[ascl:2406.003]
SMART: Spectral energy distribution (SED) fitter

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.

[ascl:2406.004]
candl: Differentiable likelihood framework for analyzing CMB power spectrum measurements

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.

[ascl:2406.005]
Lenser: Measure weak gravitational flexion

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.

[ascl:2406.006]
anzu: Measurements and emulation of Lagrangian bias models for clustering and lensing cross-correlations

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.

[ascl:2406.007]
CARDiAC: Anisotropic Redshift Distributions in Angular Clustering

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.

[ascl:2406.008]
sphereint: Integrate data on a grid within a sphere

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.

[ascl:2406.009]
CBiRd: Bias tracers In Redshift space

Zhang, Pierre; d'Amico, Guido; Gleyzes, Jerome; Beutler, F.; Colas, T.; Gil-Marin, H.; Kokron, N.; Lewandowski, M.; Markovic, D.; Perko, A.

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.

[ascl:2406.010]
PRyMordial: Precise computations of BBN within and beyond the Standard Model

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.

[ascl:2406.011]
CTC: Color transformations calculator

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.

[ascl:2406.012]
QMC: Quadratic Monte Carlo

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.

[ascl:2406.013]
AAD: ALeRCE Anomaly Detector

Perez-Carrasco, Manuel; Cabrera-Vives, Guillermo; Hernandez-García, Lorena; Förster, F.; Sanchez-Saez, Paula; Muñoz Arancibia, Alejandra M.; Arredondo, Javier; Astorga, Nicolás; Bauer, Franz E.; Bayo, Amelia; Catelan, M.; Dastidar, Raya; Estévez, P. A.; Lira, Paulina; Pignata, Giuliano

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.

[ascl:2406.014]
EVA: Excess Variability-based Age

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.

[ascl:2406.015]
FLORAH: Galaxy merger tree generator with machine learning

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.

[ascl:2406.016]
BiaPy: Bioimage analysis pipeline builder

Franco-Barranco, Daniel; Andrés-San Román, Jesús A.; Hidalgo-Cenalmor, Ivan; Backová, Lenka; González-Marfil, Aitor; Caporal, Clément; Chessel, Anatole; Gómez-Gálvez, Pedro; Escudero, Luis M.; Wei, Donglai; Muñoz-Barrutia, Arrate; Arganda-Carreras, Ignacio

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.

[ascl:2406.017]
ytree: yt-based merger-tree code

ytree reads and works with merger tree data from multiple formats. An extension of yt (ascl:1011.022), which can analyze snapshots from cosmological simulations, ytree can be thought of as the yt of merger trees. ytree's online documentation lists supported formats; support for additional formats can be added, as in principle, any type of tree-like data where an object has one or more ancestors and a single descendant can be supported.

[ascl:2406.018]
SuperLite: Spectral synthesis code for interacting transients

SuperLite produces synthetic spectra for astrophysical transient phenomena affected by circumstellar interaction. It uses Monte Carlo methods and multigroup structured opacity calculations for semi-implicit, semirelativistic radiation transport in high-velocity shocked outflows, and can reproduce spectra of typical Type Ia, Type IIP, and Type IIn supernovae. SuperLite also generates high-quality spectra that can be compared with observations of transient events, including superluminous supernovae, pulsational pair-instability supernovae, and other peculiar transients.

[ascl:2406.019]
MBE: Magnification bias estimation

Magnification bias estimation estimates magnification bias for a galaxy sample with a complex photometric selection for the example of SDSS BOSS. The code works for CMASS and the LOWZ, z1 and z3 samples. A template for applying the approach to other surveys is included; requirements include a galaxy catalog that provides magnitudes (used for photometric selection) and the exact conditions used for the photometric selection.

[ascl:2406.020]
LeHaMoC: Leptonic-Hadronic Modeling Code for high-energy astrophysical sources

LeHaMoC simulates high-energy astrophysical sources. It simulates the behavior of relativistic pairs, protons interacting with magnetic fields, and photons in a spherical region. The package contains numerous physical processes, including synchrotron emission and self-absorption, inverse Compton scattering, photon-photon pair production, and adiabatic losses. It also includes proton-photon pion production, proton-photon (Bethe-Heitler) pair production, and proton-proton collisions. LeHaMoC can model expanding spherical sources with a variable magnetic field strength. In addition, three types of external radiation fields can be defined: grey body or black body, power-law, and tabulated.

