diff --git a/TrkDiag/data/TrkQual_BDT1_v2.0.ubj b/TrkDiag/data/TrkQual_BDT1_v2.0.ubj
new file mode 100644
index 0000000..699871a
Binary files /dev/null and b/TrkDiag/data/TrkQual_BDT1_v2.0.ubj differ
diff --git a/TrkDiag/src/SConscript b/TrkDiag/src/SConscript
index efb348f..7d02d04 100644
--- a/TrkDiag/src/SConscript
+++ b/TrkDiag/src/SConscript
@@ -103,7 +103,8 @@ helper.make_plugins( [
   'TMVA',
   'ROOTTMVASofie',
     'pthread',
-        'onnxruntime'
+        'onnxruntime',
+    'xgboost'
     ] )
 
 helper.make_dict_and_map( [
diff --git a/TrkDiag/src/TrackQuality_module.cc b/TrkDiag/src/TrackQuality_module.cc
index b1cf2eb..6fafdce 100644
--- a/TrkDiag/src/TrackQuality_module.cc
+++ b/TrkDiag/src/TrackQuality_module.cc
@@ -23,6 +23,8 @@
 #include "Offline/RecoDataProducts/inc/MVAResult.hh"
 // ONNXRuntime
 #include "onnxruntime/onnxruntime_cxx_api.h"
+// XGBoost
+#include <xgboost/c_api.h>
 // C++
 #include <iostream>
 #include <fstream>
@@ -47,6 +49,7 @@ namespace mu2e
         fhicl::Atom<art::InputTag> kalSeedPtrTag{Name("KalSeedPtrCollection"), Comment("Input tag for KalSeedPtrCollection")};
         fhicl::Atom<bool> printMVA{Name("PrintMVA"), Comment("Print the MVA used"), false};
         fhicl::Atom<std::string> onnxFilename{Name("onnxFilename"), Comment("Filename for the .onnx file to use")};
+        fhicl::Atom<std::string> xgbFileName{Name("xgbFilename"), Comment("Path to XGBoost .ubj model file")};
         fhicl::Atom<int> debug{Name("debugLevel"), Comment("Debug printout level"), 0};
       };
 
@@ -63,6 +66,7 @@ namespace mu2e
 
     ConfigFileLookupPolicy _configFileLookup;
 
+    // for the ANN
     Ort::Env _env;
     Ort::SessionOptions _session_options;
     Ort::Session _session;
@@ -74,6 +78,11 @@ namespace mu2e
     size_t _total_size;
     Ort::MemoryInfo _memory_info;
     Ort::AllocatedStringPtr _output_name;
+
+    // for the BDT
+    BoosterHandle _booster; // Booster object
+    // Number of features is fixed, must match training!
+    static constexpr size_t _nFeatures = 7;
   };
 
   TrackQuality::TrackQuality(const Parameters& conf) :
@@ -91,7 +100,8 @@ namespace mu2e
     _memory_info(Ort::MemoryInfo::CreateCpu(OrtArenaAllocator, OrtMemTypeDefault)),
     _output_name(_session.GetOutputNameAllocated(0, _allocator))
     {
-      produces<MVAResultCollection>();
+      produces<MVAResultCollection>("ANN");
+      produces<MVAResultCollection>("BDT");
 
       // Handle dynamic dimensions if needed
       for (auto& dim : _input_shape) {
@@ -103,11 +113,29 @@ namespace mu2e
       for (auto dim : _input_shape) {
         _total_size *= dim;
       }
+
+      // Load XGBoost model
+      if (XGBoosterCreate(nullptr, 0, &_booster) != 0) {
+        throw std::runtime_error(std::string("XGBoosterCreate failed: ") + XGBGetLastError());
+      }
+
+      std::string modelPath = ConfigFileLookupPolicy()(conf().xgbFileName());
+      if (XGBoosterLoadModel(_booster, modelPath.c_str()) != 0) {
+        throw std::runtime_error(std::string("XGBoosterLoadModel failed: ") + XGBGetLastError());
+      }
+
+      // verify the loaded model matches the expected feature count
+      bst_ulong nFeaturesModel = 0;
+      if (XGBoosterGetNumFeature(_booster, &nFeaturesModel) != 0) {
+        throw std::runtime_error(std::string("XGBoosterGetNumFeature failed: ") + XGBGetLastError());
+      }
+
     }
 
   void TrackQuality::produce(art::Event& event ) {
     // create output
-    unique_ptr<MVAResultCollection> mvacol(new MVAResultCollection());
+    unique_ptr<MVAResultCollection> anncol(new MVAResultCollection());
+    unique_ptr<MVAResultCollection> bdtcol(new MVAResultCollection());
 
