diff --git a/benchmarks/imaging_shower_ML/options/imaging_ml_data.py b/benchmarks/imaging_shower_ML/options/imaging_ml_data.py
index 8a570becf3d517de2f5debab454badeddd71efd4..6bf9d7c1022972075c272db361a29df7ded24c41 100644
--- a/benchmarks/imaging_shower_ML/options/imaging_ml_data.py
+++ b/benchmarks/imaging_shower_ML/options/imaging_ml_data.py
@@ -6,7 +6,6 @@ from GaudiKernel import SystemOfUnits as units
 from Configurables import ApplicationMgr, EICDataSvc, PodioInput, PodioOutput, GeoSvc
 from GaudiKernel.SystemOfUnits import MeV, GeV, mm, cm, mrad
 
-from Configurables import Jug__Base__InputCopier_dd4pod__Geant4ParticleCollection_dd4pod__Geant4ParticleCollection_ as MCCopier
 from Configurables import Jug__Digi__CalorimeterHitDigi as CalHitDigi
 from Configurables import Jug__Reco__CalorimeterHitReco as CalHitReco
 from Configurables import Jug__Reco__CalorimeterHitsMerger as CalHitsMerger
@@ -38,11 +37,6 @@ podev = EICDataSvc('EventDataSvc', inputs=[f.strip() for f in kwargs['input'].sp
 podin = PodioInput('PodioReader', collections=['mcparticles', 'EcalBarrelHits', 'EcalBarrelScFiHits'])
 podout = PodioOutput('out', filename=kwargs['output'])
 
-copier = MCCopier('MCCopier',
-        OutputLevel=WARNING,
-        inputCollection='mcparticles',
-        outputCollection='mcparticles2')
-
 # Central Barrel Ecal (Imaging Cal.)
 becal_img_daq = dict(
         dynamicRangeADC=3*MeV,
@@ -139,7 +133,7 @@ podout.outputCommands = [
 ]
 
 ApplicationMgr(
-    TopAlg=[podin, copier,
+    TopAlg=[podin,
             becal_img_digi, becal_img_reco, becal_img_merger, becal_img_sorter,
             becal_scfi_digi, becal_scfi_reco, becal_scfi_merger, becal_scfi_sorter,
             becal_combiner,
diff --git a/benchmarks/imaging_shower_ML/scripts/prepare_tf_dataset.py b/benchmarks/imaging_shower_ML/scripts/prepare_tf_dataset.py
index 52b2d5290708e774e6c81793b2f585eb183ed21c..d94d1cb6ad9f177625367a8aa134e8da5d917018 100644
--- a/benchmarks/imaging_shower_ML/scripts/prepare_tf_dataset.py
+++ b/benchmarks/imaging_shower_ML/scripts/prepare_tf_dataset.py
@@ -85,8 +85,8 @@ if __name__ == '__main__':
     df.loc[:, 'rc'] = rc
     df.loc[:, 'eta'] = eta
 
-    dfm = flatten_collection(rdf, 'mcparticles2', ['genStatus', 'pdgID', 'ps.x', 'ps.y', 'ps.z', 'mass'])
-    dfm.rename(columns={c: c.replace('mcparticles2.', '') for c in dfm.columns}, inplace=True)
+    dfm = flatten_collection(rdf, 'mcparticles', ['genStatus', 'pdgID', 'ps.x', 'ps.y', 'ps.z', 'mass'])
+    dfm.rename(columns={c: c.replace('mcparticles.', '') for c in dfm.columns}, inplace=True)
     # selete incident particles
     dfm = dfm[dfm['genStatus'].isin([0, 1])]
     # NOTE: assumed single particles