Robustness of Neural Ratio and Posterior Estimators to Distributional Shifts for Population-level Dark Matter Analysis in Strong Gravitational Lensing
We investigate the robustness of neural ratio estimators (NREs) and sequential neural posterior estimators (SNPEs) to distributional shifts in the context of measuring the abundance of dark matter subhalos using strong gravitational lensing data. While these data-driven inference frameworks can be a...
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IOP Publishing
2025-01-01
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| Series: | The Astrophysical Journal |
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| Online Access: | https://doi.org/10.3847/1538-4357/adee20 |
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| author | Andreas Filipp Yashar Hezaveh Laurence Perreault-Levasseur |
| author_facet | Andreas Filipp Yashar Hezaveh Laurence Perreault-Levasseur |
| author_sort | Andreas Filipp |
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| description | We investigate the robustness of neural ratio estimators (NREs) and sequential neural posterior estimators (SNPEs) to distributional shifts in the context of measuring the abundance of dark matter subhalos using strong gravitational lensing data. While these data-driven inference frameworks can be accurate on test data from the same distribution as the training sets, in real applications, it is expected that simulated training data and true observational data will differ in their distributions. We explore the behavior of a trained NRE and trained SNPEs to estimate the population-level parameters of dark matter subhalos from a large sample of images of strongly lensed galaxies with test data presenting distributional shifts within and beyond the bounds of the training distribution in the nuisance parameters (e.g., the background source morphology). While our results show that NREs and SNPEs perform well when tested perfectly in distribution, they exhibit significant biases that often lead to not recovering the ground truth in the 3 σ interval when confronted with slight deviations from the examples seen in the training distribution. This indicates the necessity for caution when applying NREs and SNPEs to real astrophysical data, where high-dimensional underlying distributions are not perfectly known. |
| format | Article |
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| institution | Kabale University |
| issn | 1538-4357 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
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| series | The Astrophysical Journal |
| spelling | doaj-art-41bce4e343334a54b8faf1e5cadd42ae2025-08-20T06:01:48ZengIOP PublishingThe Astrophysical Journal1538-43572025-01-01989222610.3847/1538-4357/adee20Robustness of Neural Ratio and Posterior Estimators to Distributional Shifts for Population-level Dark Matter Analysis in Strong Gravitational LensingAndreas Filipp0https://orcid.org/0000-0003-4701-3469Yashar Hezaveh1https://orcid.org/0000-0002-8669-5733Laurence Perreault-Levasseur2https://orcid.org/0000-0003-3544-3939Ciela Institute , Montreal Institute for Astrophysics and Machine Learning, Montreal QC, Canada ; andreas.filipp@umontreal.ca; Mila—Quebec AI Institute , Montreal QC, Canada; Department of Physics, University of Montréal , Montréal QC, CanadaCiela Institute , Montreal Institute for Astrophysics and Machine Learning, Montreal QC, Canada ; andreas.filipp@umontreal.ca; Mila—Quebec AI Institute , Montreal QC, Canada; Department of Physics, University of Montréal , Montréal QC, Canada; Center for Computational Astrophysics, Flatiron Institute , NY, USA; Perimeter Institute for Theoretical Physics , Waterloo, Canada; Trottier Space Institute, McGill University , Montréal, CanadaCiela Institute , Montreal Institute for Astrophysics and Machine Learning, Montreal QC, Canada ; andreas.filipp@umontreal.ca; Mila—Quebec AI Institute , Montreal QC, Canada; Department of Physics, University of Montréal , Montréal QC, Canada; Center for Computational Astrophysics, Flatiron Institute , NY, USA; Perimeter Institute for Theoretical Physics , Waterloo, Canada; Trottier Space Institute, McGill University , Montréal, CanadaWe investigate the robustness of neural ratio estimators (NREs) and sequential neural posterior estimators (SNPEs) to distributional shifts in the context of measuring the abundance of dark matter subhalos using strong gravitational lensing data. While these data-driven inference frameworks can be accurate on test data from the same distribution as the training sets, in real applications, it is expected that simulated training data and true observational data will differ in their distributions. We explore the behavior of a trained NRE and trained SNPEs to estimate the population-level parameters of dark matter subhalos from a large sample of images of strongly lensed galaxies with test data presenting distributional shifts within and beyond the bounds of the training distribution in the nuisance parameters (e.g., the background source morphology). While our results show that NREs and SNPEs perform well when tested perfectly in distribution, they exhibit significant biases that often lead to not recovering the ground truth in the 3 σ interval when confronted with slight deviations from the examples seen in the training distribution. This indicates the necessity for caution when applying NREs and SNPEs to real astrophysical data, where high-dimensional underlying distributions are not perfectly known.https://doi.org/10.3847/1538-4357/adee20Strong gravitational lensingDark matterNeural networksGravitational lensingGalaxy dark matter halos |
| spellingShingle | Andreas Filipp Yashar Hezaveh Laurence Perreault-Levasseur Robustness of Neural Ratio and Posterior Estimators to Distributional Shifts for Population-level Dark Matter Analysis in Strong Gravitational Lensing The Astrophysical Journal Strong gravitational lensing Dark matter Neural networks Gravitational lensing Galaxy dark matter halos |
| title | Robustness of Neural Ratio and Posterior Estimators to Distributional Shifts for Population-level Dark Matter Analysis in Strong Gravitational Lensing |
| title_full | Robustness of Neural Ratio and Posterior Estimators to Distributional Shifts for Population-level Dark Matter Analysis in Strong Gravitational Lensing |
| title_fullStr | Robustness of Neural Ratio and Posterior Estimators to Distributional Shifts for Population-level Dark Matter Analysis in Strong Gravitational Lensing |
| title_full_unstemmed | Robustness of Neural Ratio and Posterior Estimators to Distributional Shifts for Population-level Dark Matter Analysis in Strong Gravitational Lensing |
| title_short | Robustness of Neural Ratio and Posterior Estimators to Distributional Shifts for Population-level Dark Matter Analysis in Strong Gravitational Lensing |
| title_sort | robustness of neural ratio and posterior estimators to distributional shifts for population level dark matter analysis in strong gravitational lensing |
| topic | Strong gravitational lensing Dark matter Neural networks Gravitational lensing Galaxy dark matter halos |
| url | https://doi.org/10.3847/1538-4357/adee20 |
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