pixelpop 0.2.16

Powered by numpyro and jax, package for fitting the GW population with a nonparametric binning scheme, where bins are correlated with only their nearest neighbors. Meant for inferring the GW population distribution nonparametrically in higher dimensions.

PixelPop

Package for nonparameteric (AKA weakly modeled, data-driven) Bayesian inference of a gravitational wave population, built on JAX and numpyro. Aimed particularly at correlated nonparameteric inference in spaces with dimension 2-3.

This method works by binning the space into a cartesian grid, and inferring the log-rate density in each bin, each of which is a free parameter. Each bin is coupled to its nearest-neighbors using an intrinsic conditional-autoregressive (ICAR) model.

The dimension of the inference problem can become very large (e.g. 10^4 for a 2-dimensional space with a density of 100 bins along each axis), and we leverage auto-differentiation and GPU acceleration in JAX, as well as the efficient No-U-Turn HMC sampler in numpyro to sample the posterior.

Running PixelPop

Please see the example run scripts in the examples/ directory.

Attribution

Please cite Heinzel et al. (2025) if you use PixelPop in your research.

@article{Heinzel:2024jlc,
    author = "Heinzel, Jack and Mould, Matthew and {\'A}lvarez-L{\'o}pez, Sof{\'\i}a and Vitale, Salvatore",
    title = "{High resolution nonparametric inference of gravitational-wave populations in multiple dimensions}",
    eprint = "2406.16813",
    archivePrefix = "arXiv",
    primaryClass = "astro-ph.HE",
    doi = "10.1103/PhysRevD.111.063043",
    journal = "Phys. Rev. D",
    volume = "111",
    number = "6",
    pages = "063043",
    year = "2025"
}

Additionally, consider citing Heinzel et al. (2025) which applies PixelPop to GWTC-3

@article{Heinzel:2024hva,
    author = "Heinzel, Jack and Mould, Matthew and Vitale, Salvatore",
    title = "{Nonparametric analysis of correlations in the binary black hole population with LIGO-Virgo-KAGRA data}",
    eprint = "2406.16844",
    archivePrefix = "arXiv",
    primaryClass = "astro-ph.HE",
    doi = "10.1103/PhysRevD.111.L061305",
    journal = "Phys. Rev. D",
    volume = "111",
    number = "6",
    pages = "L061305",
    year = "2025"
},

and Alvarez-Lopez et al. (2025) which shows PixelPop can accurately recover the complex, multi-dimensional correlations in a realistic population-synthesis population.

@article{Alvarez-Lopez:2025ltt,
    author = "Alvarez-Lopez, Sofia and Heinzel, Jack and Mould, Matthew and Vitale, Salvatore",
    title = "{Nowhere left to hide: revealing realistic gravitational-wave populations in high dimensions and high resolution with PixelPop}",
    eprint = "2506.20731",
    archivePrefix = "arXiv",
    primaryClass = "astro-ph.HE",
    month = "6",
    year = "2025"
}