[ascl:2406.021]
photochem: Chemical model of planetary atmospheres

Photochem models the photochemical and climate composition of a planet's atmosphere. It takes inputs such as the stellar UV flux and atmospheric temperature structure to find the steady-state chemical composition of an atmosphere, or evolve atmospheres through time. Photochem also contains 1-D climate models and a chemical equilibrium solver.

[ascl:2406.022]
phazap: Low-latency identification of strongly lensed signals

Phazap post-processes gravitational-wave (GW) parameter estimation data to obtain the phases and polarization state of the signal at a given detector and frequency. It is used for low-latency identification of strongly lensed gravitational waves via their phase consistency by measuring their distance in the detector phase space. Phazap builds on top of the IGWN conda enviroment which includes the standard GW packages LALSuite (ascl:2012.021) and bilby (ascl:1901.011), and can be applied beyond lensing to test possible deviations in the phase evolution from modified theories of gravity and constrain GW birefringence.

[ascl:2406.023]
AARD: Automatic detection of solar active regions

This python code automatically detects solar active regions (AR). Based on morphological operation and region growing, it uses synoptic magnetograms from SOHO/MDI and SDO/HMI and calculates the parameters that characterize each AR, including the latitude and longitude of the flux-weighted centroid of two polarities and the whole AR, the area, and the flux of each polarity, and the initial and final dipole moments.

[ascl:2406.024]
GRINN: Gravity Informed Neural Network for studying hydrodynamical systems

GRINN (Gravity Informed Neural Network) solves the coupled set of time-dependent partial differential equations describing the evolution of self-gravitating flows in one, two, and three spatial dimensions. It is based on physics informed neural networks (PINNs), which are mesh-free and offer a fundamentally different approach to solving such partial differential equations. GRINN has solved for the evolution of self-gravitating, small-amplitude perturbations and long-wavelength perturbations and, when modeling 3D astrophysical flows, provides accuracy on par with finite difference (FD) codes with an improvement in computational speed.

[ascl:2406.025]
PowerSpecCovFFT: FFTLog-based computation of non-Gaussian analytic covariance of galaxy power spectrum multipoles

PowerSpecCovFFT computes the non-Gaussian (regular trispectrum and its shot noise) part of the analytic covariance matrix of the redshift-space galaxy power spectrum multipoles using an FFTLog-based method. The galaxy trispectrum is based on a tree-level standard perturbation theory but with a slightly different galaxy bias expansion. The code computes the non-Gaussian covariance of the power spectrum monopole, quadrupole, hexadecapole, and their cross-covariance up to kmax ~ 0.4 h/Mpc.

[ascl:2406.026]
Faceted-HyperSARA: Parallel faceted imaging in radio interferometry

Thouvenin, Pierre-Antoine; Dabbech, Arwa; Jiang, Ming; Abdulaziz, Abdullah; Thiran, Jean-Philippe; Jackson, Adrian; Wiaux, Yves

Faceted-HyperSARA images radio-interferometric wideband intensity data. Written in MATLAB, the library offers a collection of utility functions and scripts from data extraction from an RI measurement set MS Table to the reconstruction of a wideband intensity image over the field of view and frequency range of interest. The code achieves high precision imaging from large data volumes and supports data dimensionality reduction via visibility gridding and estimation of the effective noise level when reliable noise estimates are not available. Faceted-HyperSASA also corrects the w-term via w-projection and incorporates available compact Fourier models of the direction dependent effects (DDEs) in the measurement operator.

