MaNGIA: 10 000 mock galaxies for stellar population analysis

Abstract

Context. 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. Therefore, a dedicated effort is needed to forward-model simulations into the observational plane. Aims. We emulate data from the Mapping Nearby Galaxies at Apache Point Observatory (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 latest hydro-cosmological simulations IllustrisTNG 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. Methods. We chose TNG galaxies to match the MaNGA sample selection in terms of mass, size, and redshift in order to limit the impact of selection effects. We produced MaNGA-like datacubes from all simulated galaxies, and processed them with the stellar population analysis code pyPipe3D. This allowed us to extract spatially resolved maps of star formation history, age, metallicity, mass, and kinematics, following the same procedures used as part of the official MaNGA data release. Results. This first paper presents the approach used 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 the kinematics of massive galaxies, require further investigation, but are likely to uncover new physical understanding. We compare our results to other attempts to mock similar observations, all of smaller datasets. Conclusions. Our final dataset is released with this publication, consisting of ≳10 000 post-processed datacubes analysed with pyPipe3D, along with the codes developed to create it. Future work will employ modern machine learning and other analysis techniques to connect observations of nearby galaxies to their cosmological evolutionary past.