  Two-line elements are widely used for space operations to predict the orbit
with a moderate accuracy for 2-3 days. Local optimization methods, such as the
nonlinear least squares method with differential corrections, can estimate a
TLE as long as there exists an initial estimate that provides the desired
precision. Global optimization methods to estimate TLEs are computationally
intensive, and estimating a large number of them is prohibitive. In this paper,
the feasibility of estimating TLEs using machine learning methods is
investigated. First, a Monte-Carlo approach to estimate a TLE, when there are
no initial estimates that provide the desired precision, is introduced. The
proposed Monte-Carlo method is shown to estimate TLEs with residual mean
squared errors below 1 km for space objects with varying area-to-mass ratios
and orbital characteristics. Second, gradient boosting decision trees and
fully-connected neural networks are trained to map the orbital evolution of
space objects to the associated TLEs using 8 million publicly available TLEs
from the US space catalog. The desired precision in the mapping to estimate a
TLE is achieved for one of the three test cases, which is a low area-to-mass
ratio space object.
