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The heaviest elements in the universe are synthesized through rapid neutron capture (r-process) in extremely neutron-rich outflows. Neutron star mergers were established as an importantr-process source through the multimessenger observation of GW170817. Collapsars were also proposed as a potentially major source of heavy elements; however, this is difficult to probe through optical observations due to contamination by other emission mechanisms. Here we present observational constraints onr-process nucleosynthesis by collapsars based on radio follow-up observations of nearby long gamma-ray bursts (GRBs). We make the hypothesis that late-time radio emission arises from the collapsar wind ejecta responsible for forgingr-process elements, and consider the constraints that can be set on this scenario using radio observations of a sample of Swift/Burst Alert Telescope GRBs located within 2 Gpc. No radio counterpart was identified in excess of the radio afterglow of the GRBs in our sample. This gives the strictest limit to the collapsarr-process contribution of ≲0.2M_⊙for GRB 060505 and GRB 05826, under the models we considered. Our results additionally constrain energy injection by a long-lived neutron star remnant in some of the considered GRBs. While our results are in tension with collapsars being the majority ofr-process production sites, the ejecta mass and velocity profile of collapsar winds, and the emission parameters, are not yet well modeled. As such, our results are currently subject to large uncertainties, but further theoretical work could greatly improve them.

 

Abstract The observed distributions of the source properties from gravitational-wave (GW) detections are biased due to the selection effects and detection criteria in the detections, analogous to the Malmquist bias. In this work, this observation bias is investigated through its fundamental statistical and physical origins. An efficient semi-analytical formulation for its estimation is derived, which is as accurate as the standard method of numerical simulations, with only a millionth of the computational cost. Then, the estimated bias is used for unmodeled inferences on the binary black hole population. These inferences show additional structures, specifically two peaks in the joint mass distribution around binary masses ∼10 M ⊙ and ∼30 M ⊙ . Example ready-to-use scripts and some produced data sets for this method are shared in an online repository.