Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Machine-learning interpolation of population-synthesis simulations to
  interpret gravitational-wave observations: a case study   We report on advances to interpret current and future gravitational-wave
events in light of astrophysical simulations. A machine-learning emulator is
trained on numerical population-synthesis predictions and inserted into a
Bayesian hierarchical framework. In this case study, a modest but
state-of-the-art suite of simulations of isolated binary stars is interpolated
across two event parameters and one population parameter. The validation
process of our pipelines highlights how omitting some of the event parameters
might cause errors in estimating selection effects, which propagates as
systematics to the final population inference. Using LIGO/Virgo data from O1
and O2 we infer that black holes in binaries are most likely to receive natal
kicks with one-dimensional velocity dispersion $\sigma$ = 105+44 km/s. Our
results showcase potential applications of machine-learning tools in
conjunction with population-synthesis simulations and gravitational-wave data.

28
The categories of this paper are: alg-geom
1
The categories of this paper are: cs
32
The categories of this paper are: atom-ph
36
The categories of this paper are: cmp-lg
26
The categories of this paper are: bayes-an
0
The categories of this paper are: hep-ph
7
The categories of this paper are: hep-th
23
The categories of this paper are: solv-int
33
The categories of this paper are: plasm-ph
30
The categories of this paper are: q-alg
22
The categories of this paper are: chao-dyn
9
The categories of this paper are: nlin
14
The categories of this paper are: math-ph
21
The categories of this paper are: adap-org
37
The categories of this paper are: supr-con
18
The categories of this paper are: econ