update a comment in chi2

1 files changed, 14 insertions, 31 deletions

diff --git a/utils/chi2 b/utils/chi2
index b3875d1..7a8342d 100755
--- a/utils/chi2
+++ b/utils/chi2
@@ -224,40 +224,23 @@ def make_nll(data, muons, mc, bins, print_nll=False):
         if any(x[i] < 0 for i in range(len(x))):
             return np.inf
 
+        # Get the data histograms. We need to do this within the
+        # likelihood function since we apply the energy bias corrections to the
+        # data.
+        #
+        # Note: Applying the energy bias correction to the data has a few
+        # issues. If the energy scale parameter changes by even a very small
+        # amount it can cause events to cross a bin boundary you will see a
+        # discrete jump in the likelihood. This isn't good for minimizers, and
+        # so when doing the first minimization with nlopt, I fix these
+        # parameters. Later, when we run the MCMC, these paramters are then
+        # free to float and the small discontinuities in the likelihood
+        # shouldn't be a problem.
         data_hists = get_data_hists_fast(ke_dict_data,x,bins)
 
         # Get the Monte Carlo histograms. We need to do this within the
-        # likelihood function since several of the parameters like the energy
-        # bias and resolution affect the histograms.
-        #
-        # Note: I really should be applying the bias term to the data instead
-        # of the Monte Carlo. The reason being that the reconstruction was all
-        # tuned based on the data from the Monte Carlo. For example, the single
-        # PE charge distribution is taken from the Monte Carlo. Therefore, if
-        # there is some difference in bias between data and Monte Carlo, we
-        # should apply it to the data. However, the reason I apply it to the
-        # Monte Carlo instead is because applying an energy bias correction to
-        # an analysis in which we're using histograms is not really a good
-        # idea. If the energy scale parameter changes by a very small amount
-        # and causes a single event to cross a bin boundary you will see a
-        # discrete jump in the likelihood. This isn't good for minimizers
-        # (although it may be ok with the MCMC depending on the size of the
-        # jump). To reduce this issue, I apply the energy bias correction to
-        # the Monte Carlo, and since there are almost 100 times more events in
-        # the Monte Carlo than in data, the issue is much smaller.
-        #
-        # All that being said, this doesn't completely get rid of the issue,
-        # but I do two things that should make it OK:
-        #
-        # 1. I use the SBPLX minimizer instead of COBYLA which should have a
-        #    better chance of jumping over small discontinuities.
-        #
-        # 2. I ultimately run an MCMC and so if we get stuck somewhere close to
-        #    the minimum, the MCMC results should correctly deal with any small
-        #    discontinuities.
-        #
-        # Also, it's critical that I first adjust the data energy by whatever
-        # amount I find with the stopping muons and Michel distributions.
+        # likelihood function since we apply the energy resolution parameters
+        # to the Monte Carlo.
         mc_hists = get_mc_hists_fast(ke_dict,x,bins,apply_norm=True)
         muon_hists = get_mc_hists_fast(ke_dict_muon,x,bins,apply_norm=False)