Fortifying gravitational-wave population inference with normalizing flows

[2606.14229] Fortifying gravitational-wave population inference with normalizing flows Skip to main content Search Submit Donate Log in Astrophysics > High Energy Astrophysical Phenomena arXiv:2606.14229 (astro-ph) [Submitted on 12 Jun 2026 ( v1 ), last revised 24 Jul 2026 (this version, v2)] Title: Fortifying gravitational-wave population inference with normalizing flows Authors: Christian Adamcewicz , Hugh McDougall , Paul D. Lasky , Eric Thrane View PDF HTML (experimental) Abstract: As the LIGO-Virgo-KAGRA collaboration's (LVK's) gravitational-wave transient catalog grows, we are learning a wealth of information from the population properties of binary black hole mergers. Events in the catalog are represented with posterior samples describing the astrophysical parameters for each event. Population studies combine these samples to measure the distribution of astrophysical parameters such as black hole masses and spins. However, the posterior-sample representation of each event is only approximate. We construct a mock population with masses drawn from an astrophysically-motivated distribution with sharp features. Using this, we demonstrate that when $\gtrsim 300$ events are combined, even with each event's posterior represented by $1 \times 10^4 {-} 2 \times 10^4$ samples, the numerical error can become large enough that the resulting population inference is unreliable. We consider two solutions. In the short term, we show that nested samples (already produced by LVK analyses) can be used to more accurately describe each event in population studies. But this will only grant a temporary reprieve until the nested-sample representation becomes inadequate. In the longer term, we propose to represent each event with a normalizing flow. In order to represent each event with sufficient accuracy, each normalizing flow can be used to generate an arbitrarily large number of new posterior samples with a significantly reduced computational cost relative to traditional sampling methods. When compared to nested sampling, our normalizing flows produce posterior draws with a median of $\approx 80\%$ fewer likelihood evaluations per sample, while also providing greater opportunity for parallelization. We believe refinement of normalizing flow architectures and training techniques in future works could further reduce this per-sample cost significantly. Comments: 23 pages, 13 figures Subjects: High Energy Astrophysical Phenomena (astro-ph.HE) Cite as: arXiv:2606.14229 [astro-ph.HE] (or arXiv:2606.14229v2 [astro-ph.HE] for this version) https://doi.org/10.48550/arXiv.2606.14229 Focus to learn more arXiv-issued DOI via DataCite Related DOI : https://doi.org/10.1103/qq4j-1nx6 Focus to learn more DOI(s) linking to related resources Submission history From: Christian Adamcewicz [ view email ] [v1] Fri, 12 Jun 2026 08:11:12 UTC (1,458 KB) [v2] Fri, 24 Jul 2026 07:15:23 UTC (1,985 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: astro-ph.HE < prev | next > new | recent | 2026-06 Change to browse by: astro-ph References & Citations INSPIRE HEP SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) IArxiv recommender toggle IArxiv Recommender ( What is IArxiv? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. 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