ARES is the main component of the Bayesian Large Scale Structure inference pipeline. The present version of the ARES framework is 2.1. Please consult Release notes for an overview of the different improvements over the different versions.

ARES 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

Theory

• Introduction to ARES
• Introduction to BORG
• FFT normalization in ARES/BORG

User documentation

• Release notes
• Building
• Inputs
• Outputs
• Running the executables
• Postprocessing
• Extra modules
• Clusters

Developer documentation

• Development with git
• Code architecture
• Code architecture
• ARES modules
• Code tutorials
• Contributing to this documentation
• Copyright and authorship

Citing

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 Aquila website.

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; arXiv: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; arXiv: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; arXiv: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; arXiv: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; arXiv: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; arXiv: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; arXiv: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; arXiv: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; arXiv: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; arXiv: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; arXiv: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 (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; arXiv: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; arXiv: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; arXiv: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 (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; arXiv: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; arXiv: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; arXiv:1808.07496 (Alcock-Paczynski expansion test)

• Lyman-α forest:

  • N. Porqueres, J. Jasche, G. Lavaux, T. Enßlin, Inferring high-redshift large-scale structure dynamics from the Lyman-α forest, Astronomy and Astrophysics (2019) 630, A151; arXiv: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 forest data, Astronomy and Astrophysics (2020) 642, A139; arXiv: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; arXiv: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; arXiv: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 (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; arXiv: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; arXiv: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; arXiv: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 (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; arXiv: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; arXiv:2004.06707 (biased tracers with EFT bias model and likelihood)

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.

Python reference documentation

• PyBORG root module
• Cosmology computations
• Likelihood and associated structures
• Samplers and sampler algorithms
• BORG forward model
• Standard BORG forward models
• Velocity field computations