diff --git a/zeus21/UVLFs.py b/zeus21/UVLFs.py
index b654c53..975d32a 100644
--- a/zeus21/UVLFs.py
+++ b/zeus21/UVLFs.py
@@ -8,8 +8,8 @@
 Edited by Hector Afonso G. Cruz
 JHU - July 2024
 
-Bug fix by Emily Bregou
-UT Austin - June 2025
+Edited by Emily Bregou
+UT Austin - February 2026
 """
 
 from . import cosmology
@@ -35,7 +35,7 @@ def MUV_of_SFR(SFRtab, kappaUV):
 
 
 #and combine to get UVLF:
-def UVLF_binned(Astro_Parameters,Cosmo_Parameters,HMF_interpolator, zcenter, zwidth, MUVcenters, MUVwidths, DUST_FLAG=True, RETURNBIAS = False):
+def UVLF_binned(Astro_Parameters,Cosmo_Parameters,HMF_interpolator, zcenter, zwidth, MUVcenters, MUVwidths, minMUV = None, DUST_FLAG=True, RETURNBIAS = False, RETURNWEIGHTS = False):
     'Binned UVLF in units of 1/Mpc^3/mag, for bins at <zcenter> with a Gaussian width zwidth, centered at MUV centers with tophat width MUVwidths. z width only in HMF since that varies the most rapidly. If flag RETURNBIAS set to true it returns number-avgd bias instead of UVLF, still have to divide by UVLF'
     
     if(constants.NZ_TOINT>1):
@@ -57,7 +57,7 @@ def UVLF_binned(Astro_Parameters,Cosmo_Parameters,HMF_interpolator, zcenter, zwi
     MUVbarlist = np.fmin(MUVbarlist,constants._MAGMAX)
     
 
-    if(RETURNBIAS==True): # weight by bias
+    if(RETURNBIAS==True): # weight by bias) 
         biasM = np.array([bias_Tinker(Cosmo_Parameters, HMF_interpolator.sigma_int(HMF_interpolator.Mhtab,zcenter+dz*zwidth)) for dz in DZ_TOINT])
     else: # do not weight by bias
         biasM = np.ones_like(WEIGHTS_TOINT)
@@ -80,8 +80,22 @@ def UVLF_binned(Astro_Parameters,Cosmo_Parameters,HMF_interpolator, zcenter, zwi
     
     xhi = np.subtract.outer(MUVcuthi, currMUV)/(np.sqrt(2) * sigmaUV)
     xlo = np.subtract.outer(MUVcutlo, currMUV )/(np.sqrt(2) * sigmaUV)
-    weights = (erf(xhi) - erf(xlo)).T/(2.0 * MUVwidths)
-    
+
+    # Cut distributions based on minMUV (user-input, halo mass dependent)
+    #MUVmin can be set to be MUV_of_SFR(max_SFR, Astro_Parameters._kappaUV), with max_SFR = Mstar/min_t & Mstar = fb*Mh
+    if minMUV is None:
+        minMUV = np.full_like(HMF_interpolator.Mhtab, -100)
+    x_min = (minMUV - currMUV)/(np.sqrt(2) * sigmaUV)
+    xhi_cut = np.fmax(xhi, x_min)
+    xlo_cut = np.fmax(xlo, x_min)
+
+    weights_unnormalized = (erf(xhi_cut) - erf(xlo_cut)).T/(2.0 * MUVwidths)
+    weights = weights_unnormalized/ (0.5*(1-erf(x_min)))[:,None] # Renormalize distributions based on the portion cut off by minMUV
+
+    if RETURNWEIGHTS:
+        return weights
+
+
     UVLF_filtered = np.trapz(weights.T * HMFcurr, HMF_interpolator.Mhtab, axis=-1)
 
     if(Astro_Parameters.USE_POPIII==False):