MaNGIA: 10,000 mock galaxies for stellar population analysis

Abstract

Modern astronomical observations give unprecedented access to the physical properties of nearby galaxies, including spatially resolved stellar populations. However, observations can only give a present-day view of the Universe, whereas cosmological simulations give access to the past record of the processes that galaxies have experienced in their evolution. To connect the events that happened in the past with galactic properties as seen today, simulations must be taken to a common ground before being compared to observations. We emulate data from the MaNGA survey, which is the largest integral field spectroscopic galaxy survey to date with its 10,000 nearby galaxies of all types. For this, we use the cosmological simulations TNG50 to generate MaNGIA (Mapping Nearby Galaxies with IllustrisTNG Astrophysics), a mock MaNGA sample of similar size that emulates observations of galaxies for stellar population analysis. We choose TNG galaxies to match the MaNGA sample selection to limit the impact of selection effects. We produce MaNGA-like datacubes from all simulated galaxies, and process these with the pyPipe3D analysis code. This allows us to extract spatially resolved stellar maps. This first paper presents the approach to generate the mock sample and provides an initial exploration of its properties. We show that the stellar populations and kinematics of the simulated MaNGIA galaxies are overall in good agreement with observations. Specific discrepancies, especially in the age and metallicity gradients in low- to intermediate-mass regimes and in massive galaxies' kinematics, require further investigation. We compare our results to other attempts to mock similar observations, all of smaller data sets. Our final dataset will be released with the publication, consisting of >10,000 post-processed data-cubes analysed with pyPipe3D, along with the codes developed to create it.

Submitted to A&A, 21 pages, 17 figures, 1 table

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Cited by in corpus (11)

• Stellar Populations With Optical Spectra: Deep Learning vs. Popular Spectrum Fitting Codes
• Spatially resolved mock observations of stellar kinematics: full radiative transfer treatment of simulated galaxies
• ERGO-ML: Towards a robust machine learning model for inferring the fraction of accreted stars in galaxies from integral-field spectroscopic maps
• The evolution of radial gradients of MaNGA quiescent elliptical galaxies: inside-out quenching or outer mass growth?
• ERGO-ML: Comparing IllustrisTNG and HSC galaxy images via contrastive learning
• iMaNGA: mock MaNGA galaxies based on IllustrisTNG and MaStar SSPs -- II. The catalogue
• Measuring the physical imprints of gas flows in galaxies I: Accretion rate histories
• GalaxyGenius: Mock galaxy image generator for various telescopes from hydrodynamical simulations
• Mock Observations: Morphological Analysis of Galaxies in TNG100 Simulations
• The spatially-resolved effect of mergers on the stellar mass assembly of MaNGA galaxies
• From simulations to observations. Methodology and data release of mock TNG50 galaxies at 0.3 < z < 0.7 for WEAVE-StePS