[ascl:2406.027]
phi-GPU: Parallel Hermite Integration on GPU

Berczik, Peter; Nitadori, Keigo; Zhong, Shiyan; Spurzem, Rainer; Hamada, Tsuyoshi; Wang, Xiaowei; Berentzen, Ingo; Veles, Alexander; Ge, Wei

The phi-GPU (Parallel Hermite Integration on GPU) high-order N-body parallel dynamic code uses the fourth-order Hermite integration scheme with hierarchical individual block time-steps and incorporates external gravity. The software works directly with GPU, using only NVIDIA GPU and CUDA code. It creates numerical simulations and can be used to study galaxy and star cluster evolution.

[ascl:2406.028]
Redback: Bayesian inference package for fitting electromagnetic transients

Sarin, Nikhil; Hübner, Moritz; Omand, Conor M. B.; Setzer, Christian N.; Schulze, Steve; Adhikari, Naresh; Sagués-Carracedo, Ana; Galaudage, Shanika; Wallace, Wendy F.; Lamb, Gavin P.; Lin, En-Tzu

Redback provides end-to-end interpretation and parameter estimation of electromagnetic transients. Using data downloaded by the code or provided by the user, the code processes the data into a homogeneous transient object. Redback implements several different types of electromagnetic transients models, ranging from simple analytical models to numerical surrogates, fits models implemented in the package or provided by the user, and plots lightcurves. The code can also be used as a tool to simulate realistic populations without having to fit anything, as models are implemented as functions and can be used to simulate populations. Redback uses Bilby (ascl:1901.011) for sampling and can easily switch samplers and likelihoods.

[ascl:2406.030]
AutoPhOT: Rapid publication-quality photometry of transients

AutoPhOT (AUTOmated Photometry Of Transients) produces publication-quality photometry of transients quickly. Written in Python 3, this automated pipeline's capabilities include aperture and PSF-fitting photometry, template subtraction, and calculation of limiting magnitudes through artificial source injection. AutoPhOT is also capable of calibrating photometry against either survey catalogs (*e.g.*, SDSS, PanSTARRS) or using a custom set of local photometric standards.

[submitted]
Exovetter

Exovetter is an open-source, pip-installable python package which calculates metrics on high cadence time series photometry to distinguish between exoplanet transit signals and false positives. The package standardizes the implementation of metrics developed for the TESS, Kepler, and K2 missions such as Odd-Even, Multiple Event Statistic, and Centroid Offset (see “Planetary Candidates Observed by Kepler. VIII.”, Thompson et al. 2018.). Metrics can be run individually or together as part of a pipeline. Exovetter also includes several visualizations to further evaluate the transits and metrics.

[ascl:2406.029]
WinNet: Flexible, multi-purpose, single-zone nuclear reaction network

Reichert, M.; Winteler, C.; Korobkin, O.; Arcones, A.; Bliss, J.; Eichler, M.; Frischknecht, U.; Fröhlich, C.; Hirschi, R.; Jacobi, M.; Kuske, J.; Martínez-Pinedo, G.; Martin, D.; Mocelj, D.; Rauscher, T.; Thielemann, F. K.

WinNet, a single zone nuclear reaction network, calculates many different nucleosynthesis processes, including r-process, nup-process, and explosive nucleosynthesis, and many more). It reads in a user-defined file with runtime parameters, then chooses the evolution mode, which is dependent on temperature. The temperature, density, and neutrino quantities are updated, after which the reaction network equations are solved numerically. If convergence is not achieved, the step size is halved and the iteration is repeated. Once convergence is reached, the output is generated and the time is evolved; the final output such as the final abundances and mass fractions are written.

[ascl:2407.001]
MAKEE: MAuna Kea Echelle Extraction

MAKEE (MAuna Kea Echelle Extraction) reduces data from the HIRES and ESI instruments at Keck Observatory. It is optimized for the spectral extraction of single, unresolved point sources and is designed to run non-interactively using a set of default parameters. Taking the raw HIRES FITS files as input, the code determines the position (or trace) of each echelle order, defines the object and background extraction boundaries, optimally extracts a spectrum for each order, and computes wavelength calibrations. MAKEE produces FITS format "spectral images" (each row is a separate echelle order spectrum) and the data values are in arbitrary (relative) flux units. MAKEE will reduce data from all HIRES formats, including the single CCD format, the single CCD with Red and UV cross dispersers, and the current 3 CCD system. It can handle a variety of pixel binnings, including 1x1, 1x2, 1x4 (column x row).

