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[ascl:1507.004]
L-PICOLA: Fast dark matter simulation code

L-PICOLA generates and evolves a set of initial conditions into a dark matter field and can include primordial non-Gaussianity in the simulation and simulate the past lightcone at run-time, with optional replication of the simulation volume. It is a fast, distributed-memory, planar-parallel code. L-PICOLA is extremely useful for both current and next generation large-scale structure surveys.

[ascl:1907.023]
REVOLVER: REal-space VOid Locations from suVEy Reconstruction

REVOLVER reconstructs real space positions from redshift-space tracer data by subtracting RSD through FFT-based reconstruction (optional) and applies void-finding algorithms to create a catalogue of voids in these tracers. The tracers are normally galaxies from a redshift survey but could also be halos or dark matter particles from a simulation box. Two void-finding routines are provided. The first is based on ZOBOV (ascl:1304.005) and uses Voronoi tessellation of the tracer field to estimate the local density, followed by a watershed void-finding step. The second is a voxel-based method, which uses a particle-mesh interpolation to estimate the tracer density, and then uses a similar watershed algorithm. Input data files can be in FITS format, or ASCII- or NPY-formatted data arrays.

[ascl:2009.022]
Harmonia: Hybrid-basis inference for large-scale galaxy clustering

Harmonia combines clustering statistics decomposed in spherical and Cartesian Fourier bases for large-scale galaxy clustering likelihood analysis. Optimal weighting schemes for spherical Fourier analysis can also be readily implemented using the code.

[ascl:2106.018]
picca: Package for Igm Cosmological-Correlations Analyses

du Mas des Bourboux, Hélion; Rich, James; Font-Ribera, Andreu; de Sainte Agathe, Victoria; Farr, James; Etourneau, Thomas; Le Goff, Jean-Marc; Cuceu, Andrei; Balland, Christophe; Bautista, Julian E.; Blomqvist, Michael; Brinkmann, Jonathan; Brownstein, Joel R.; Chabanier, Solène; Chaussidon, Edmond; Dawson, Kyle; González-Morales, Alma X.; Guy, Julien; Lyke, Brad W.; de la Macorra, Axel; Mueller, Eva-Maria; Myers, Adam D.; Nitschelm, Christian; Muñoz Gutiérrez, Andrea; Palanque-Delabrouille, Nathalie; Parker, James; Percival, Will J.; Pérez-Ràfols, Ignasi; Petitjean, Patrick; Pieri, Matthew M.; Ravoux, Corentin; Rossi, Graziano; Schneider, Donald P.; Seo, Hee-Jong; Slosar, Anže; Stermer, Julianna; Vivek, M.; Yèche, Christophe; Youles, Samantha

picca fits continua of forests, computes correlation functions (1D and 3D) and power-spectra (1D), computes covariance matrices, and fits models for the correlation functions. This set of tools is used for the analysis of the Lyman-alpha forest sample from the extended Baryon Oscillation Spectroscopic Survey (eBOSS) and the Dark Energy Spectroscopic Instrument (DESI).

[ascl:2312.015]
SUNBIRD: Neural-network-based models for galaxy clustering

Cuesta-Lazaro, Carolina; Paillas, Enrique; Yuan, Sihan; Cai, Yan-Chuan; Nadathur, Seshadri; Percival, Will J.; Beutler, Florian; de Mattia, Arnaud; Eisenstein, Daniel; Forero-Sanchez, Daniel; Padilla, Nelson; Pinon, Mathilde; Ruhlmann-Kleider, Vanina; Sánchez, Ariel G.; Valogiannis, Georgios; Zarrouk, Pauline

SUNBIRD trains neural-network-based models for galaxy clustering. It also incorporates pre-trained emulators for different summary statistics, including galaxy two-point correlation function, density-split clustering statistics, and old-galaxy cross-correlation function. These models have been trained on mock galaxy catalogs, and were calibrated to work for specific samples of galaxies. SUNBIRD implements routines with PyTorch to train new neural-network emulators.