  We present results using the AutoClass analysis application available at
NASA/Ames Intelligent Systems Div. (2002) which is a Bayesian, finite mixture
model classification system developed by Cheeseman and Stutz (1996). We apply
this system to Mount Wilson Solar Observatory (MWO) intensity and magnetogram
images and classify individual pixels on the solar surface to calculate daily
indices that are then correlated with total solar irradiance (TSI) to yield a
set of regression coefficients. This approach allows us to model the TSI with a
correlation of better than 0.96 for the period 1996 to 2007. These regression
coefficients applied to classified pixels on the observed solar surface allow
the construction of images of the Sun as it would be seen by TSI measuring
instruments like the Solar Bolometric Imager recently flown by Foukal et al.,
(2004). As a consequence of the very high correlation we achieve in reproducing
the TSI record, our approach holds out the possibility of creating an on-going,
accurate, independent estimate of TSI variations fromground-based observations
which could be used to compare, and identify the sources of disagreement among,
TSI observations from the various satellite instruments and to fill in gaps in
the satellite record. Further, our spatially-resolved images should assist in
characterizing the particular solar surface regions associated with TSI
variations. Also, since the particular set of MWO data on which this analysis
is based is available on a daily basis back to at least 1985, and on an
intermittent basis before then, it will be possible to estimate the TSI
emission due to identified solar surface features at several solar minima to
constrain the role surface magnetic effects have on long-term trends in solar
energy output.