[ascl:2407.002]
pyFAT: Python Fully Automated TiRiFiC

Python Fully Automated TiRiFiC (pyFAT) wraps around the tilted ring fitting code (TiRiFiC, ascl:1208.008) to fully automate the process of fitting simple tilted ring models to line emission cubes. pyFAT is the successor to the IDL/GDL FAT (ascl:1507.011) code and offers improved handling and fitting as well as several new features. PyFAT fits simple rotationally symmetric discs with asymmetric warps and surface brightness distributions, providing a base model that can can be used in TiRiFiC to explore large scale motions. pyFAT delivers much more control over the fitting procedure, which is made possible by the new modular setup and the use of omegaconf for the input and default settings.

[ascl:2407.003]
pycosie: Python analysis code used on Technicolor Dawn

pycosie is analysis code used for Technicolor Dawn (TD), a Gadget-3 derived cosmological radiative SPH simulation suite. The target analyses are to complement what is done with TD and other analysis software in its suite. pycosie creates power spectrum from generated Lyman-alpha forests spectra, links absorbers to potential host galaxies, grids gas information for each galaxy, and reads specific output files from software such as Rockstar (ascl:1210.008) and SKID (ascl:1102.020).

[ascl:2407.004]
Forklens: Deep learning weak lensing shear

Zhang, Zekang; Shan, Huanyuan; Li, Nan; Wei, Chengliang; Yao, Ji; Ban, Zhang; Fang, Yuedong; Guo, Qi; Liu, Dezi; Li, Guoliang; Lin, Lin; Li, Ming; Li, Ran; Li, Xiaobo; Luo, Yu; Meng, Xianmin; Nie, Jundan; Qi, Zhaoxiang; Qiu, Yisheng; Shao, Li; Tian, Hao; Wang, Lei; Wang, Wei; Xian, Jingtian; Xu, Youhua; Zhang, Tianmeng; Zhang, Xin; Zhou, Zhimin

Forklens measures weak gravitational lensing signal using a deep-learning methoe. It measures galaxy shapes (shear) and corrects the smearing of the point spread function (PSF, an effect from either/both the atmosphere and optical instrument). It contains a custom CNN architecture with two input branches, fed with the observed galaxy image and PSF image, and predicts several features of the galaxy, including shape, magnitude, and size. Simulation in the code is built directly upon GalSim (ascl:1402.009).

[ascl:2407.005]
BaCoN: BAyesian COsmological Network

BaCoN (BAyesian COsmological Network) trains and tests Bayesian Convolutional Neural Networks in order to classify dark matter power spectra as being representative of different cosmologies, as well as to compute the classification confidence. It supports the following theories: LCDM, wCDM, f(R), DGP, and a randomly generated class. Additional cosmologies can be easily added.

[ascl:2407.006]
provabgs: SED modeling tools for PROVABGS

provabgs infers full posterior distributions of galaxy properties for galaxies in the DESI Bright Galaxy Survey using state-of-the-art Bayesian spectral energy distribution (SED) modeling of DESI spectroscopy and photometry. provabgs includes a state-of-the-art stellar population synthesis (SPS) model based on non-parametric prescription for star formation history, a metallicity history that varies over the age of the galaxy, and a flexible dust prescription. It has a neural network emulator for the SPS model that enables accelerated inference. Full posteriors of the 12 SPS parameters can be derived in ~10 minutes. The emulator is currently designed for galaxies from 0 < z < 0.6. provabgs also includes a Bayesian inference pipeline that is based on zeus (ascl:2008.010).

[ascl:2407.007]
GRDzhadzha: Evolve matter on curved spacetimes

Aurrekoetxea, Josu C.; Bamber, Jamie; Brady, Sam E.; Clough, Katy; Helfer, Thomas; Marsden, James; Traykova, Dina; Wang, Zipeng

GRDzhadzha evolves matter on curved spacetimes with an analytic time and space dependence. Written in C++14, it uses hybrid MPI/OpenMP parallelism to achieve good performance. The code is based on publicly available 3+1D numerical relativity code GRChombo (ascl:2306.039) and inherits all of the capabilities of the main GRChombo code, which uses the Chombo library for adaptive mesh refinement.

