diff --git a/rdstar/studies/low_level_cut_study/utilities/plotting.py b/rdstar/studies/low_level_cut_study/utilities/plotting.py
index 9de0860c3235d5e6446082cfdafcb7b8c4704c00..dce5a97fdd685d79dc5eafa1116a638a18c831a3 100644
--- a/rdstar/studies/low_level_cut_study/utilities/plotting.py
+++ b/rdstar/studies/low_level_cut_study/utilities/plotting.py
@@ -8,6 +8,7 @@ import matplotlib.pyplot as plt
 from typing import List, Tuple, Dict
 import pandas as pd
 import copy
+import numpy as np
 import os
 
 from templatefitter.plotter.plot_style import set_matplotlibrc_params, KITColors
@@ -88,28 +89,48 @@ class LowLevelCutPlotter:
                 else:
                     cuts = [new_cut_info.cut_limits]
                 for cut in cuts:
-                    ax.axvline(cut, color=KITColors.kit_red)
+                    ax.axvline(cut, color=KITColors.kit_red, lw=3.)
 
                 df_bkg = df.query("isSignal == 0")
                 bkg_data = df_bkg[cut_var].abs() if new_cut_info.cut_abs else df_bkg[cut_var]
                 df_sig = df.query("isSignal == 1")
                 sig_data = df_sig[cut_var].abs() if new_cut_info.cut_abs else df_sig[cut_var]
+
+                var_range = (min(bkg_data.min(), sig_data.min()), max(bkg_data.max(), sig_data.max()))
+
+                sig_counts, sig_bins = np.histogram(sig_data, bins=50, range=var_range)
+                bkg_counts, bkg_bins = np.histogram(bkg_data, bins=50, range=var_range)
+                factor = sig_counts.max() / ((sig_counts + bkg_counts).max()) / 0.5
+
                 ax.hist(
                     [bkg_data, sig_data],
                     bins=50,
+                    range=var_range,
                     histtype="stepfilled",
                     stacked=True,
                     lw=1.,
-                    color=[KITColors.kit_yellow, KITColors.kit_cyan],
+                    color=[KITColors.kit_cyan, KITColors.kit_orange],
                     label=["Background", "Signal"],
                     edgecolor=KITColors.kit_black
                 )
+                ax.hist(
+                    sig_bins[:-1],
+                    sig_bins,
+                    weights=sig_counts / factor,
+                    range=var_range,
+                    histtype="stepfilled",
+                    lw=1.,
+                    color=KITColors.kit_black,
+                    alpha=0.5,
+                    edgecolor=KITColors.kit_black,
+                    label=f"Signal Shape\nScaled with x{round(1 / factor, 2)}"
+                )
 
                 plt.grid(False)
                 plt.xlabel(cut_info.cut_label)
                 plt.ylabel("Entries")
                 plt.title(combined_channel_dict[channel], loc="right")
-                plt.legend(loc="best")
+                plt.legend(loc=1, frameon=True, fancybox=True)
 
                 save_path = os.path.join(self.target_path, f"{key}_{new_cut_info.cut_name}_evaluation.pdf")
                 output.update({f"{key}_{new_cut_info.cut_name}_evaluation": save_path})