In this article, we present an event-driven algorithm that generalizes the recent hard-sphere event-chain Monte Carlo method without introducing discretizations in time or in space. A factorization of the Metropolis filter and the concept of infinite
simal Monte Carlo moves are used to design a rejection-free Markov-chain Monte Carlo algorithm for particle systems with arbitrary pairwise interactions. The algorithm breaks detailed balance, but satisfies maximal global balance and performs better than the classic, local Metropolis algorithm in large systems. The new algorithm generates a continuum of samples of the stationary probability density. This allows us to compute the pressure and stress tensor as a byproduct of the simulation without any additional computations.
We investigate the relation of the stellar initial mass function (IMF) and the dense core mass function (CMF), using stellar masses and positions in 14 well-studied young groups. Initial column density maps are computed by replacing each star with a
model initial core having the same star formation efficiency (SFE). For each group the SFE, core model, and observational resolution are varied to produce a realistic range of initial maps. A clumpfinding algorithm parses each initial map into derived cores, derived core masses, and a derived CMF. The main result is that projected blending of initial cores causes derived cores to be too few and too massive. The number of derived cores is fewer than the number of initial cores by a mean factor 1.4 in sparse groups and 5 in crowded groups. The mass at the peak of the derived CMF exceeds the mass at the peak of the initial CMF by a mean factor 1.0 in sparse groups and 12.1 in crowded groups. These results imply that in crowded young groups and clusters, the mass distribution of observed cores may not reliably predict the mass distribution of protostars which will form in those cores.