[ascl:2407.008]
RealSim: Statistical observational realism for synthetic images from galaxy simulations

Bottrell, Connor; Hani, Maan H.; Teimoorinia, Hossen; Ellison, Sara L.; Moreno, Jorge; Torrey, Paul; Hayward, Christopher C.; Thorp, Mallory; Simard, Luc; Hernquist, Lars

RealSim generates survey-realistic synthetic images of galaxies from hydrodynamical simulations of galaxy formation and evolution. The main functionality of this code inserts "idealized" simulated galaxies into Sloan Digital Sky Survey (SDSS) images in such a way that the statistics of sky brightness, resolution, and crowding are matched between simulated galaxies and observed galaxies in the SDSS. The suite accepts idealized synthetic images in calibrated AB surface brightnesses and rebins them to the desired redshift and CCD angular scale; RealSim can add Poisson noise, if desired, by adopting generic values of photometric calibrations in survey fields. Images produced by the suite can be inserted into real image fields to incorporate real skies, PSF degradation, and contamination by neighboring sources in the field of view. The RealSim methodology can be applied to any existing galaxy imaging survey.

[ascl:2407.009]
ATM: Asteroid Thermal Modeling

ATM (Asteroid Thermal Modeling) models asteroid flux measurements to estimate an asteroid's size, surface temperature distribution, and emissivity, and creates model spectral energy distributions for the different thermal models. After downloading lookup tables for relevant models, it can also fit observations of asteroids.

[ascl:2407.010]
UFalcon: Ultra Fast Lightcone

Sgier, R. J.; Réfrégier, Alexandre; Amara, Adam; Nicola, Andrina; Fluri, Janis; Herbel, Jörg; Kacprzak, Tomasz; Reeves, Alexander; Machado Poletti Valle, Luis Fernando

UFalcon rapidly post-processes N-body code output into signal maps for many different cosmological probes. The package is able to produce maps of weak-lensing convergence, linear-bias galaxy over-density, cosmic microwave background (CMB) lensing convergence and the integrated Sachs-Wolfe temperature perturbation given a set of N-body lightcones. It offers high flexibility for lightcone construction, such as user-specific survey-redshift ranges, redshift distributions and single-source redshifts. UFalcon also computes the galaxy intrinsic alignment signal, which can be treated as an additive component to the cosmological signal.

[ascl:2407.011]
bigfile: A reproducible massively parallel IO library for hierarchical data

bigfile stores data from cosmology simulations from HPC systems and beyond. It provides a hierarchical structure of data columns via File, Dataset and Column. A Column stores a two dimensional table. Numerical typed columns are supported; attributes can be attached to a Column and both numerical attributes and string attributes are supported. Type casting is performed on-the-fly if read/write operations request a different data type than the file has stored.

[ascl:2407.012]
Fof: Friends-of-friends code to find groups

Fof uses the friends-of-friends method to find groups. A particle belongs to a friends-of-friends group if it is within some linking length of any other particle in the group. After all such groups are found, those with less than a specified minimum number of group members are rejected. The program takes input files in the TIPSY (ascl:1111.015) binary format and produces a single ASCII output file called fof.grp. This output file is in the TIPSY array format and contains the group number to which each particle belongs. A group number of zero means that the particle does not belong to a group. The fof.grp file can be read in by TIPSY and used to color each particle by group number to visualize the groups. Simulations with periodic boundary conditions can also be handled by fof by specifying the period in each dimension on the command line.

[ascl:2407.013]
cola_halo: Parallel cosmological N-body simulator

cola_halo generates hundreds of realizations on the fly. This parallel cosmological N-body simulation code generates random Gaussian initial condition using 2LPTIC (ascl:1201.005), time evolves N-body particles with colacode (ascl:1602.021), and finds dark-matter halos with the Friends-of-Friends code (ascl:2407.012).

