Code
PolyBin3D
PolyBin3D is a sister-code to PolyBin, designed to compute binned polyspectra in three-dimensions (relevant for galaxy surveys and N-body simulations). It can compute the binned power spectrum and bispectrum in redshift-space, and is loosely based on the Spectra Without Windows code, but with a much more modern and user-friendly implementation. The package includes both conventional estimators and more nuanced forms that do not receive bias from window and mask effects.
PolyBin
PolyBin is a Python code for efficient estimation of binned polyspectra on the CMB (or any other spherical field). It can compute the binned power spectrum, bispectrum, and trispectrum of spin-0 and spin-2 fields, optionally including parity-violating contributions. This implements the estimators described in Philcox 2023a and Philcox 2023c, which are minimum variance (under certain assumptions), and can be efficiently computed. The estimators can also fully account for masking and inpainting operations, yielding spectra without contamination from the observational mask. These have been used to compute the Planck parity-odd temperature trispectrum.
Full-Shape-Likelihoods
The Full Shape Likelihoods repository contains Montepython likelihoods for the full-shape analysis of current and future survey data using the CLASS-PT code. The likelihoods include the power spectrum multipoles, the real-space power spectrum, the BAO parameters, and the bispectrum monopoles, optionally including primordial non-Gaussianity. This has been used for various analyses of the BOSS dataset including Philcox & Ivanov 2022, in conjunction with the below code for computing the relevant correlators.
Spectra Without Windows
The Spectra Without Windows repository contains the analysis code required to compute the galaxy power spectrum and bispectrum multipoles using optimal quadratic and cubic estimators, as descibed in Philcox 2020b, Philcox 2021 and Ivanov et al. 2023. The output measurements do not include the survey window functions, thus can be easily compared to theory models. Additionally, they give close-to-minimum-variance measurements of the power spectrum and bispectrum, which is particularly useful on small-scales. The code is written in Python, with some routines borrowed from nbodykit. To compute the statistics, one requires only the survey mask, the data, and a random particle catalog. The power spectrum and bispectrum codes have been used in the latest full-shape BOSS DR12 analyses including Philcox & Ivanov 2022.
NPCFs.jl
NPCFs.jl is a Julia code to compute N-point correlation functions for a range of values of N, in a variety of dimensions and spatial curvatures. This implements two algorithms described in Philcox & Slepian 2021b to compute the NPCF of n points; a slow O(n^N) approach, counting all n^N tuplets of points, and an efficient O(n^2) approach, making use of hyperspherical harmonic decomposition to split the summation into one over pairs of particles. The code features full parallelization, both inside and between nodes, and currently includes support for isotropic 2PCFs, 3PCFs, 4PCFs and 5PCFs in 2D, 3D and 4D Euclidean geometries, and 2D spherical geometries. Application to other scenarios, such as anisotropic NPCFs and more dimensions, can be straightforwardly added.
encore
encore is a C++ code which computes the isotropic N-point correlation functions (NPCFs) of a set of N_g discrete points (e.g., a galaxy survey) using the O(N_g^2) method of Philcox et al. 2021. The code supports N = 2, 3, 4, 5 and 6, and can additionally subtract the Gaussian contribution of the 4PCF at the estimator-level. It is fast, making use of OpenMP parallelization, AVX assembly code, and, for the higher-point functions, GPU acceleration, allowing computation of the full 4PCF for a BOSS-like survey in only a few tens of CPU-hours. It may further be extended to anisotropic NPCFs, following the methods of Philcox & Slepian 2021b.
CLASS-PT
CLASS-PT is an extension of CLASS used to compute one-loop perturbation theory for matter and biased tracers in real and redshift space, described in Chudaykin et al. 2020. It has been used in a number of works and can be interfaced with the MontePython for fast MCMC sampling. Custom-built likelihoods for full-shape power spectrum and bispectrum analyses are available on Github.
RascalC
RascalC is a C++ code to quickly compute covariance matrices of galaxy two- and three-point correlation functions in arbitrary survey geometries, based on (Philcox et al. 2019; Philcox & Eisenstein 2019). This is a fast Monte Carlo integrator of the relevant 12-18 dimensional integrals, and has been shown to give highly accurate covariances in a fraction of the time required from mock catalogs, fully taking into account anisotropies from the survey window. Covariances can be computed both for correlation functions in Legendre multipoles, correlation functions in angular bins and jackknife correlation functions. This is currently being used by a number of teams, including eBOSS, and extensive documentation is available online. This code is being used to compute covariances for DESI correlation function analyses.
HIPSTER
HIPSTER (HIgh-k Power Spectrum EstimatoR) is a fast C++ code to compute galaxy power spectra and bispectra from surveys of arbitrary shapes, based on Philcox & Eisenstein 2020 and Philcox 2020. These have quadratic order and are optimized for small-scale spectral computation, since they use a configuration-space algorithm to compute the spectra as weighted pair counts, rather than using Fast Fourier Transforms (FFTs). As such, they doesn’t suffer from many of the common problems of FFTs; aliasing, shot-noise and window function convolution. For computing the power spectrum and bispectrum in cosmological simulations (with periodic boundary conditions), we include a variant of HIPSTER with minimal dependence on random particles. Extensive documentation is provided for HIPSTER, and it can be run in a single line of code. A new branch of HIPSTER is also being used to compute three-dimensional Lyman-alpha power spectra.
Kepler’s Goat Herd
Kepler’s Goat Herd is a C++ code which uses the method of Philcox et al. 2021 to solve Kepler’s equation for elliptical orbit evolution. The code performs the relevant contour integrals numerically, and compares the results to conventional methods based on root-finding and series solutions. Our method is found to be around twice the speed of a quartic root-finder for the same level of accuracy.
EffectiveHalos
EffectiveHalos is a fast Python code to accurately compute the matter power spectrum and halo covariances across a large range of scales using the Effective Halo model developed in Philcox et al. 2020. The matter power spectrum is percent-level accurate for scales between 0.02 and 1 h/Mpc for a variety of cosmologies (and expected to also have good accuracy beyond these scales), and uses Effective Field Theory to generate a model for the quasi-linear power spectrum which is combined with halo model terms. In addition, the code can be used to compute the covariance between halo number counts in two mass bins, or the covariance between halo number counts and the matter power spectrum. This includes all relevant effects; the intrinsic covariance, halo exclusion and super-sample effects. The code is simple to run, with a tutorial and full API documentation available online.
HADES
HADES (Hexadecapolar Analysis for Dust Estimation in Simulations) follows the work of Philcox et al. 2018b, using polarized dust anisotropy properties to measure the amount of dust contamination in Cosmic Microwave Background (CMB) B-mode maps. In particular, it allows for discrimination between unsubstracted dust and Inflationary Gravitational Waves (IGWs) well below the current constraints on IGW amplitudes. A basic tutorial is provided online.
Chempy
Chempy was originally developed by Jan Rybizki in Heidelberg, Germany and is a fast and flexible Python model of galactic chemical evolution, which can be used to infer Galactic parameters using Markov Chain Monte Carlo (MCMC) methods. In Philcox et al. 2018a, this was extended to include a scoring system for nucleosynthetic yield tables (judging them by their ability to reproduce proto-solar abundances) and in Philcox & Rybizki 2019, we extended the statistics to use multiple stars via modern statistical methods. A selection of tutorials are available online.