PolyBin3D estimates the binned power spectrum and bispectrum for 3D fields such as the distributions of matter and galaxies. For each statistic, two estimators are available: the standard (ideal) estimators, which do not take into account the mask, and window-deconvolved estimators. In the second case, the computation of a Fisher matrix is required; this depends on binning and the mask, but does not need to be recomputed for each new simulation. PolyBin3D supports GPU acceleration using JAX. It is a sister code to PolyBin (ascl:2307.020), which computes the polyspectra of data on the two-sphere, and is a modern reimplementation of the former Spectra-Without-Windows (ascl:2108.011) code.

WignerFamilies generates families of Wigner 3j and 6j symbols by recurrence relation. These exact methods are orders of magnitude more efficient than strategies such as prime factorization for problems which require every non-trivial symbol in a family, and are very useful for large quantum numbers. WignerFamilies is thread-safe and very fast, beating the standard Fortran routine DRC3JJ from SLATEC by a factor of 2-4.

LensIt enables CMB lensing and CMB delensing using the flat-sky approximation. The package can find the maximum posterior estimation of CMB lensing deflection maps from temperature and/or polarization maps and perform Wiener filtering of masked CMB data and allow for inhomogenous noise, including lensing deflections, using a multigrid preconditioner. It contains fast and accurate simulation libraries for lensed CMB skies, and standard quadratic estimator lensing reconstruction tools. LensIt also includes CMB internal delensing tools, including internal delensing biases calculation for temperature and/or polarization maps.

The superABC sampling method obtains cosmological constraints from supernova light curves using Approximate Bayesian Computation (ABC) without any likelihood assumptions. It provides an interface to two forward model simulations, SNCosmo (ascl:1611.017) and SNANA (ascl:1010.027), for supernova cosmology.

Panphasia computes a very large realization of a Gaussian white noise field. The field has a hierarchical structure based on an octree geometry with 50 octree levels fully populated. The code sets up Gaussian initial conditions for cosmological simulations and resimulations of structure formation. Panphasia provides an easy way to publish the linear phases used to set up cosmological simulation initial conditions; publishing phases enriches the literature and makes it easier to reproduce and extend published simulation work.

BayeSN performs hierarchical Bayesian SED modeling of type Ia supernova light curves. This probabilistic optical-NIR SED model analyzes the population distribution of physical properties as well as cosmology-independent distance estimation for individual SNe. BayeSN is built with NumPyro and Jax (ascl:2111.002) and provides support for GPU acceleration.

EffectiveHalos provides models of the real-space matter power spectrum, based on a combination of the Halo Model and Effective Field Theory, which are 1% accurate up to k = 1 h/Mpc, across a range of cosmologies, including those with massive neutrinos. It can additionally compute accurate halo count covariances (including a model of halo exclusion), both alone and in combination with the matter power spectrum.

Meanoffset performs astronomical image alignment. The code uses the means of the rows and columns of an original image for alignment and finds the optimal offset corresponding to the maximum similarity by comparing different offsets between images. The similarity is evaluated by the standard deviation of the quotient divided by the means. The code is fast and robust.

astroNN creates neural networks for deep learning using Keras for model and training prototyping while taking advantage of Tensorflow's flexibility. It contains tools for use with APOGEE, Gaia and LAMOST data, though is primarily designed to apply neural nets on APOGEE spectra analysis and predict luminosity from spectra using data from Gaia parallax with reasonable uncertainty from Bayesian Neural Net. astroNN can handle 2D and 2D colored images, and the package contains custom loss functions and layers compatible with Tensorflow or Keras with Tensorflow backend to deal with incomplete labels. The code contains demo for implementing Bayesian Neural Net with Dropout Variational Inference for reasonable uncertainty estimation and other neural nets.

EBWeyl computes the electric and magnetic parts of the Weyl tensor, Eαβ and Bαβ, using a 3+1 slicing formulation. The module provides a Finite Differencing class with 4th (default) and 6th order backward, centered, and forward schemes. Periodic boundary conditions are used by default; otherwise, a combination of the 3 schemes is available. It also includes a Weyl class that computes for a given metric the variables of the 3+1 formalism, the spatial Christoffel symbols, spatial Ricci tensor, electric and magnetic parts of the Weyl tensor projected along the normal to the hypersurface and fluid flow, the Weyl scalars and invariant scalars. EBWeyl can also compute the determinant and inverse of a 3x3 or 4x4 matrice in every position of a data box.

The Machine Learning Telescope Pointing Correction code trains and tests machine learning models for correcting telescope pointing. Using historical APEX data from 2022, including pointing corrections, and other data such as weather conditions, position and rotation of the secondary mirror, pointing offsets observed during pointing scans, and the position of the sun, among other data, the code treats the data in two different ways to test which factors are the most likely to account for pointing errors.

pyilc implements the needlet internal linear combination (NILC) algorithm for CMB component separation in pure Python; it also implements harmonic-space ILC. The code can also perform Cross-ILC, where the covariance matrices are computed only from independent splits of the maps. In addition, pyilc includes an inpainting code, diffusive_inpaint, that diffusively inpaints a masked region with the mean of the unmasked neighboring pixels.

GPUniverse models quantum fields in finite dimensional Hilbert spaces with Generalised Pauli Operators (GPOs) and overlapping degrees of freedom. In addition, the package can simulate sets of qubits that are only quasi independent (i.e., the Pauli algebras of different qubits have small, but non-zero anti-commutator), which is useful for validating analytical results for holographic versions of the Weyl field.

PySSED (Python Stellar Spectral Energy Distributions) downloads and extracts data on multi-wavelength catalogs of astronomical objects and regions of interest and automatically proceses photometry into one or more stellar SEDs. It then fits those SEDs with stellar parameters. PySSED can be run directly from the command line or as a module within a Python environment. The package offers a wide variety plots, including Hertzsprung–Russell diagrams of analyzed objects, angular separation between sources in specific catalogs, and two-dimensional offset between cross-matches.

NbodyIMRI uses N-body simulations to study Dark Matter-dressed intermediate-mass ratio inspirals (IMRI) and extreme mass ratio inspiral (EMRI) systems. The code calculates all BH-BH forces and BH-DM forces directly while neglecting DM-DM pairwise interactions. This allows the code to scale up to very large numbers of DM particles in order to study stochastic processes like dynamical friction.