[ascl:2407.014]
PFFT: Parallel fast Fourier transforms

PFFT computes massively parallel, fast Fourier transformations on distributed memory architectures. PFFT can be understood as a generalization of FFTW-MPI (ascl:1201.015) to multidimensional data decomposition; in fact, using PFFT is very similar to FFTW. The library is written in C and MPI; a Fortran interface is also available.

[ascl:2407.015]
AstroCLIP: Multimodal contrastive pretraining for astronomical data

Lanusse, Francois; Parker, Liam; Golkar, Siavash; Cranmer, Miles; Bietti, Alberto; Eickenberg, Michael; Krawezik, Geraud; McCabe, Michael; Ohana, Ruben; Pettee, Mariel; Regaldo-Saint Blancard, Bruno; Tesileanu, Tiberiu; Cho, Kyunghyun; Ho, Shirley; Polymathic AI Collaboration

AstroCLIP performs contrastive pre-training between two different kinds of astronomical data modalities (multi-band imaging and optical spectra) to yield a meaningful embedding space which captures physical information about galaxies and is shared between both modalities. The embeddings can be used as the basis for competitive zero- and few-shot learning on a variety of downstream tasks, including similarity search, redshift estimation, galaxy property prediction, and morphology classification.

[submitted]
Flash-X: A Performance Portable, Multiphysics Simulation Software Instrument

Dubey, Anshu; Weide, Klaus; O'Neal, Jared; Dhruv, Akash; Couch, Sean; Harris, J. Austin; Klosterman, Tom; Jain, Rajeev; Rudi, Johann; Messer, Bronson; Pajkos, Michael; Carlson, Jared; Chu, Ran; Wahib, Mohamed; Chawdhary, Saurabh; Ricker, Paul M.; Lee, Dongwook; Antypas, Katie; Riley, Katherine M.; Daley, Christopher; Ganapathy, Murali; Timmes, Francis X.; Townsley, Dean M.; Vanella, Marcos; Bachan, John; Rich, Paul M.; Kumar, Shravan; Endeve, Eirik; Hix, W. Raphael; Mezzacappa, Anthony; Papatheodore, Thomas

Flash-X simulates physical phenomena in several scientific domains, primarily those involving compressible or incompressible reactive flows, using Eulerian adaptive mesh and particle techniques. It derives some of its solvers from and is a descendant of FLASH (ascl:1010.082). Flash-X has a new framework that relies on abstractions and asynchronous communications for performance portability across a range of heterogeneous hardware platforms, including exascale machines. It also includes new physics capabilities, such as the Spark general relativistic magnetohydrodynamics (GRMHD) solver, and supports interoperation with the AMReX mesh framework, the HYPRE linear solver package, and the Thornado neutrino radiation hydrodynamics package, among others.

[ascl:2407.016]
Heimdall: GPU accelerated transient detection pipeline for radio astronomy

Heimdall uses direct, tree, and sub-band dedispersion algorithms on massively parallel computing architectures (GPUs) to speed up real-time detection of radio pulsar and other transient events.

[submitted]
ELISA: Efficient Library for Spectral Analysis in High-Energy Astrophysics

Xue, Wang-Chen; Xiong, Shao-Lin; Li, Xiao-Bo; Xie, Sheng-Lun; Zheng, Chao; Zhang, Yan-Qiu; Liu, Jia-Cong

ELISA is a Python library designed for efficient spectral modeling and robust statistical inference. With user-friendly interface, ELISA streamlines the spectral analysis workflow.

The modeling framework of ELISA is flexible, allowing users to construct complex models by combining models of ELISA and XSPEC, as well as custom models. Parameters across different model components can also be linked. The models can be fitted to the spectral datasets using either Bayesian or maximum likelihood approaches. For Bayesian fitting, ELISA incorporates advanced Markov Chain Monte Carlo (MCMC) algorithms, including the No-U-Turn Sampler (NUTS), nested sampling, and affine-invariant ensemble sampling, to tackle the posterior sampling problem. For maximum likelihood estimation (MLE), ELISA includes two robust algorithms: the Levenberg-Marquardt algorithm and the Migrad algorithm from Minuit. The computation backend is based on Google's JAX, a high-performance numerical computing library, which can reduce the runtime for fitting procedures like MCMC, thereby enhancing the efficiency of analysis.

