diff --git a/CHANGES.md b/CHANGES.md
index f6cdcc0..661003a 100644
--- a/CHANGES.md
+++ b/CHANGES.md
@@ -1,4 +1,7 @@
 # Changelog
 
-## Upcoming
+## v1.0.1 (upcoming)
+Minor optimizations that make use of finufft v2.3.
+
+## v1.0.0 (2024-05-28)
 Initial release, with support for CPU, GPU, and batched periodograms
diff --git a/pyproject.toml b/pyproject.toml
index a1f418e..4984775 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -12,7 +12,7 @@ authors = [{ name = "Lehman Garrison" },
 requires-python = ">=3.8"
 license = { file = "LICENSE" }
 urls = { Homepage = "https://github.com/flatironinstitute.org/nifty-ls" }
-dependencies = ["numpy", "finufft >= 2.2"]
+dependencies = ["numpy", "finufft >= 2.3"]
 dynamic = ["version"]
 
 [project.optional-dependencies]
diff --git a/src/nifty_ls/cpu_helpers.cpp b/src/nifty_ls/cpu_helpers.cpp
index 5642ba4..e51fbd3 100644
--- a/src/nifty_ls/cpu_helpers.cpp
+++ b/src/nifty_ls/cpu_helpers.cpp
@@ -174,12 +174,6 @@ void process_finufft_inputs(
             }
             w2(i, j) *= std::exp(phase_shift * t2(j));
         }
-
-        // TODO: if fmin/df is an integer, we may be able to do
-        //   t = ((df * t) % 1) * 2 * np.pi
-        // Could help a lot with range-reduction performance.
-        t1(j) = std::fmod(t1(j), TWO_PI);
-        t2(j) = std::fmod(t2(j), TWO_PI);
     }
 }
 
diff --git a/src/nifty_ls/cufinufft.py b/src/nifty_ls/cufinufft.py
index 9f736bc..810ad9e 100644
--- a/src/nifty_ls/cufinufft.py
+++ b/src/nifty_ls/cufinufft.py
@@ -158,9 +158,6 @@ def lombscargle(
     phase_shift1 = cp.exp(1j * (Nshift + fmin / df) * t1)
     phase_shift2 = cp.exp(1j * (Nshift + fmin / df) * t2)
 
-    t1 %= 2 * cp.pi
-    t2 %= 2 * cp.pi
-
     yw[:] = y * w2.real
     if fit_mean:
         # Up to now, w and w2 are identical
diff --git a/src/nifty_ls/finufft.py b/src/nifty_ls/finufft.py
index 4d22230..5692394 100644
--- a/src/nifty_ls/finufft.py
+++ b/src/nifty_ls/finufft.py
@@ -195,9 +195,6 @@ def lombscargle(
         phase_shift1 = np.exp(1j * (Nshift + fmin / df) * t1)
         phase_shift2 = np.exp(1j * (Nshift + fmin / df) * t2)
 
-        t1 %= 2 * np.pi
-        t2 %= 2 * np.pi
-
         yw[:] = y * w2.real
         if fit_mean:
             # Up to now, w and w2 are identical