Diffhalos generates statistically accurate Monte-Carlo and quasi-Monte-Carlo lightcones of halos, subhalos and Diffmah mass-assembly histories, enabling autodiff gradients of the mass functions.
halox: Dark matter halo properties and large-scale structure calculations using JAX
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abstract
Dark matter halos are fundamental structures in cosmology, forming the gravitational potential wells hosting galaxies and clusters of galaxies. Their properties and statistical distribution (including the halo mass function) are invaluable tools to infer the fundamental properties of the Universe. The \texttt{halox} package is a JAX-powered Python library enabling differentiable and accelerated computations of key properties of dark matter halos, and of the halo mass function. The automatic differentiation capabilities of \texttt{halox} enable its usage in gradient-based workflows, e.g. in efficient Hamiltonian Monte Carlo sampling or machine learning applications.
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astro-ph.GA 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
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Diffhalos: A Generative Model of Cosmological Lightcones of Dark Matter Halos
Diffhalos generates statistically accurate Monte-Carlo and quasi-Monte-Carlo lightcones of halos, subhalos and Diffmah mass-assembly histories, enabling autodiff gradients of the mass functions.