Context. Fast radio bursts (FRBs) are radio transients of an unknown origin whose nature we wish to determine. The number of detected FRBs is large enough for a statistical approach to parts of this challenge to be feasible. Aims. Our goal is to determine the current best-fit FRB population model. Our secondary aim is to provide an easy-to-use tool for simulating and understanding FRB detections. This tool can compare surveys, or provide information about the intrinsic FRB population. Methods. To understand the crucial link between detected FRBs and the underlying FRB source classes, we performed an FRB population synthesis to determine how the underlying population behaves. The Python package we developed for this synthesis, frbpoppy , is open source and freely available. frbpoppy simulates intrinsic FRB populations and the surveys that find them with the aim to produce virtual observed populations. These populations can then be compared with real data, which allows constraints to be placed on the underlying physics and selection effects. Results. We are able to replicate real Parkes and ASKAP FRB surveys in terms of detection rates and observed distributions. We also show the effect of beam patterns on the observed dispersion measure distributions. We compare four types of source models. The “complex” model, featuring a range of luminosities, pulse widths, and spectral indices, reproduces current detections best. Conclusions. Using frbpoppy , an open-source FRB population synthesis package, we explain current FRB detections and offer a first glimpse of what the true population must be.
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