  The range to which the Laser Interferometer Gravitational-Wave Observatory
(LIGO) can observe astrophysical systems varies over time, limited by noise in
the instruments and their environments. Identifying and removing the sources of
noise that limit LIGO's range enables higher signal-to-noise observations and
increases the number of observations. The LIGO observatories are continuously
monitored by hundreds of thousands of auxiliary channels that may contain
information about these noise sources. This paper describes an algorithm that
uses linear regression, namely lasso (least absolute shrinkage and selection
operator) regression, to analyze all of these channels and identify a small
subset of them that can be used to reconstruct variations in LIGO's
astrophysical range. Exemplary results of the application of this method to
three different periods of LIGO Livingston data are presented, along with
computational performance and current limitations.
