Synference¶

Synference is an open-source python package for SED fitting of photometric and spectroscopic data using Simulation-Based Inference (SBI) methods. It is part of the broader Synthesizer project, which aims to provide tools for generating and analyzing synthetic astronomical observables.

The aim of Synference is to make the SBI approach to SED fitting more accessible to those without experience with machine learning or SBI. Synference provides a \(10^3-10^5\) speed-up over conventional Bayesian SED fitting tools whilst retaining robust Bayesian posteriors, and Synthesizer provides a rapid and flexible forward model for generating training data (or you can bring your own!).

This documentation provides a broad overview of the various components in Synference and how they interact. The Getting Started guide contains download and installation instructions, as well as an overview of the code.

For detailed examples of what Synference can do, take a look at the Examples page. A full description of the code base is provided in the API.

Contents¶

Citation & Acknowledgement¶

Please cite all of the following papers if you use Synference in your research. Harvey et al. 2025 (in prep.) introduces the Synference code, Lovell et al. 2025 & Roper et al. 2025 introduce the Synthesizer code, and Ho et al. 2024 introduces the LtU-ILI framework.

@ARTICLE{2025OJAp....8E.152L,
      author = {{Lovell}, Christopher C. and {Roper}, William J. and {Vijayan}, Aswin P. and {Wilkins}, Stephen M. and {Newman}, Sophie and {Seeyave}, Louise},
      title = "{Synthesizer: a Software Package for Synthetic Astronomical Observables}",
      journal = {The Open Journal of Astrophysics},
keywords = {Instrumentation and Methods for Astrophysics, Cosmology and Nongalactic Astrophysics, Astrophysics of Galaxies},
      year = 2025,
      month = oct,
      volume = {8},
      eid = {152},
      pages = {152},
      doi = {10.33232/001c.145766},
archivePrefix = {arXiv},
      eprint = {2508.03888},
primaryClass = {astro-ph.IM},
      adsurl = {https://ui.adsabs.harvard.edu/abs/2025OJAp....8E.152L},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

@ARTICLE{2025arXiv250615811R,
   author = {{Roper}, Will J. and {Lovell}, Christopher and {Vijayan}, Aswin and {Wilkins}, Stephen and {Akins}, Hollis and {Berger}, Sabrina and {Sant Fournier}, Connor and {Harvey}, Thomas and {Iyer}, Kartheik and {Leonardi}, Marco and {Newman}, Sophie and {Pautasso}, Borja and {Perry}, Ashley and {Seeyave}, Louise and {Sommovigo}, Laura},
    title = "{Synthesizer: Synthetic Observables For Modern Astronomy}",
  journal = {arXiv e-prints},
 keywords = {Instrumentation and Methods for Astrophysics, Astrophysics of Galaxies},
     year = 2025,
    month = jun,
      eid = {arXiv:2506.15811},
    pages = {arXiv:2506.15811},
archivePrefix = {arXiv},
       eprint = {2506.15811},
 primaryClass = {astro-ph.IM},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2025arXiv250615811R},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

@ARTICLE{2024OJAp....7E..54H,
author = {{Ho}, Matthew and {Bartlett}, Deaglan J. and {Chartier}, Nicolas and {Cuesta-Lazaro}, Carolina and {Ding}, Simon and {Lapel}, Axel and {Lemos}, Pablo and {Lovell}, Christopher C. and {Makinen}, T. Lucas and {Modi}, Chirag and {Pandya}, Viraj and {Pandey}, Shivam and {Perez}, Lucia A. and {Wandelt}, Benjamin and {Bryan}, Greg L.},
title = "{LtU-ILI: An All-in-One Framework for Implicit Inference in Astrophysics and Cosmology}",
journal = {The Open Journal of Astrophysics},
keywords = {Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Astrophysics of Galaxies, Computer Science - Machine Learning},
   year = 2024,
month = jul,
volume = {7},
   eid = {54},
pages = {54},
   doi = {10.33232/001c.120559},
archivePrefix = {arXiv},
      eprint = {2402.05137},
primaryClass = {astro-ph.IM},
      adsurl = {https://ui.adsabs.harvard.edu/abs/2024OJAp....7E..54H},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}

Contributing¶

Please see here for contribution guidelines.

Primary Contributors¶

Author:

License¶

Synference is free software made available under the GNU General Public License v3.0. For details see the LICENSE.