Impact probability computation of Near-Earth Objects using Monte Carlo Line Sampling and Subset Simulation

TL;DR

This paper presents two Monte Carlo-based sampling methods, line sampling and subset simulation, to enhance impact probability estimation of Near-Earth Objects, achieving higher accuracy or reduced computational effort compared to standard methods.

Contribution

Introduction of two novel Monte Carlo sampling techniques, line sampling and subset simulation, for more efficient and accurate asteroid impact risk assessment.

Findings

Both methods improve accuracy over standard MC.

They reduce the number of samples needed for reliable estimates.

Sensitivity analysis shows parameter settings significantly affect results.

Abstract

This work introduces two Monte Carlo (MC)-based sampling methods, known as line sampling and subset simulation, to improve the performance of standard MC analyses in the context of asteroid impact risk assessment. Both techniques sample the initial uncertainty region in different ways, with the result of either providing a more accurate estimate of the impact probability or reducing the number of required samples during the simulation with respect to standard MC techniques. The two methods are first described and then applied to some test cases, providing evidence of the increased accuracy or the reduced computational burden with respect to a standard MC simulation. Finally, a sensitivity analysis is carried out to show how parameter setting affects the accuracy of the results and the numerical efficiency of the two methods.