This article was accepted into the corpus but its outbound wikilinks were never NER-processed — typical at the deepest BFS hop or when the run's entity cap was reached. No expansion funnel to show.
| RedMaPPer | |
|---|---|
| Name | RedMaPPer |
| Developer | Harvard University Smithsonian Astrophysical Observatory Lawrence Berkeley National Laboratory |
| Released | 2014 |
| Latest release | 6.3 |
| Programming language | Python (programming language) Cython |
| Genre | Astronomical data analysis software |
| License | BSD |
RedMaPPer is a photometric galaxy cluster finder and richness estimator developed for wide-field imaging surveys. It combines statistical models of galaxy colors with probabilistic membership assignment to detect galaxy clusters and estimate cluster-centric properties. The software has been applied to major surveys and integrated into pipelines used by collaborations and institutions in observational cosmology.
RedMaPPer was designed to identify galaxy clusters by exploiting the red-sequence of early-type galaxies in multiband imaging. The algorithm was motivated by needs from projects such as the Sloan Digital Sky Survey, Dark Energy Survey, Pan-STARRS, Kilo-Degree Survey, and Euclid (spacecraft). Development involved researchers affiliated with Harvard University, Smithsonian Astrophysical Observatory, Lawrence Berkeley National Laboratory, and collaborations including the Dark Energy Survey Collaboration and the SDSS Collaboration. RedMaPPer outputs include cluster catalogs, photometric redshifts, and probabilistic membership lists used by teams working with data from instruments like the Dark Energy Camera, Subaru Telescope, Canada–France–Hawaii Telescope, and pipelines run at centers such as National Energy Research Scientific Computing Center.
The core methodology fits an empirical model of the red-sequence color–magnitude relation as a function of redshift, using matched-filter techniques and maximum-likelihood estimation. It uses radial filters centered on candidate brightest cluster galaxy candidates and iteratively refines cluster centers and richness estimates. The approach integrates photometric redshift priors, luminosity functions, and radial profiles informed by studies from groups associated with Planck (spacecraft), Atacama Cosmology Telescope, South Pole Telescope, and X-ray studies from the Chandra X-ray Observatory and XMM-Newton. Key algorithmic components draw on statistical techniques common to teams at Fermilab, SLAC National Accelerator Laboratory, and analysis packages developed for LSST (Vera C. Rubin Observatory) science verification.
RedMaPPer requires calibrated, multiband photometric catalogs with appropriate magnitude systems and star–galaxy separation. Typical inputs are imaging catalogs from surveys like Sloan Digital Sky Survey, Dark Energy Survey, Pan-STARRS, Kilo-Degree Survey, Hyper Suprime-Cam Subaru Strategic Program, and space missions such as Euclid (spacecraft). Ancillary datasets used for validation or joint analyses include spectroscopic redshift samples from Baryon Oscillation Spectroscopic Survey, DEEP2 Redshift Survey, and VIPERS, as well as X-ray catalogs from ROSAT and Chandra X-ray Observatory. Team collaborations often incorporate contrast checks against microwave background measurements from Planck (spacecraft), Atacama Cosmology Telescope, and South Pole Telescope for Sunyaev–Zel'dovich effect confirmation.
Performance validation uses spectroscopic training sets and cross-matching with external cluster catalogs from X-ray, Sunyaev–Zel'dovich, and lensing studies. RedMaPPer’s photometric redshift accuracy and richness–mass scaling relations have been compared against weak-lensing mass calibrations from analyses using Hubble Space Telescope imaging and ground-based shape catalogs produced by teams working with CFHTLenS and DES Year 1. Metrics reported in the literature include completeness, purity, and scatter in richness–mass relations, with benchmarking against catalogs from Planck (spacecraft), ROSAT, Chandra X-ray Observatory, and SZ-selected samples from South Pole Telescope. Validation pipelines have been exercised on data reprocessed in environments at Lawrence Berkeley National Laboratory and national data facilities such as NERSC.
RedMaPPer catalogs have been used in cosmological analyses constraining parameters like the matter density parameter and amplitude of matter fluctuations through cluster-count studies performed by collaborations including Dark Energy Survey Collaboration and surveys affiliated with SDSS Collaboration. Catalogs support cross-correlation studies with cosmic microwave background maps from Planck (spacecraft) and Atacama Cosmology Telescope, weak-lensing mass calibration with Hubble Space Telescope and ground-based surveys, environmental studies of galaxy evolution with data from GALEX and WISE (satellite), and follow-up target selection for spectroscopic programs such as DESI and 2dF Galaxy Redshift Survey. Cluster samples have also been used in investigations of baryonic feedback, scaling relations reported in works associated with XMM-Newton teams, and multiwavelength studies involving Spitzer Space Telescope and radio observatories like VLA.
The initial public implementation appeared in the mid-2010s and has seen multiple releases introducing improvements to center-finding, richness estimation, and handling of survey systematics. Development has been coordinated by groups at Harvard University, Smithsonian Astrophysical Observatory, and Lawrence Berkeley National Laboratory, with contributions from collaborators associated with Fermilab, SLAC National Accelerator Laboratory, and the Dark Energy Survey Collaboration. Releases are often accompanied by application to contemporaneous survey data such as SDSS Data Release 8, DES Year 1, and later survey releases associated with Pan-STARRS and HSC SSP; pipelines interface with community tools like Astropy and survey-specific calibration frameworks. Continuous integration and testing have been practiced in computational environments supported by NERSC and institutional clusters.
Category:Astronomical software