Index: trunk/doc/release.2015/ps1.calibration/calibration.tex
===================================================================
--- trunk/doc/release.2015/ps1.calibration/calibration.tex	(revision 41186)
+++ trunk/doc/release.2015/ps1.calibration/calibration.tex	(revision 41188)
@@ -1367,7 +1367,7 @@
 fluxes.
 
-The first challenge is to select which measurements to use in
-the calculation of the average photometry.  For the $3\pi$ Survey
-data, a single object may have anywhere from zero to roughly twenty
+The first challenge is to select which measurements to use in the
+calculation of the average photometry.  For the $3\pi$ Survey data, a
+single object may have anywhere from zero to roughly twenty
 measurements in a given filter.  Not all measurements are of equal
 value, but we need a process which assigns an average photometry value
@@ -1377,14 +1377,16 @@
 measurements available in each filter for each object.  Once the set
 of measurements to be used in the analysis is determined, we use the
-Iteratively Reweighted Least Squares (IRLS) technique to determine the
-average photometry given the possible presence of non-Gaussian
-outliers even within the best subset of measurements.  
-
-\note{include a reference to IRLS and describe concept more}
-\code{http://users.stat.umn.edu/~sandy/courses/8053/handouts/robust.pdf}
-\code{https://arxiv.org/pdf/0807.0575.pdf}
-\code{https://www.redalyc.org/pdf/3939/393933924009.pdf}
-\code{Street, J. O., Carrol, R. J., \& Ruppert D. 1988, Am. Stat, 42, 152}
-\code{Green, P. J., 1984, J. R. Statist. Soc B, 42, 149}
+Iteratively Reweighted Least Squares (IRLS) technique \citep[see,
+  e.g.,][]{Green.1984} to determine the average photometry given the
+possible presence of non-Gaussian outliers even within the best subset
+of measurements.
+
+%% \note{include a reference to IRLS and describe concept more}
+%% \code{http://users.stat.umn.edu/~sandy/courses/8053/handouts/robust.pdf}
+%% \code{https://arxiv.org/pdf/0807.0575.pdf}
+%% \code{https://www.redalyc.org/pdf/3939/393933924009.pdf}
+%% \code{Street, J. O., Carrol, R. J., \& Ruppert D. 1988, Am. Stat, 42, 152}
+%% \code{Green, P. J., 1984, J. R. Statist. Soc B, 42, 149}
+% https://www.researchgate.net/publication/256800227_Robust_estimation_of_excitation_in_mechanical_systems_under_model_uncertainties
 
 \subsubsection{Selection of Measurements}
@@ -1498,10 +1500,11 @@
 Pan-STARRS\,1 detections have a relatively high rate of non-Gaussian
 outliers, partly because of the wide range of instrumental features
-affecting the data (see Paper III).  We have used a
-technique called Iteratively Reweighted Least Squares (IRLS) fitting
-to reduce the sensitivity of the fits to outlier measurements.  We
-have also used bootstrap resampling to determine confidence limits on
-our fits given the observed collection of photometry measurements.  In
-this case, the analysis is fitting the trivial model that the
+affecting the data (see Paper III).  \textmod{We have used Iteratively
+  Reweighted Least Squares (IRLS) fitting to reduce the sensitivity of
+  the fits to outlier measurements.}  
+
+We have also used bootstrap resampling to determine confidence limits
+on our fits given the observed collection of photometry measurements.
+In this case, the analysis is fitting the trivial model that the
 photometry measurements are derived from a population with an
 underlying constant value.  The discussion below applies to both the
@@ -1509,10 +1512,16 @@
 photometry fluxes.  This technique is used to calculate the average
 magnitudes for all three types of photometry stored in the DVO
-database: PSF, Kron, and seeing-matched total aperture photometry.  
-
-The IRLS analysis starts with an ordinary least squares fit, using the
-weights for each measurement as determined from Poisson statistics.
-Since our model is a constant flux, this step is equivalent to
-calculating a simple weighted average.  
+database: PSF, Kron, and seeing-matched total aperture photometry.
+
+\textadd{Iteratively-reweighted least-squares fitting describes a
+  class of parameter estimation techniques in which weights are
+  modified compared to that derived from the standard error in order
+  to improve the speed of convergence or the robustness to deviant
+  measurements.  Broad reviews of these techniques can be found in
+  \cite{Green.1984} and \cite{Street.1988}}.  \textmod{In our
+  implementation, the IRLS analysis} starts with an ordinary least
+squares fit, using the weights for each measurement as determined from
+Poisson statistics.  Since our model is a constant flux, this step is
+equivalent to calculating a simple weighted average.
 
 Next, the deviations from the average value for each photometry
@@ -2980,9 +2989,10 @@
 To further improve the astrometric calibration reliability in this
 region, we have generated a new reference catalog combining the PS1
-PV3 photometry with astrometry from Gaia DR2 \citep{2018AA...616A...1G}.  We are reprocessing all
-images from the region North of $+70\mathdegree$ and will provide a
-complete Polar Region release using the same data as used for DR2.
-This updated release is expected to be available from MAST near the
-end of summer 2019.
+PV3 photometry with astrometry from Gaia DR2
+\citep{2018AA...616A...1G}.  We are reprocessing all images from the
+region North of $+70\mathdegree$ and will provide a complete Polar
+Region release using the same data as used for DR2.  This updated
+release is expected to be available from MAST near the end of summer
+2019.
 
 We consider skycells with more than 10\% bad groups to have been