  In this paper, we introduce a new algorithm for rare event estimation based
on adaptive importance sampling. We consider a smoothed version of the optimal
importance sampling density, which is approximated by an ensemble of
interacting particles. The particle dynamics is governed by a McKean-Vlasov
stochastic differential equation, which was introduced and analyzed in
(Carrillo et al., Stud. Appl. Math. 148:1069-1140, 2022) for consensus-based
sampling and optimization of posterior distributions arising in the context of
Bayesian inverse problems. We develop automatic updates for the internal
parameters of our algorithm. This includes a novel time step size controller
for the exponential Euler method, which discretizes the particle dynamics. The
behavior of all parameter updates depends on easy to interpret accuracy
criteria specified by the user. We show in numerical experiments that our
method is competitive to state-of-the-art adaptive importance sampling
algorithms for rare event estimation, namely a sequential importance sampling
method and the ensemble Kalman filter for rare event estimation.
