The optimally-sampled galaxy-wide stellar initial mass function - Observational tests and the publicly available GalIMF code
Z. Yan, T. Jerabkova, P. Kroupa

TL;DR
This paper introduces a deterministic optimal sampling method for the galaxy-wide initial mass function (IGIMF), compares it with observations, and provides a Python tool for calculating the IGIMF and related models based on galaxy properties.
Contribution
It presents a new optimal sampling approach for the IGIMF, demonstrating its consistency with empirical relations and providing a publicly available Python module for practical applications.
Findings
IGIMF aligns with observed cluster and galaxy relations
GalIMF code enables calculation of IGIMF and OSGIMF
Predictions about supernova occurrence based on SFR
Abstract
Here we present a full description of the integrated galaxy-wide initial mass function (IGIMF) theory in terms of the optimal sampling and compare it with available observations. Optimal sampling is the method we use to discretize the IMF into stellar masses deterministically. Evidence has been indicating that nature may be closer to deterministic sampling as observations suggest a smaller scatter of various relevant observables than random sampling would give, which may result from a high level of self-regulation during the star formation process. The variation of the IGIMFs under various assumptions are documented. The results of the IGIMF theory are consistent with the empirical relation between the total mass of a star cluster and the mass of its most massive star, and the empirical relation between a galaxy's star formation rate (SFR) and the mass of its most massive cluster.…
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Taxonomy
TopicsGalaxies: Formation, Evolution, Phenomena · Gamma-ray bursts and supernovae · Stellar, planetary, and galactic studies
