diff --git a/pax/datastructure.py b/pax/datastructure.py
index bbcaa7fa..ddac59b4 100644
--- a/pax/datastructure.py
+++ b/pax/datastructure.py
@@ -199,6 +199,9 @@ class Peak(StrictModel):
     #: Total areas of all hits per PMT (pe).
     area_per_channel = np.array([], dtype='float64')
 
+    # contribution to tight coincidence, 1 or 0 for each channel
+    coincidence_per_channel = np.array([], dtype=np.int16)
+
     #: Total area of all hits across all PMTs (pes).
     #: In XerawdpImitation mode, rightmost sample is not included in area integral.
     area = 0.0
diff --git a/pax/plugins/io/ROOTClass.py b/pax/plugins/io/ROOTClass.py
index f64ef8ca..90fa1703 100644
--- a/pax/plugins/io/ROOTClass.py
+++ b/pax/plugins/io/ROOTClass.py
@@ -191,6 +191,7 @@ def _build_model_class(self, model):
                             for _fn, length in ((
                                 ('area_per_channel', n_channels),
                                 ('hits_per_channel', n_channels),
+                                ('coincidence_per_channel', n_channels),
                                 ('n_saturated_per_channel', n_channels),
                                 ('sum_waveform', n_waveform_samples),
                                 ('sum_waveform_top', n_waveform_samples),
diff --git a/pax/plugins/peak_processing/BasicProperties.py b/pax/plugins/peak_processing/BasicProperties.py
index 5da4ffa1..3403386d 100644
--- a/pax/plugins/peak_processing/BasicProperties.py
+++ b/pax/plugins/peak_processing/BasicProperties.py
@@ -119,16 +119,23 @@ def transform_event(self, event):
 
             # Compute a tight coincidence count (useful for distinguishing S1s from junk)
             x = peak.hits['index_of_maximum']
-            l = peak.index_of_maximum - self.tight_coincidence_samples
-            r = peak.index_of_maximum + self.tight_coincidence_samples
-            peak.tight_coincidence = len(np.unique(peak.hits['channel'][(x >= l) & (x <= r)]))
+            left = peak.index_of_maximum - self.tight_coincidence_samples
+            right = peak.index_of_maximum + self.tight_coincidence_samples
+            peak.tight_coincidence = len(np.unique(peak.hits['channel'][(x >= left) & (x <= right)]))
+
+            # list of pmts that contribute to tight coincidence of the peak
+            peak.coincidence_per_channel = np.zeros_like(peak.hits_per_channel)
+            for hitt in peak.hits:
+                if hitt['index_of_maximum'] >= left and hitt['index_of_maximum'] <= right:
+                    pmt_index = hitt['channel']
+                    peak.coincidence_per_channel[pmt_index] = 1
 
             # varying tight coincidence intervals
             peak.tight_coincidence_thresholds = np.zeros(5, dtype=np.int16)
             for ip, window in enumerate([5, 4, 3, 2, 1]):
-                l = peak.index_of_maximum - window
-                r = peak.index_of_maximum + window
-                peak.tight_coincidence_thresholds[ip] = len(np.unique(peak.hits['channel'][(x >= l) & (x <= r)]))
+                left = peak.index_of_maximum - window
+                right = peak.index_of_maximum + window
+                peak.tight_coincidence_thresholds[ip] = len(np.unique(peak.hits['channel'][(x >= left) & (x <= right)]))
 
             # Store the waveform; for tpc also store the top waveform
             put_w_in_center_of_field(w, peak.sum_waveform, cog_idx)