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83 lines
No EOL
4.6 KiB
Markdown
# SelfiSys: Assess the Impact of Systematic Effects in Galaxy Surveys
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[](https://arxiv.org/abs/2412.04443)
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[](https://github.com/hoellin/selfisys_public/blob/main/LICENSE)
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[](https://github.com/hoellin/selfisys_public)
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[](https://github.com/hoellin/selfisys_public/commits/main)
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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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- **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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---
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## Documentation
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The documentation, including a detailed API reference, is available at [hoellin.github.io/selfisys_public](https://hoellin.github.io/selfisys_public/).
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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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## Contributors
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- **Tristan Hoellinger**, [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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For information on contributing, refer to [CONTRIBUTING.md](CONTRIBUTING.md).
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## References
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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., 2024*
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[arXiv:2412.04443](https://arxiv.org/abs/2412.04443) [[astro-ph.CO]](https://arxiv.org/abs/2412.04443) [[ADS]](https://ui.adsabs.harvard.edu/abs/arXiv:2412.04443) [[pdf]](https://arxiv.org/pdf/2412.04443)
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BibTeX entry for citation:
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```bibtex
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@ARTICLE{hoellinger2024diagnosing,
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author = {Hoellinger, Tristan and Leclercq, Florent},
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title = "{Diagnosing Systematic Effects Using the Inferred Initial Power Spectrum}",
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journal = {arXiv e-prints},
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keywords = {Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics},
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year = 2024,
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month = dec,
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eid = {arXiv:2412.04443},
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pages = {arXiv:2412.04443},
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doi = {10.48550/arXiv.2412.04443},
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archivePrefix = {arXiv},
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eprint = {2412.04443},
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primaryClass = {astro-ph.CO},
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adsurl = {https://ui.adsabs.harvard.edu/abs/2024arXiv241204443H},
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adsnote = {Provided by the SAO/NASA Astrophysics Data System}
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}
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```
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## Requirements
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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 in particular Approximate Bayesian Computation (ABC) with a Population Monte Carlo (PMC) sampler.
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A comprehensive list of dependencies, including version specifications to ensure reproducibility, will be provided in a yaml file, along with installation instructions, in a future release.
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---
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## License
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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. |