BiaPy provides deep-learning workflows for a large variety of image analysis tasks, including 2D and 3D semantic segmentation, instance segmentation, object detection, image denoising, single image super-resolution, self-supervised learning and image classification. Though developed specifically for bioimages, it can be used for watershed-based instance segmentation for friends-of-friends proto-haloes.

FLORAH generates the assembly history of halos using a recurrent neural network and normalizing flow model. The machine-learning framework can be used to combine multiple generated networks that are trained on a suite of simulations with different redshift ranges and mass resolutions. Depending on the training, the code recovers key properties, including the time evolution of mass and concentration, and galaxy stellar mass versus halo mass relation and its residuals. FLORAH also reproduces the dependence of clustering on properties other than mass, and is a step towards a machine learning-based framework for planting full merger trees.

EVA (Excess Variability-based Age) computes the VarX values and VarX90 ages for a given list of stars. The package retrieves information from Gaia, performs basic var90 calculations, then calculates the age of the group in a given band or overall (by combining all three bands). EVA then analyzes and plots the results.

The ALeRCE anomaly detector cross-validates six anomaly detection algorithms for three classes (transient, periodic, and stochastic) of anomalous sources within the Zwicky Transient Facility (ZTF) data stream using the ALeRCE light curve features. A machine and deep learning-based framework is used for anomaly detection. For each class, a distinct anomaly detection model is constructed using only information about the known objects (i.e., inliers) for training. An anomaly score is computed using the probabilities to determine whether the light curve corresponds to a transient, stochastic, or periodic nature.

Quadratic Monte Carlo generates ensembles of models and confines fitness landscapes without relying on linear stretch moves; it works very efficiently for ring potential and Rosenbrock density. The method is general and can be implemented into any existing MC software, requiring only a few lines of code.

Color transformations calculator determines the magnitude of a galaxy in a needed photometric band, given its color and magnitude in the original band. It supports various optical and near intrared surveys, including SDSS, DECaLS, DELVE, UKIDSS, VHS, and VIKING, and provides conversions for both total and aperture magnitudes with apertures of 1.5", 2" or 3" diameters. The source code, useful for performing bulk calculations, is available in Python and IDL; the calculator is also offered as a web service.

PRyMordial offers fast and precise evaluation of both the Big Bang Nucleosynthesis (BBN) light-element abundances and the effective number of relativistic degrees of freedom. It can be used within and beyond the Standard Model. The package calculates N_eff and helium-4, deuterium, helium-3 and lithium-7 abundances. PRyMordial corrects for QED plasma effects, neutron lifetime, and incomplete neutrino decoupling, and includes an optional module that re-elaborates all the ODE systems of the code in Julia.

CBiRd (Code for Bias tracers In Redshift space) provides correlators in the Effective Field Theory of Large-Scale Structure (EFTofLSS) in a ready-to-use pipeline for cosmological analysis of galaxy-redshift surveys data. It provides a core calculation package (C++BiRd), a Python implementation of a Taylor expansion of the power spectrum around a reference cosmology for efficient evaluation (TBiRd), and libraries to correct for observational systematics. CBiRd also provides MCMC samplers (MCBiRd) for a power spectrum and bispectrum analysis of galaxy-redshift surveys data based on emcee (ascl:1303.002), and can provide an earlybird pass to explore the cosmos with LSS surveys.

sphereint calculates the numerical volume in a sphere. It provides a weight for each grid position based on whether or not it is in (weight = 1), out (weight = 0), or partially in (weight in between 0 and 1) a sphere of a given radius. A cubic cell is placed around each grid position and the volume of the cell in the sphere (assuming a flat surface in the cell) is calculated and normalized by the cell volume to obtain the weight.

CARDiAC (Code for Anisotropic Redshift Distributions in Angular Clustering) computes the impact of anisotropic redshift distributions on a wide class of angular clustering observables. It supports auto- and cross-correlations of galaxy samples and cosmic shear maps, including galaxy-galaxy lensing. The anisotropy can be present in the mean redshift and/or width of Gaussian distributions, as well as in the fraction of galaxies in each component of multi-modal distributions. Templates of these variations can be provided by the user or simulated internally within the code.

