The Initial Mass Function as the Equilibrium State of a Variational Process: why the IMF cannot be sampled stochastically

TL;DR

This paper demonstrates that the stellar initial mass function (sIMF) naturally arises from the Maximum Entropy principle applied to star-forming clump fragmentation, supporting a deterministic rather than stochastic sampling process.

Contribution

It establishes a first-principles derivation of the sIMF from entropy maximization, linking physical fragmentation processes to the observed mass distribution.

Findings

The sIMF emerges from maximum entropy applied to star-forming clumps.

Deviations from the sIMF violate the Maximum Entropy principle.

Optimal sampling is physically justified by entropy considerations.

Abstract

The stellar initial mass function (sIMF) is often treated as a stochastic probability distribution, yet such an interpretation implies Poisson noise that is inconsistent with growing observational evidence. In particular, the observed relation between the mass of the most massive star formed in an embedded cluster and the cluster's total stellar mass supports a deterministic sampling process, known as optimal sampling. However, the physical origin of optimal sampling has not been formally established in the literature. In this work, we show that the stellar mass distribution implied by optimal sampling emerges from applying the Maximum Entropy principle to the fragmentation of star-forming clumps, whose structure is set by density-dependent cooling in the optically thin regime. Here, the maximum entropy leads to unbiased distributions. By applying calculus of variations to minimize the entropy functional obtained assuming fragmentation, we recover the power-law form of the sIMF, and we show that any distribution deviating from the sIMF violates the Maximum Entropy principle. This work provides a first-principles foundation for the deterministic nature of star formation. Thus, the sIMF is the distribution resulting from a maximally unbiased system.