@@ -15,9 +15,8 @@
 from .cython_impl import lombscargle_cython
 from .fast_impl import lombscargle_fast
 from .fastchi2_impl import lombscargle_fastchi2
-from .scipy_impl import lombscargle_scipy
+from .scipy_impl import _scipy_supports_floating_mean, lombscargle_scipy
 from .slow_impl import lombscargle_slow
-from .utils import SCIPY_LT_1_15
 
 METHODS = {
     "slow": lombscargle_slow,
@@ -93,7 +92,7 @@ def validate_method(  # ruff: ignore[PLR0917]
         "scipy" in methods
         and dy is None
         and method_kwds is None
-        and not (fit_mean and SCIPY_LT_1_15)
+        and (not fit_mean or _scipy_supports_floating_mean())
     )
 
     # automatically choose the appropriate method
 
@@ -1,12 +1,24 @@
+from functools import cache
+
 import numpy as np
 
 from astropy.timeseries.periodograms.lombscargle.utils import (
     compute_chi2_ref,
     convert_normalization,
 )
+from astropy.utils import minversion
 from astropy.utils.compat.optional_deps import HAS_SCIPY
 
-from .utils import SCIPY_LT_1_15
+
+@cache
+def _scipy_supports_floating_mean():
+    # Deferred so that scipy is only imported and its version only probed when
+    # the answer is needed; passing the module object lets minversion fall
+    # back to scipy.__version__ when the distribution metadata is missing
+    # (e.g. in some frozen applications).
+    import scipy
+
+    return minversion(scipy, "1.15.0.dev0")
 
 
 def lombscargle_scipy(
@@ -54,6 +66,9 @@ def lombscargle_scipy(
 
     from scipy import signal
 
+    if fit_mean and not _scipy_supports_floating_mean():
+        raise ValueError("fit_mean=True requires scipy 1.15 or later")
+
     t, y = np.broadcast_arrays(t, y)
 
     # Scipy requires floating-point input
@@ -69,9 +84,6 @@ def lombscargle_scipy(
     if center_data:
         y = y - y.mean()
 
-    if fit_mean and SCIPY_LT_1_15:
-        raise ValueError("fit_mean=True requires scipy 1.15 or later")
-
     kwargs = {"floating_mean": True} if fit_mean else {}
 
     # Note: scipy `freqs` input is in angular frequencies
 
@@ -1,17 +1,6 @@
-from importlib.metadata import PackageNotFoundError, version
 from math import factorial
 
 import numpy as np
-from packaging.version import Version
-
-# The distribution metadata gives the scipy version without the cost of
-# importing scipy itself; if scipy is installed without metadata (e.g. in
-# some frozen applications) assume it is recent, and if it is not installed
-# at all this value is never consulted since HAS_SCIPY gates all uses.
-try:
-    SCIPY_LT_1_15 = Version(version("scipy")) < Version("1.15.0.dev0")
-except PackageNotFoundError:
-    SCIPY_LT_1_15 = False
 
 
 def bitceil(N):
 
@@ -9,8 +9,8 @@
 from astropy.timeseries.periodograms.lombscargle._testing import (
     assert_not_strictly_equal,
 )
-from astropy.timeseries.periodograms.lombscargle.implementations.utils import (
-    SCIPY_LT_1_15,
+from astropy.timeseries.periodograms.lombscargle.implementations.scipy_impl import (
+    _scipy_supports_floating_mean,
 )
 
 ALL_METHODS = LombScargle.available_methods
@@ -74,7 +74,7 @@ def test_all_methods(
     data, method, center_data, fit_mean, errors, with_units, normalization
 ):
     if method == "scipy":
-        if fit_mean and SCIPY_LT_1_15:
+        if fit_mean and not _scipy_supports_floating_mean():
             pytest.skip("SciPy 1.15+ required for using `fit_mean=True`")
         if errors == "full" or (errors == "partial" and normalization == "psd"):
             pytest.skip("scipy method only supports uniform uncertainties dy")