The anzu package offers two independent codes for hybrid Lagrangian bias models in large-scale structure. The first code measures the hybrid "basis functions"; the second takes measurements of these basis functions and constructs an emulator to obtain predictions from them at any cosmology (within the bounds of the training set). anzu is self-contained; given a set of N-body simulations used to build emulators, it measures the basis functions. Alternatively, given measurements of the basis functions, anzu should in principle be useful for constructing a custom emulator.

Lenser estimates weak gravitational lensing signals, particularly flexion, from real survey data or realistically simulated images. Lenser employs a hybrid of image moment analysis and an Analytic Image Modeling (AIM) analysis. In addition to extracting flexion measurements by fitting a (modified Sérsic) model to a single image of a galaxy, Lenser can do multi-band, multi-epoch fitting. In multi-band mode, Lenser fits a single model to multiple postage stamps, each representing an exposure of a single galaxy in a particular band.

candl (CMB Analysis With A Differentiable Likelihood) analyzes CMB power spectrum measurements using a differentiable likelihood framework. It is compatible with JAX (ascl:2111.002), though JAX is optional, allowing for fast and easy computation of gradients and Hessians of the likelihoods, and candl provides interface tools for working with other cosmology software packages, including Cobaya (ascl:1910.019) and MontePython (ascl:1805.027). The package also provides auxiliary tools for common analysis tasks, such as generating mock data, and supports the analysis of primary CMB and lensing power spectrum data.

SMART (Spectral energy distributions Markov chain Analysis with Radiative Transfer models) implements a Bayesian Markov chain Monte Carlo (MCMC) method to fit the ultraviolet to millimeter spectral energy distributions (SEDs) of galaxies exclusively with radiative transfer models. The models constitute four types of pre-computed libraries, which describe the starburst, active galactic nucleus (AGN) torus, host galaxy and polar dust components.

Scaling Relations Finder finds the scaling relations between magnetic field properties and observables for a model of galactic magnetic fields. It uses observable quantities as input: the galaxy rotation curve, the surface densities of the gas, stars and star formation rate, and the gas temperature to create galactic dynamo models. These models can be used to estimate parameters of the random and mean components of the magnetic field, as well as the gas scale height, root-mean-square velocity and the correlation length and time of the interstellar turbulence, in terms of the observables.

GAStimator implements a Python MCMC Gibbs-sampler with adaptive stepping. The code is simple, robust, and stable and well suited to high dimensional problems with many degrees of freedom and very sharp likelihood features. It has been used extensively for kinematic modeling of molecular gas in galaxies, but is fully general and may be used for any problem MCMC methods can tackle.

CosmoPower develops Bayesian inference pipelines that leverage machine learning to solve inverse problems in science. While the emphasis is on building algorithms to accelerate Bayesian inference in cosmology, the implemented methods allow for their application across a wide range of scientific fields. CosmoPower provides neural network emulators of matter and Cosmic Microwave Background power spectra, which can replace Boltzmann codes such as CAMB (ascl:1102.026) or CLASS (ascl:1106.020) in cosmological inference pipelines, to source the power spectra needed for two-point statistics analyses. This provides orders-of-magnitude acceleration to the inference pipeline and integrates naturally with efficient techniques for sampling very high-dimensional parameter spaces.

ndcube manipulates, inspects, and visualizes multi-dimensional contiguous and non-contiguous coordinate-aware data arrays. A sunpy (ascl:1401.010) affiliated package, it combines data, uncertainties, units, metadata, masking, and coordinate transformations into classes with unified slicing and generic coordinate transformations and plotting and animation capabilities. ndcube handles data of any number of dimensions and axis types (e.g., spatial, temporal, and spectral) whose relationship between the array elements and the real world can be described by World Coordinate System (WCS) translations.

raccoon implements the cross-correlation function (CCF) method. It builds weighted binary masks from a stellar spectrum template, computes the CCF of stellar spectra with a mask, and derives radial velocities (RVs) and activity indicators from the CCF. raccoon is mainly implemented in Python 3; it also uses some Fortran subroutines that are called from Python.

blackthorn generates spectra of dark matter annihilations into right-handed (RH) neutrinos or into particles that result from their decay. These spectra include photons, positrons, and neutrinos. The code provides support for varied RH-neutrino masses ranging from MeV to TeV by incorporating hazma, PPPC4DMID, and HDMSpectra models to compute dark matter annihilation cross sections and mediator decay widths. blackthorn also computes decay branching fractions and partial decay widths.

