 520, 5746 <http://dx.doi.org/10.1093/mnras/stad432> `_ ;
`arXiv:2203.08838 <http://arxiv.org/pdf/2203.08838> `_ (local fNL sampling)
* Photometric redshift inference:
* E. Tsaprazi, J. Jasche, G. Lavaux, F. Leclercq, *Higher-order statistics of the large-scale structure from photometric redshifts* ,
`arXiv:2301.03581 <http://arxiv.org/pdf/2301.03581> `_
(photometric redshift sampling with a structure formation model)
* Effective Field Theory (EFT) bias model and likelihood:
* F. Schmidt, F. Elsner, J. Jasche, N. M. Nguyen, G. Lavaux, *A rigorous EFT-based forward model for large-scale structure* ,
`Journal of Cosmology and Astroparticle Physics (2019) 01, 042 <http://dx.doi.org/10.1088/1475-7516/2019/01/042> `_ ;
`arXiv:1808.02002 <http://arxiv.org/pdf/1808.02002> `_ (EFT likelihood)
* F. Schmidt, G. Cabass, J. Jasche, G. Lavaux, *Unbiased cosmology inference from biased tracers using the EFT likelihood* ,
`Journal of Cosmology and Astroparticle Physics (2020) 11, 008 <http://dx.doi.org/10.1088/1475-7516/2020/11/008> `_ ;
`arXiv:2004.06707 <http://arxiv.org/pdf/2004.06707> `_ (biased tracers with EFT bias model and likelihood)
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Acknowledgements
----------------
This work has been funded by the following grants and institutions over the
years:
* The DFG cluster of excellence "Origin and Structure of the Universe"
(http://www.universe-cluster.de).
* Institut Lagrange de Paris (grant ANR-10-LABX-63, http://ilp.upmc.fr) within
the context of the Idex SUPER subsidized by the French government through
the Agence Nationale de la Recherche (ANR-11-IDEX-0004-02).
* BIG4 (ANR-16-CE23-0002) (https://big4.iap.fr)
* The "Programme National de Cosmologie et Galaxies" (PNCG, CNRS/INSU)
* Through the grant code ORIGIN, it has received support from
the "Domaine d'Interet Majeur (DIM) Astrophysique et Conditions d'Apparitions
de la Vie (ACAV)" from Ile-de-France region.
* The Starting Grant (ERC-2015-STG 678652) "GrInflaGal" of the European Research Council.
.. Indices and tables
.. ==================
..
.. * :ref:`genindex`
.. * :ref:`modindex`
.. * :ref:`search`
.. Order of headings used throughout the documentation:
######### part
***** *** * chapter
========= sections
--------- subsections
~~~~~~~~~ subsubsections
^^^^^^^^^
'''''''''
.. toctree-filt ::
:maxdepth: 1
:caption: Python reference documentation
:aquila:pythonref.rst