After fitting, goodness-of-fit assessment can be done with a single function call, which automatically conducts posterior predictive checks and leave-one-out cross-validation for Bayesian models, or parametric bootstrap for MLE. These methods offer greater accuracy and reliability than traditional fit-statistic/dof measures, and thus better model discovery capability. For comparing multiple candidate models, ELISA provides robust Bayesian tools such as the Widely Applicable Information Criterion (WAIC) and the Leave-One-Out Information Criterion (LOOIC), which are more reliable than AIC or BIC. Thanks to the object-oriented design, collecting the analysis results should be simple. ELISA also provide visualization tools to generate ready-for-publication figures.

ELISA is an open-source project and community contributions are welcome and greatly appreciated.

[ascl:2407.017]
photGalIMF: Stellar mass and luminosity evolution calculator

The photGalIMF code calculates the evolution of stellar mass and luminosity for a galaxy model, based on the PARSEC stellar evolution model (ascl:1502.005). It requires input lists specifying the age, mass, metallicity, and initial mass function (IMF) of single stellar populations. These input parameters can be provided by the companion galaxy chemical simulation code GalIMF (ascl:1903.010), which generates realistic sets of inputs.

[ascl:2407.018]
pony3d: Efficient island-finding tool for radio spectral line imaging

pony3d statistically identifies islands of contiguous emission inside a three-dimensional volume. The primary functionality is the rapid and reliable creation of masks for the deconvolution of radio interferometric radio spectral line emission. It has been designed to run on the output of the wsclean imager (ascl:1408.023) whereby the individual FITS image per frequency plane enables a high degree of parallelism, but can work on any image set providing this criterion is met. Single channel island rejection is offered, along with 3D mask dilation and boxcar averaging. pony3d is also a prototype source-finding and extraction tool.

[ascl:2407.019]
hipipe: VLT/HiRISE reduction pipeline

The High-Resolution Imaging and Spectroscopy of Exoplanets (HiRISE) instrument at the Very Large Telescope (VLT) combines the exoplanet imager SPHERE with the high-resolution spectrograph CRIRES using single-mode fibers. HiRISE has been designed to enable the characterization of known, directly-imaged planetary companions in the H band at a spectral resolution on the order of R = λ/∆λ = 140 000. The hipipe package is a custom python pipeline used to reduce the HiRISE data and produce high-level science products that can be used for astrophysical interpretation.

[ascl:2407.020]
Package-X: Calculate Feynman loop integrals

Package‑X instantly solves one loop Feynman integrals in full generality. Written in Mathematica and extensively tested and adopted, the package computes dimensionally regulated one-loop integrals with up to four distinct propagators of arbitrarily high rank, calculates traces of Dirac matrices in d dimensions for closed fermion loops, or carries out Dirac algebra for open fermion lines. Package‑X also generates analytic results for any kinematic configuration (*e.g.*, at zero external momentum or physical threshold) for real masses and external invariants, provides analytic expressions for UV-divergent, IR-divergent and finite parts either separately or all together, and computes discontinuities across cuts of one-loop integrals, among other tasks.

[submitted]
Sailfish: GPU-accelerated astrophysics gas dynamics code

Sailfish simulates accreting binary systems, including binary protostars, post-AGN stellar binaries, mass-transferring X-ray binaries, and double black hole systems. The binary components are "on the grid" rather than excised, and are evolved according to the Kepler two-body problem, modified to account for gravitational wave losses or self-consistent forcing from the orbiting gas. The solvers are shock-capturing and are second order accurate in space and time. Gravity is fully Newtonian. Thermodynamics can be treated using a gamma-law equation of state with a blackbody cooling term, or in the locally isothermal approximation, in which the gas temperature is set to a constant times the local free-fall speed. Sailfish is fully Cartesian and has extensive diagnostic capabilities to facilitate accurate calculations of gas-driven orbital evolution or the extraction of electromagnetic disk signatures. The code is extremely efficient, reaching more than one billion zone updates per second on an NVIDIA A100 GPU, enabling extremely high resolution of complex flows around the binary components.

