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| GALFORM | |
|---|---|
| Name | GALFORM |
| Developer | Durham University |
| Released | 1990s |
| Programming language | Fortran, C |
| Platform | Unix-like, Linux |
| Genre | Semi-analytic model |
GALFORM
GALFORM is a semi-analytic computational model for galaxy formation developed to predict the properties of galaxies within a cosmological context. The code links theoretical frameworks from Lambda-CDM cosmology, dark matter halo formation, and baryonic physics to observable quantities such as luminosity functions, stellar mass functions, and morphological fractions. Its development has involved collaborations among researchers at Durham University, the University of Cambridge, and research groups connected to instruments like the Sloan Digital Sky Survey and the James Webb Space Telescope.
GALFORM is a semi-analytic model that populates merger trees of cold dark matter haloes with baryonic prescriptions for cooling, star formation, feedback, and chemical enrichment. The model integrates halo merger histories from N-body simulations such as the Millennium Simulation and the Bolshoi Simulation with analytic treatments inspired by studies of radiative cooling, supernova feedback, and active galactic nucleus feedback. Outputs are compared to observational datasets from surveys including the Two Micron All Sky Survey, the Hubble Ultra-Deep Field, and the Galaxy And Mass Assembly survey.
GALFORM traces its origins to semi-analytic frameworks developed in the 1990s by groups led at Durham University and refined through collaborations with teams at the Max Planck Institute for Astrophysics, the Kavli Institute for Cosmology, Cambridge, and the Institute for Computational Cosmology. Key milestones include incorporation of physically motivated AGN feedback following work by researchers at the Institute of Astronomy, Cambridge and calibration against the Stellar mass function measurements from the Two Degree Field Galaxy Redshift Survey and the Sloan Digital Sky Survey. The model evolved through successive versions to accommodate results from large-scale simulations like Millennium-II and the IllustrisTNG comparisons performed by international consortia.
GALFORM implements prescriptions for halo merger trees, gas accretion, radiative cooling informed by Sutherland & Dopita cooling curves, angular momentum retention following studies at the California Institute of Technology, and star formation laws based on empirical relations such as the Kennicutt–Schmidt law. Feedback modules include energy-driven supernova feedback inspired by observations from the Chandra X-ray Observatory and momentum-driven AGN feedback modeled with guidance from theoretical work at the Harvard-Smithsonian Center for Astrophysics. Chemical enrichment tracks yields from populations studied in works associated with the European Southern Observatory and incorporates stellar population synthesis models like Bruzual & Charlot and Maraston to produce spectral energy distributions comparable to data from the Very Large Telescope and the Spitzer Space Telescope.
Parameter choices in GALFORM are calibrated against observational benchmarks such as the luminosity functions measured by the Sloan Digital Sky Survey, stellar mass functions from the COSMOS survey, and star formation histories inferred from the Hubble Space Telescope. Calibration employs statistical techniques developed in the Bayesian tradition and optimization methods used in studies at the University of Oxford and the University College London to match constraints from the Cosmic Microwave Background measurements by Planck and structure growth traced by the Baryon Oscillation Spectroscopic Survey. Key parameters control efficiencies of cooling, star formation, supernova reheating, AGN heating, and metal yields tied to nucleosynthesis studies at the Max Planck Institute for Astrophysics.
Researchers use GALFORM to predict galaxy luminosity functions, color bimodality, morphological fractions, clustering statistics, and the evolution of the stellar mass density across redshift, with comparisons to datasets from the Hubble Space Telescope, Subaru Telescope, and the Atacama Large Millimeter/submillimeter Array. GALFORM has been applied to forecast galaxy populations for survey planning for facilities such as the Euclid mission, the Vera C. Rubin Observatory, and the James Webb Space Telescope, and to interpret observations of high-redshift galaxies found in programs like the Hubble Deep Field and the COSMOS field.
GALFORM differs from hydrodynamical simulations such as Illustris, EAGLE, and Horizon-AGN by using semi-analytic prescriptions that are computationally cheaper and allow exploration of large parameter spaces, similar in spirit to models like L-GALAXIES and Santa Cruz SAM. Comparisons focus on stellar mass functions, metallicity relations, and galaxy clustering where work by teams at the Max Planck Institute for Astrophysics and the Leiden Observatory have highlighted strengths and trade-offs between semi-analytic approaches and full hydrodynamics in reproducing observations from surveys like the Sloan Digital Sky Survey and the DEEP2 Galaxy Redshift Survey.
Limitations of GALFORM include simplifying assumptions about baryonic processes compared to full hydrodynamical treatments developed at the Princeton University and numerical resolution dependencies tied to underlying N-body simulations like the Millennium Simulation. Future directions include coupling to improved merger trees from exascale simulations led by collaborations at the Argonne National Laboratory and implementing feedback physics informed by multi-wavelength observations from instruments like ALMA and the Nancy Grace Roman Space Telescope. Ongoing development aims to integrate machine learning calibration techniques from research groups at Google DeepMind and Lawrence Berkeley National Laboratory to enhance predictive power.
Category:Galaxy formation models