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[ascl:1010.030]
CosmicEmu: Cosmic Emulator for the Dark Matter Power Spectrum

Lawrence, Earl; Heitmann, Katrin; White, Martin; Higdon, David; Wagner, Christian; Habib, Salman; Williams, Brian

Many of the most exciting questions in astrophysics and cosmology, including the majority of observational probes of dark energy, rely on an understanding of the nonlinear regime of structure formation. In order to fully exploit the information available from this regime and to extract cosmological constraints, accurate theoretical predictions are needed. Currently such predictions can only be obtained from costly, precision numerical simulations. The "Coyote Universe'' simulation suite comprises nearly 1,000 N-body simulations at different force and mass resolutions, spanning 38 wCDM cosmologies. This large simulation suite enabled construct of a prediction scheme, or emulator, for the nonlinear matter power spectrum accurate at the percent level out to k~1 h/Mpc. This is the first cosmic emulator for the dark matter power spectrum.

[ascl:1512.016]
ZeldovichRecon: Halo correlation function using the Zeldovich approximation

ZeldovichRecon computes the halo correlation function using the Zeldovich approximation. It includes 3 variants: 1.) zelrecon.cpp, which computes the various contributions to the correlation function; 2.) zelrecon_ctypes.cpp, which is designed to be called from Python using the ctypes library; and 3.) a version which implements the "ZEFT" formalism of "A Lagrangian effective field theory" [arxiv:1506.05264] including the alpha term described in that paper.

[ascl:1607.017]
BoxRemap: Volume and local structure preserving mapping of periodic boxes

BoxRemap remaps the cubical domain of a cosmological simulation into simple non-cubical shapes. It can be used for on-the-fly remappings of the simulation geometry and is volume-preserving; remapped geometry has the same volume V = L3 as the original simulation box. The remappings are structure-preserving (local neighboring structures are mapped to neighboring places) and one-to-one, with every particle/halo/galaxy/etc. appearing once and only once in the remapped volume.

[ascl:1708.030]
GAMBIT: Global And Modular BSM Inference Tool

GAMBIT Collaboration; Athron, Peter; Balazs, Csaba; Bringmann, Torsten; Buckley, Andy; Chrząszcz, Marcin; Conrad, Jan; Cornell, Jonathan M.; Dal, Lars A.; Dickinson, Hugh; Edsjö, Joakim; Farmer, Ben; Jackson, Paul; Krislock, Abram; Kvellestad, Anders; Lundberg, Johan; McKay, James; Mahmoudi, Farvah; Martinez, Gregory D.; Putze, Antje Raklev, Are; Ripken, Joachim; Rogan, Christopher; Saavedra, Aldo; Savage, Christopher; Scott, Pat; Seo, Seon-Hee; Serra, Nicola; Weniger, Christoph; White, Martin; Wild, Sebastian

GAMBIT (Global And Modular BSM Inference Tool) performs statistical global fits of generic physics models using a wide range of particle physics and astrophysics data. Modules provide native simulations of collider and astrophysics experiments, a flexible system for interfacing external codes (the backend system), a fully featured statistical and parameter scanning framework, and additional tools for implementing and using hierarchical models.

[ascl:2011.029]
DarkBit: Dark matter constraints calculator

Bringmann, Torsten; Conrad, Jan; Cornell, Jonathan M.; Dal, Lars A.; Edsjö, Joakim; Farmer, Ben; Kahlhoefer, Felix; Kvellestad, Anders; Putze, Antje; Savage, Christopher; Scott, Pat; Weniger, Christoph; White, Martin; Wild, Sebastian

DarkBit computes dark matter constraints on extensions to the Standard Model of particle physics. Written in the GAMBIT (ascl:1708.030) framework, it seamlessly integrates with other tools in the statistical fitting framework; it is also available as a standalone tool. It offers a signal yield calculator for gamma-ray observations, provides likelihoods for arbitrary combinations of spin-independent and spin-dependent scattering processes, and provides a general solution for studying complex particle physics models that predict dark matter annihilation to a multitude of final states.

[ascl:2011.030]
DDCalc: Dark matter direct detection phenomenology package

Bringmann, Torsten; Conrad, Jan; Cornell, Jonathan M.; Dal, Lars A.; Edsjö, Joakim; Farmer, Ben; Kahlhoefer, Felix; Kvellestad, Anders; Putze, Antje; Savage, Christopher; Scott, Pat; Weniger, Christoph; White, Martin; Wild, Sebastian

DDCalc performs various dark matter direct detection calculations, including signal rate predictions, constraints on light DM, and likelihoods for several experiments. It offers eighteen non-relativistic effective operators to describe velocity and momentum transfer, and elastic scattering of DM particles off nucleons, and has an extended detector interface.

[ascl:2308.015]
FishLSS: Fisher forecasting for Large Scale Structure surveys

FishLSS computes the Fisher information matrix for a set of observables and model parameters. It can model the redshift-space power spectrum of any biased tracer of the CDM+baryon field and the post-reconstruction galaxy power spectrum. The code also models the projected cross-correlation of galaxies with the CMB lensing convergence, the projected galaxy power spectrum, and the CMB lensing convergence power spectrum. FishLSS requires pyFFTW (ascl:2109.009), velocileptors (ascl:2308.014), and CLASS (ascl:1106.020).

[ascl:2308.014]
velocileptors: Velocity-based Lagrangian and Eulerian PT expansions of redshift-space distortions

velocileptors computes the real- and redshift-space power spectra and correlation functions of biased tracers using 1-loop perturbation theory (with effective field theory counter terms and up to cubic biasing) as well as the real-space pairwise velocity moments. It provides simple computation of the power spectrum wedges or multipoles, and uses a reduced set of parameters for computing the most common case of the redshift-space power spectrum. In addition, velocileptors offers two "direct expansion" modules available in LPT and EPT.

[ascl:2401.011]
ostrich: Surrogate modeling using PCA and Gaussian process interpolation

Cromer, Dylan; Battaglia, Nicholas; Miyatake, Hironao; Simet, Melanie; Heitmann, Katrin; Higdon, David; White, Martin; Habib, Salman; Williams, Brian J.; Lawrence, Earl; Wagner, Christian

Ostrich emulates surrogate models for complex and expensive functions using Principal Component Analysis (PCA) to decompose a signal, then interpolate the PCA weights over the parameters θ using a Gaussian Process interpolator. The code is trained on samples from the expensive functions, recreating and interpolating between those training samples with reduced computational cost, and recalculating for each use.