  We demonstrate the use of symbolic regression in deriving analytical
formulas, which are needed at various stages of a typical experimental analysis
in collider phenomenology. As a first application, we consider kinematic
variables like the stransverse mass, $M_{T2}$, which are defined
algorithmically through an optimization procedure and not in terms of an
analytical formula. We then train a symbolic regression and obtain the correct
analytical expressions for all known special cases of $M_{T2}$ in the
literature. As a second application, we reproduce the correct analytical
expression for a next-to-leading order (NLO) kinematic distribution from data,
which is simulated with a NLO event generator. Finally, we derive analytical
approximations for the NLO kinematic distributions after detector simulation,
for which no known analytical formulas currently exist.