PALpy provides a Python interface to PAL, the positional Astronomy Library (ascl:1606.002), which is written in C. All arguments modified by the C API are returned and none are modified. The one routine that is different is palObs, which returns a simple dict that can be searched using standard Python. The keys to the dict are the short names and the values are another dict with keys name, long, lat and height.

tapify implements a suite of multitaper spectral estimation techniques for analyzing time series data. It supports analysis of both evenly and unevenly sampled time series data. The multitaper statistic tackles the problems of bias and consistency, which makes it an improvement over the classical periodogram for evenly sampled data and the Lomb-Scargle periodogram for uneven sampling. In basic statistical terms, this estimator provides a confident look at the properties of a time series in the frequency or Fourier domain.

coronagraph provides a Python noise model for directly imaging exoplanets with a coronagraph-equipped telescope. Based on the original IDL code for this coronagraph model, coronograph_noise (ascl:2405.018), the Python version has been expanded in a few key ways. Most notably, the Telescope, Planet, and Star objects used for reflected light coronagraph noise modeling can now be used for transmission and emission spectroscopy noise modeling, making this model a general purpose exoplanet noise model for many different types of observations.

coronagraph_noise simulates coronagraph noise. Written in IDL, the code includes a generalized coronagraph routine and simulators for the WFIRST Shaped Pupil Coronagraph in both spectroscopy and imaging modes. Functions available include stellar and planetary flux functions, planet photon and zodiacal light count rates, planet-star flux ratio, and clock induced charge count rate, among others. coronagraph_noise also includes routines to smooth a plot by convolving with a Gaussian profile to convolve a spectrum with a given instrument resolution and to take a spectrum that is specified at high spectral resolution and degrade it to a lower resolution. A Python implementation of coronagraph_noise, coronagraph (ascl:2405.019), is also available.

AFINO (Automated Flare Inference of Oscillations) finds oscillations in time series data using a Fourier-based model comparison approach. The code analyzes the date and generates a results file in either JSON or Pickle format, which contains numerous properties of the data and analysis, and a summary plot.

autoregressive-bbh-inference, written in Python, models the distributions of binary black hole masses, spins, and redshifts to identify physical features appearing in these distributions without the need for strongly-parametrized population models. This allows not only agnostic study of the “known unknowns” of the black hole population but also reveals the “unknown unknowns," the unexpected and impactful features that may otherwise be missed by the standard building-block method.

sunbather simulates the upper atmospheres of exoplanets and their observational signatures. The code constructs 1D Parker wind profiles using p-winds (ascl:2111.011) to simulate these with Cloudy (ascl:9910.001), and postprocesses the output with a custom radiative transfer module to predict the transmission spectra of exoplanets.

EF-TIGRE (Effective Field Theory of Interacting dark energy with Gravitational REdshift) constrains interacting Dark Energy/Dark Matter models in the Effective Field Theory framework through Large Scale Structures observables. In particular, the observables include the effect of gravitational redshift, a distortion of time from galaxy clustering. This generates a dipole in the correlation function which is detectable with two distinct populations of galaxies, thus making it possible to break degeneracies among parameters of the EFT description.

LTdwarfIndices studies spectral indices to determine whether one or more brown dwarfs are photometric variable candidates. For a single brown dwarf, it analyzes a given set of indices and outputs the number of graphs the object appears in in the variable area, whether it is a variable or non-variable candidate, and, optionally, an index-index or histogram plot. Using another code module, LTdwarftIndices can also analyze a set of sample indices for many brown dwarfs.

fitramp fits a ramp to a series of nondestructive reads and detects and rejects jumps. The software performs likelihood-based jump detection for detectors read out up-the-ramp; it uses the entire set of reads to compute likelihoods. The code compares the χ² value of a fit with and without a jump for every possible jump location. fitramp can fit ramps with and without fitting the reset value (the pedestal), and fit and mask jumps within or between groups of reads. It can also compute the bias of ramp fitting.

