Natal Kicks of Compact Binaries: I. Population Synthesis Framework and Illustrative Examples
Year: 2026
Type: article
Abstract: The natal kicks imparted to neutron stars and black holes at birth are critical for understanding massive-star evolution, compact-object formation, and compact binary merger rates. However, constraining these kicks is challenging, since they cannot be directly observed. Here, we outline a framework for inferring natal kick probability distributions for compact binaries. Our method embeds the rapid binary-population code COSMIC in a Monte-Carlo workflow that evolves ensembles of simulated binaries. From those which host compact objects, we can select subsets that match the observational characteristics of any given system (e.g. component masses, orbital period, mass-transfer status, systemic velocity). As a proof-of-concept, we apply this framework to six compact binaries spanning a wide range of companion masses and compact-object types. For each system, we identify the evolutionary channel(s) that could have led to the current observational characteristics, and isolate regions of allowed natal kicks and progenitor parameter space. We reconfirm that at least one black hole system, XTE J1118+480, must have received a natal kick υNK > 100 km s−1. The neutron-star system, GX 301–2, is also inferred to have received an impulsive kick exceeding 280 km s−1. We discuss the strengths and limitations associated with population-synthesis-based inference methods and explore the effects of adopting different prescriptions for critical binary evolution phases. We provide a detailed demonstration of the non-linear relationship between υNK and υsys, highlighting the shortcomings of relying on observations alone to measure natal kicks. The inferred kick velocity distributions across our benchmark sample are generally consistent with previous constraints.
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,046
OpenAlex ID: W7220689132