diff --git a/pes_to_spec/__init__.py b/pes_to_spec/__init__.py
index fb5f5ab974826aa2235de9d816c3a3dff54b0173..c0fb2a7c5e87bb2539127a0fc845a72955a2e588 100644
--- a/pes_to_spec/__init__.py
+++ b/pes_to_spec/__init__.py
@@ -2,4 +2,4 @@
 Estimate high-resolution photon spectrometer data from low-resolution non-invasive measurements.
 """
 
-VERSION = "0.1.4"
+VERSION = "0.1.5"
diff --git a/pes_to_spec/model.py b/pes_to_spec/model.py
index 2276bacf4d57217a94bf07258633a04fecbd6fb2..13e72acedb88217132dd94226d92ed5b64198eee 100644
--- a/pes_to_spec/model.py
+++ b/pes_to_spec/model.py
@@ -3,7 +3,7 @@ from __future__ import annotations
 import joblib
 
 import numpy as np
-from scipy.signal import fftconvolve
+from scipy.signal import convolve
 #from scipy.signal import find_peaks_cwt
 from scipy.optimize import fmin_l_bfgs_b
 from sklearn.decomposition import PCA
@@ -331,7 +331,7 @@ class HighResolutionSmoother(TransformerMixin, BaseEstimator):
         gaussian = np.exp(-0.5*(energy - mu)**2/self.high_res_sigma**2)
         gaussian /= np.sum(gaussian, axis=1, keepdims=True)
         # apply it to the data
-        high_res_gc = fftconvolve(X, gaussian, mode="same", axes=1)
+        high_res_gc = convolve(X, gaussian, mode="same", axes=1)
         return high_res_gc
 
     def inverse_transform(self, Xt: np.ndarray) -> np.ndarray:
@@ -519,7 +519,7 @@ class SelectRelevantLowResolution(TransformerMixin, BaseEstimator):
         gaussian = np.exp(-0.5*(axis - sum_low_res.shape[0]//2)**2/20**2)
         gaussian /= np.sum(gaussian, axis=0, keepdims=True)
         # apply it to the data
-        smoothened = fftconvolve(sum_low_res, gaussian, mode="same", axes=0)
+        smoothened = convolve(sum_low_res, gaussian, mode="same", axes=0)
         peak_idx = [np.argmax(smoothened)]
         if len(peak_idx) < 1:
             raise PromptNotFoundError()
@@ -1009,7 +1009,7 @@ class Model(TransformerMixin, BaseEstimator):
         M = self.wiener_filter.shape[0]
         B = expected.shape[0]
         assert expected.shape[1] == M
-        deconvolved = fftconvolve(expected,
+        deconvolved = convolve(expected,
                                   np.broadcast_to(self.wiener_filter.reshape(1, -1), (B, M)),
                                   mode="same",
                                   axes=1)