DirectSHT performs direct spherical harmonic transforms for point sets on the sphere. Given a set of points, defined by arrays of theta and phi (in radians) and weights, it provides the spherical harmonic transform coefficients alm. JAX (ascl:2111.002) can be used to speed up the computation; the code will automatically fall back to numpy if JAX is not present. The code is much faster when run on GPUs. When they are available and JAX is installed, the code automatically distributes computation and memory across them.

riddler automates fitting of type Ia supernovae spectral time series. The code is comprised of a series of neural networks trained to emulate radiative transfer simulations from TARDIS (ascl:1402.018). Emulated spectra are then fit to observations using nested sampling implemented in UltraNest (ascl:1611.001) to estimate the posterior distributions of model parameters and evidences.

morphen performs image analysis, multi-Sersic image fitting decomposition, and radio interferometric self-calibration, thus measuring basic image morphology and photometry. The code provides a state-of-the-art Python-based image fitting implementation based on the Sersic function. Geared, though not exclusively, toward radio astronomy, morphen's tools involve pure python, but also are integrated with CASA (ascl:1107.013) in order to work with common casatasks as well as WSClean (ascl:1408.023).

i-SPin simulates 3-component Schrodinger systems with and without gravity and with and without self-interactions while obeying SO(3) symmetry. The code allows the user to input desired parameters, along with initial conditions for the Schrodinger fields. Its three function modules then perform the main (drift-kick-drift) steps of the algorithm, track the fractional changes in total mass and spin in the system, and then plot results. The default plots are mass and spin density projections along with total mass and spin fractional changes.

GauPro fits a Gaussian process regression model to a dataset. A Gaussian process (GP) is a commonly used model in computer simulation. It assumes that the distribution of any set of points is multivariate normal. A major benefit of GP models is that they provide uncertainty estimates along with their predictions.

ICPertFLRW, a Cactus code (ascl:1102.013) thorn, provides as initial conditions an FLRW metric perturbed with the comoving curvature perturbation Rc in the synchronous comoving gauge. Rc is defined as a sum of sinusoidals (20 in each x, y, and z direction) whose amplitude, wavelength, and phase shift are all parameters in param.ccl. While the metric and extrinsic curvature only have first order scalar perturbations, the energy density is computed exactly in full from the Hamiltonian constraint, hence vector and tensor perturbations are initially present at higher order. These are then passed to the CT_Dust thorn to be evolved.

pySPEDAS (Python-based Space Physics Environment Data Analysis Software) supports multi-mission, multi-instrument retrieval, analysis, and visualization of heliophysics time series data. A Python implementation of SPEDAS (ascl:2405.001), it supports most of the capabilities of SPEDAS; it can load heliophysics data sets from more than 30 space-based and ground-based missions, coordinate transforms, interpolation routines, and unit conversions, and provide interactive access to numerous data sets. pySPEDAS also creates multi-mission, multi-instrument figures, includes field and wave analysis tools, and performs magnetic field modeling, among other functions.

pyADfit models accretion discs around astrophysical objects. The code provides functions to calculate physical quantities related to accretion disks and perform parameter estimation using observational data. The accretion disc model is the alpha-disc model while the parameter estimation can be performed with Nessai (ascl:2405.002), Raynest (ascl:2405.003), or CPnest (ascl:2205.021).

raynest, written in Python, computes Bayesian evidences and probability distributions using parallel chains.

nessai performs nested sampling for Bayesian Inference and incorporates normalizing flows. It is designed for applications where the Bayesian likelihood is computationally expensive. nessai uses PyTorch and also supports the use of bilby (ascl:1901.011).

The SPEDAS (Space Physics Environment Data Analysis Software) framework supports multi-mission data ingestion, analysis and visualization for the Space Physics community. It standardizes the retrieval of data from distributed repositories, the scientific processing with a powerful set of legacy routines, the quick visualization with full output control and the graph creation for use in papers and presentations. SPEDAS includes a GUI for ease of use by novice users, works on multiple platforms, and though based on IDL, can be used with or without an IDL license. The framework supports plugin modules for multiple projects such as THEMIS, MMS, and WIND, and provides interfaces for software modules developed by the individual teams of those missions. A Python implementation of the framework, PySPEDAS (ascl:2405.005), is also available.

