  Stars exhibit a bewildering variety of rapidly variable behaviors ranging
from explosive magnetic flares to stochastically changing accretion to periodic
pulsations or rotation. The principal Rubin Observatory Legacy Survey of Space
and Time (LSST) surveys will have cadences too sparse and irregular to capture
many of these phenomena. We propose here a LSST micro-survey to observe a
single Galactic field, rich in unobscured stars, in a continuous sequence of 30
second exposures for one long winter night in a single photometric band. The
result will be a unique dataset of $\sim 1$ million regularly spaced stellar
light curves (LCs). The LCs will constitute a comprehensive collection of
late-type stellar flaring, but also other classes like short-period binary
systems and cataclysmic variables, young stellar objects and ultra-short period
exoplanets. An unknown variety of anomalous Solar System, Galactic and
extragalactic variables and transients may also be present. A powerful array of
statistical procedures can be applied to individual LCs from the long-standing
fields of time series analysis, signal processing and econometrics. Dozens of
`features' describing the variability can be extracted and the ensemble of
light curves can be subject to advanced machine learning clustering procedures.
This will give a unique, authoritative, objective taxonomy of the rapidly
variable sky derived from identically cadenced LCs. This micro-survey is best
performed early in the Rubin Observatory program, and the results can inform
the wider community on the best approaches to variable star identification and
classification from the sparse, irregular cadences that dominate the planned
surveys.