     // get the KalSeedPtrs
     art::Handle<KalSeedPtrCollection> kalSeedPtrHandle;
@@ -186,7 +214,7 @@ namespace mu2e
                                                                 _input_shape.data(),
                                                                 _input_shape.size()
                                                                 );
-      // Run inference
+      // Run ANN inference
       const char* input_names[] = {_input_name.get()};
       const char* output_names[] = {_output_name.get()};
       auto output_tensors = _session.Run(
@@ -198,28 +226,55 @@ namespace mu2e
                                          1
                                          );
       // Get output
-      float* mvaout = output_tensors[0].GetTensorMutableData<float>();
+      float* annout = output_tensors[0].GetTensorMutableData<float>();
 
       if (!entrance_found) {
-        mvaout[0] = 0; // this is not a good track
+        annout[0] = 0; // this is not a good track
       }
 
+      // run XGBoost inference
+      // See reference in preamble comments...
+      float bdt_score = 0.0f;
+      DMatrixHandle dmat;
+      if (XGDMatrixCreateFromMat(features.data(), 1, _nFeatures, NAN, &dmat) != 0) { // use same features vector
+        throw std::runtime_error(std::string("XGDMatrixCreateFromMat failed: ") + XGBGetLastError());
+      }
+
+      bst_ulong out_len = 0;
+      const float* out_result = nullptr;
+      if (XGBoosterPredict(_booster, dmat, 0, 0, 0, &out_len, &out_result) != 0) {
+        XGDMatrixFree(dmat);
+        throw std::runtime_error(std::string("XGBoosterPredict failed: ") + XGBGetLastError());
+      }
+      if (out_len < 1 || out_result == nullptr) {
+        XGDMatrixFree(dmat);
+        throw std::runtime_error("XGBoosterPredict returned no result");
+      }
+
+      bdt_score = out_result[0];
+      if (XGDMatrixFree(dmat) != 0) {
+        throw std::runtime_error(std::string("XGDMatrixFree failed: ") + XGBGetLastError());
+      }
+
+
       if(_debug > 0) {
-        printf("[TrackQuality::%s::%s] Inputs = %.0f, %.4f, %.4f, %.4f, %.4f, %.4f %.4f --> output = %.4f\n",
+        printf("[TrackQuality::%s::%s] Inputs = %.0f, %.4f, %.4f, %.4f, %.4f, %.4f %.4f --> ANN output = %.4fm BDT output = %.4fm\n",
                __func__, moduleDescription().moduleLabel().c_str(),
-               features[0], features[1], features[2], features[3], features[4], features[5], features[6], mvaout[0]);
+               features[0], features[1], features[2], features[3], features[4], features[5], features[6], annout[0], bdt_score);
       }
 
-      mvacol->push_back(MVAResult(mvaout[0]));
+      anncol->push_back(MVAResult(annout[0]));
+      bdtcol->push_back(MVAResult(bdt_score));
     }
 
-    if ( (mvacol->size() != kalSeedPtrs.size()) ) {
-      throw cet::exception("TrackQuality") << "KalSeedPtr and MVAResult sizes are inconsistent (" << kalSeedPtrs.size() << ", " << mvacol->size();
+    if ( (anncol->size() != kalSeedPtrs.size()) ) {
+      throw cet::exception("TrackQuality") << "KalSeedPtr and MVAResult sizes are inconsistent (" << kalSeedPtrs.size() << ", " << anncol->size();
     }
 
 
     // put the output products into the event
-    event.put(move(mvacol));
+    event.put(move(anncol), "ANN");
+    event.put(move(bdtcol), "BDT");
   }
 }// mu2e