Swiftest is a software package designed to model the long-term dynamics of system of bodies in orbit around a dominant central body, such a planetary system around a star, or a satellite system around a planet. The main body of the program is written in Modern Fortran, taking advantage of the object-oriented capabilities included with Fortran 2003 and the parallel capabilities included with Fortran 2008 and Fortran 2018. Swiftest also includes a Python package that allows the user to quickly generate input, run simulations, and process output from the simulations. Swiftest uses a NetCDF output file format which makes data analysis with the Swiftest Python package a streamlined and flexible process for the user. Building off a strong legacy, including its predecessors Swifter and Swift, Swiftest takes the next step in modeling the dynamics of planetary systems by improving the performance and ease of use of software, and by introducing a new collisional fragmentation model. Currently, Swiftest includes the four main symplectic integrators included in its predecessors: WHM, RMVS, HELIO, and SyMBA. In addition, Swiftest also contains the Fraggle model for generating products of collisional fragmentation.

We present a module built into the PypeIt Python package to reduce high resolution Y, J, H, K, and L band spectra from the W. M. Keck Observatory NIRSPEC spectrograph. This data reduction pipeline is capable of spectral extraction, wavelength calibration, and telluric correction of data taken before and after the 2018 detector upgrade, all in a single package. The procedure for reducing data is thoroughly documented in an expansive tutorial.

A fast GPU-based bispectrum estimator implemented using JAX.

RhoPop identifies compositionally distinct populations of small planets (R≲2R_⊕). It employs mixture models in a hierarchical framework and the dynesty (ascl:1809.013) nested sampler for parameter and evidence estimates. RhoPop includes a density-mass grid of water-rich compositions from water mass fraction (WMF) 0-1.0 and a grid of volatile-free rocky compositions over a core mass fraction (CMF) range of 0.006-0.95. Both grids were calculated using the ExoPlex mass-radius-composition calculator (ascl:2404.029).

ExoPlex is a thermodynamically self-consistent mass-radius-composition calculator. Users input a bulk molar composition and a mass or radius, and ExoPlex will calculate the resulting radius or mass. Additionally, it will produce the planet's core mass fraction, interior mineralogy and the pressure, adiabatic temperature, gravity and density profiles as a function of depth.

binary_precursor models light curves of supernova (SN) precursors powered by a pre-SN outburst accompanying accretion onto a compact object companion. Though it is only one of the possible models, it is useful for interpretations of (bright) SN precursors highly exceeding the Eddington limit of massive stars, which are observed in a fraction of SNe with dense circumstellar matter (CSM) around the progenitor. It offers a number of editable parameters, including compact object mass, progenitor mass, progenitor radii, and opacity. Initial CSM velocity can be normalized by the progenitor escape velocity (xi parameter), and the CSM mass, ionization temperature, and binary separation can also be specified.

S2FFT computes Fourier transforms on the sphere and rotation group using JAX (ascl:2111.002) or PyTorch. It leverages autodiff to provide differentiable transforms, which are also deployable on hardware accelerators (e.g., GPUs and TPUs). More specifically, S2FFT provides support for spin spherical harmonic and Wigner transforms (for both real and complex signals), with support for adjoint transformations where needed, and comes with different optimisations (precompute or not) that one may select depending on available resources and desired angular resolution L.

LEO-vetter automatically vets transit signals found in light curve data. Inspired by the Kepler Robovetter (ascl:2012.006), LEO-vetter computes vetting metrics to be compared to a series of pass-fail thresholds. If a signal passes all tests, it is considered a planet candidate (PC). If a signal fails at least one test, it may be either an astrophysical false positive (FP; e.g., eclipsing binary, nearby eclipsing signal) or false alarm (FA; e.g., systematic, stellar variability). Pass-fail thresholds can be changed to suit individual research purposes, and LEO-vetter produces vetting reports for manual inspection of signals. Flux-level vetting can be applied to any light curve dataset (such as Kepler, K2, and TESS), including light curves with mixes of cadences, while pixel-level vetting has been implemented for TESS.

stringgen creates emulations of cosmic string maps with statistics similar to those of a single (or small ensemble) of reference simulations. It uses wavelet phase harmonics to calculate a compressed representation of these reference simulations, which may then be used to synthesize new realizations with accurate statistical properties, e.g., 2 and 3 point correlations, skewness, kurtosis, and Minkowski functionals.