Refinement: the likelihood's D and beta fitted bin by bin on the refinement's threads

EstimateLikelihoodParameters fits each resolution bin on its own and writes only that bin's values, so
the bins run under ParallelFor; the result is the same, bit for bit. It was the largest single stage of
a refinement (107 calls, one core): 5reo 2.42 -> 0.56 s, 8dyz 2.46 -> 0.57 s, 9yl4 3.54 -> 0.88 s;
whole refinement 5reo 6.3 -> 4.8 s, 8dyz 6.9 -> 5.3 s (RTX 5080, -N 8, loaded box).

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_015CeEGgpKdfkCTJb3YNMJcu
(cherry picked from commit 85aa34d3031c8e36be4462fca544bc801af45d5a)
(cherry picked from commit d84ba55252adf455d5b4ba62052dfbafa6290a64)

image_analysis/structure_refinement/RefinementXray.cpp

@@ -229,7 +229,7 @@ RefinementReflections PrepareRefinementReflections(const std::vector<MergedRefle
 }
 
 void EstimateLikelihoodParameters(const RefinementReflections &refl, const std::vector<std::complex<float>> &fmodel,
-                                  XrayRowModel &rows) {
+                                  XrayRowModel &rows, size_t nthreads) {
     const size_t n = refl.Size();
     std::vector<double> fc(n);
     for (size_t i = 0; i < n; i++)
@@ -248,7 +248,8 @@ void EstimateLikelihoodParameters(const RefinementReflections &refl, const std::
     const size_t bins = std::clamp<size_t>(free.size() / FREE_PER_BIN, 1, MAX_BINS);
 
     std::vector<double> centre(bins), bin_d(bins), bin_beta(bins);
-    for (size_t b = 0; b < bins; b++) {
+    // each bin is fitted on its own
+    ParallelFor(static_cast<int>(bins), nthreads, [&](int b) {
         const std::vector<size_t> idx(free.begin() + b * free.size() / bins, free.begin() + (b + 1) * free.size() / bins);
         double s2 = 0, sfo = 0, sfc2 = 0, sfofc = 0;
         for (size_t i : idx) {
@@ -271,7 +272,7 @@ void EstimateLikelihoodParameters(const RefinementReflections &refl, const std::
         }
         bin_d[b] = d;
         bin_beta[b] = beta;
-    }
+    });
     rows.d.resize(n);
     rows.var.resize(n);
     for (size_t i = 0; i < n; i++) {