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[ascl:2312.017] LimberJack.jl: Auto-differentiable methods for cosmology

LimberJack.jl performs cosmological analyses of 2 point auto- and cross-correlation measurements from galaxy clustering, CMB lensing and weak lensing data. Written in Julia, it obtains gradients for its outputs faster than traditional finite difference methods, making the code greatly synergistic with gradient-based sampling methods such as Hamiltonian Monte Carlo. LimberJack.jl can efficiently exploring parameter spaces with hundreds of dimensions.

Code site:
https://github.com/jaimerzp/limberjack.jl
Used in:
https://ui.adsabs.harvard.edu/abs/2020MNRAS.491.4768R
Described in:
https://ui.adsabs.harvard.edu/abs/2023arXiv231008306R
Bibcode:
2023ascl.soft12017R

Views: 390

ascl:2312.017
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