[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.
[submitted]
Sacc: Save All Correlations and Covariances
Zuntz, J.;
Slosar, A.;
Alonso, D.;
Becker, M.;
Broussard, A.;
McClintock, T.;
Nicola, A.;
Miyatake, H.;
Sanchez, J.;
Neveu, J.
SACC (Save All Correlations and Covariances) is a format and reference library for general storage
of summary statistic measurements for the Dark Energy Science Collaboration (DESC) within and from the Large Synoptic Survey Telescope (LSST) project's Dark Energy Science Collaboration.
[ascl:2409.014]
symbolic_pofk: Precise symbolic emulators of the linear and nonlinear matter power spectrum
symbolic_pofk provides simple Python functions and a Fortran90 routine for precise symbolic emulations of the linear and non-linear matter power spectra and for the conversion σ 8 ↔ A s as a function of cosmology. These can be easily copied, pasted, and modified to other languages. Outside of a tested k range, the fit includes baryons by default; however, this can be switched off.