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SelfiSys: Assess the Impact of Systematic Effects in Galaxy Surveys
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===================================================================
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.. image:: https://img.shields.io/badge/astro--ph.CO-arxiv%3A2412.04443-B31B1B.svg
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:target: https://arxiv.org/abs/2412.04443
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:alt: arXiv
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.. image:: https://img.shields.io/github/v/tag/hoellin/selfisys_public.svg?label=version
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:target: https://github.com/hoellin/selfisys_public/releases
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:alt: GitHub Release
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.. image:: https://img.shields.io/github/last-commit/hoellin/selfisys_public
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:target: https://github.com/hoellin/selfisys_public/commits/main
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:alt: Last Commit
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.. image:: https://img.shields.io/badge/License-GPLv3-blue.svg
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:target: https://github.com/hoellin/selfisys_public/blob/main/LICENSE
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:alt: License
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**SelfiSys** is a Python package designed to address the issue of model misspecification in field-based, implicit likelihood cosmological inference.
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It leverages the inferred initial matter power spectrum, enabling a thorough diagnosis of systematic effects in large-scale spectroscopic galaxy surveys.
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Key Features
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------------
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- **Custom hidden-box forward models**
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We provide a `HiddenBox` class to simulate realistic spectroscopic galaxy surveys. It accommodates fully non-linear gravitational evolution, and incorporates multiple systematic effects observed in real-world survey, e.g., misspecified galaxy bias, survey mask, selection functions, dust extinction, line interlopers, or inaccurate gravity solver.
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- **Diagnosis of systematic effects**
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Diagnose the impact of systematic effects using the inferred initial matter power spectrum, prior to performing cosmological inference.
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- **Cosmological inference**
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Perform inference of cosmological parameters using Approximate Bayesian Computation (ABC) with a Population Monte Carlo (PMC) sampler.
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For practical examples demonstrating how to use SelfiSys, visit the `SelfiSys Examples Repository <https://github.com/hoellin/selfisys_examples>`_.
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References
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----------
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If you use the SelfiSys package in your research, please cite the following paper and feel free to `contact the authors <mailto:tristan.hoellinger@iap.fr>`_ for feedback, collaboration opportunities, or other inquiries.
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**Diagnosing Systematic Effects Using the Inferred Initial Power Spectrum**
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*Hoellinger, T. and Leclercq, F., arXiv e-prints*, 2024
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`arXiv:2412.04443 <https://arxiv.org/abs/2412.04443>`_
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`[astro-ph.CO] <https://arxiv.org/abs/2412.04443>`_
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`[ADS] <https://ui.adsabs.harvard.edu/abs/arXiv:2412.04443>`_
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`[pdf] <https://arxiv.org/pdf/2412.04443>`_
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Contributors
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------------
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- **Tristan Hoellinger**
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`tristan.hoellinger@iap.fr <mailto:tristan.hoellinger@iap.fr>`_
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Principal developer and maintainer, Institut d’Astrophysique de Paris (IAP).
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License
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-------
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This software is distributed under the GPLv3 Licence. Please review the `LICENSE <https://github.com/hoellin/selfisys_public/blob/main/LICENSE>`_ file in the repository to understand the terms of use and ensure compliance. By downloading and using this software, you agree to the terms of the licence.
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Requirements
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------------
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The code is written in Python 3.10 and depends on the following packages:
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- `pySELFI <https://pyselfi.readthedocs.io/en/latest/>`_: Python implementation of the Simulator Expansion for Likelihood-Free Inference.
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- `Simbelmynë <https://simbelmyne.readthedocs.io/en/latest/>`_: A hierarchical probabilistic simulator for generating synthetic galaxy survey data.
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- `ELFI <https://elfi.readthedocs.io/en/latest/>`_: A statistical software package for likelihood-free inference, implementing Approximate Bayesian Computation (ABC) with a Population Monte Carlo (PMC) sampler.
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A comprehensive list of dependencies, along with installation instructions, will be provided in a future release.
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.. toctree::
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:maxdepth: 2
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:caption: API Documentation
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selfisys.hiddenbox
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selfisys.normalise_hb
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selfisys.prior
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selfisys.selection_functions
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selfisys.selfi_interface
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selfisys.sbmy_interface
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selfisys.grf
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selfisys.utils
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.. toctree::
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:maxdepth: 2
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:caption: Contribute
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../../CONTRIBUTING.md
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.. toctree::
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:maxdepth: 2
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:caption: References
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../../REFERENCES.md |