The MillenniumTNG Project: semi-analytic galaxy formation models on the past lightcone - Barrera, Monica; Springel, Volker; White, Simon D M; Hernández-Aguayo, César; Hernquist, Lars; Frenk, Carlos; Pakmor, Rüdiger; Ferlito, Fulvio; Hadzhiyska, Boryana; Delgado, Ana Maria; Kannan, Rahul; Bose, Sownak (Monthly notices of the Royal Astronomical Society, 2023)
ABSTRACT Upcoming large galaxy surveys will subject the standard cosmological model, Lambda Cold Dark Matter, to new precision tests. These can be tightened considerably if theoretical models of galaxy formation are available that can predict galaxy clustering and galaxy–galaxy lensing on the full range of measurable scales, throughout volumes as large as those of the surveys, and with sufficient flexibility that uncertain aspects of the underlying astrophysics can be marginalized over. This, in particular, requires mock galaxy catalogues in large cosmological volumes that can be directly compared to observation, and can be optimized empirically by Monte Carlo Markov Chains or other similar schemes, thus eliminating or estimating parameters related to galaxy formation when constraining cosmology. Semi-analytic galaxy formation methods implemented on top of cosmological dark matter simulations offer a computationally efficient approach to construct physically based and flexibly parametrized galaxy formation models, and as such they are more potent than still faster, but purely empirical models. Here, we introduce an updated methodology for the semi-analytic L-Galaxies code, allowing it to be applied to simulations of the new MillenniumTNG project, producing galaxies directly on fully continuous past lightcones, potentially over the full sky, out to high redshift, and for all galaxies more massive than $\sim 10^8\, {\rm M}_\odot$. We investigate the numerical convergence of the resulting predictions, and study the projected galaxy clustering signals of different samples. The new methodology can be viewed as an important step towards more faithful forward-modelling of observational data, helping to reduce systematic distortions in the comparison of theory to observations.