[submitted]
Sonification of Astronomical Data

Visual displays have always been the preferential method of representing scientific information. However, the need for new inclusivity strategies and the significant perceptual advantages of hearing validate the exploration of alternative methods of communication. This analysis aims to show the potential of using sonification, an data-to-audio mapping technique, as a data analysis tool in scientific research. An optimal approach to sound design was established through the application of psychoacoustic arguments and then implemented to translate information encoded in the light curves of astronomical events from the Fermi-LAT and Kepler imaging systems. The development of a code and based on digital image processing techniques allowed the auditory display to support the spatial positioning of the numerical data rather than their temporal evolution. A series of pictures of muonic and hadron-initiated events were selected for the application of this method. The finished product was then calibrated through the determination of the pitch just noticeable difference and the construction of the isophon contours. The findings are based on binaurally inputted pure tone sinusoidal waveforms played through a signal carrier vehicle. The maximum depth of perception was found to lie in the range between 4000 Hz and 6000 Hz, with a threshold resolution to minimum frequency variations between 0.7% and 1.8% of the original stimulus, with the exception of a lower threshold level at 100 Hz. Curve fitting of the loudness contours through polynomial regression resulted in the proposal of a new relationship to represent the isophones which suggests an oscillatory response of the cochlear membrane to frequency variations. The effectiveness of the sonification design was assessed through the ‘Whispers from the Universe” project, hosted publicly on Zooniverse.

The website users showed a correct answer level of over 60% for three of the matching-based tasks, with this value decreasing to 37.5% in auditory display of visually similar light curves. Demographic diversity also appears to be correlated with auditory information processing, with age and preexisting physics knowledge improving performance by 30% and 40% respectively. Marginal improvements have been found to originate from previous training, a musical background, and the absence of hearing impairments. These findings validate the hypothesis made at the start of this research project, hence showing the potential of sonification as a stand-alone method or in aid of traditional data analysis techniques.

[submitted]
LADDER - Learning Algorithm for Deep Distance Estimation and Reconstruction

LADDER is a deep learning framework originally developed to reconstruct the “cosmic distance ladder” by analyzing sequential cosmological data. It utilizes the apparent magnitude data from the Pantheon Type Ia supernovae compilation, fully incorporating covariance information to accurately predict mean values and uncertainties. However, it’s applications extend to other sequential datasets with associated covariance information. Once trained with a particular dataset, e.g. Pantheon, LADDER can have various cosmological applications. It offers model-independent consistency checks for datasets like Baryon Acoustic Oscillations (BAO), and can calibrate high-redshift datasets such as Gamma Ray Bursts (GRBs) without assuming any underlying cosmological model. Additionally, it serves as a model-independent mock catalog generator for forecast-based cosmological studies. LADDER is aimed at emphasizing the thoughtful integration of machine learning in cosmology.

[ascl:2408.001]
SDR: Sharpened Dimensionality Reduction

Sharpened dimensionality reduction (SDR) sharpens original data before dimensionality reduction to create visually segregrated sample clusters. user-guided labeling. Each distinct cluster can then be labeled and used to further analyze an otherwise unlabeled data set. Written in C++, SDR scales well with large high-dimensional data.

[ascl:2408.002]
pySDR: Wrapper for sharpened dimensionality reduction code

pySDR performs local gradient clustering-based sharpened dimensionality reduction (SDR). The library uses the C++ LGCDR_v1 code as its backend.

[ascl:2408.003]
SHARC: SHArpened Dimensionality Reduction and Classification

SHARC (SHArpened Dimensionality Reduction and Classification) performs local gradient clustering-based sharpened dimensionality reduction (SDR) using neural network projections and uses these projections to make classifications. The library also contains functions for finding the optimal SDR parameters and for consolidating classification results obtained through multiple classifiers. It requires pySDR (ascl:2408.002). SHARC provides accurate and physically insightful classification of astronomical objects based on their broadband colors.

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