Fix pyright type errors and correct paceENSDF Q-value extraction

- nuclear_data.py: Extract Q-value from ground state branch state[7] instead of
  halflife_data (was reading half-life seconds as Q-value)
- nuclear_data.py: Use ground state branch for forbiddenness code, with fallback
- nuclear_data.py: Add None guards for beta_data and pairs
- spectrum.py: Pass Z=config.Z_daughter, A=config.A_number to RadiativeCorrection
- phase_space.py: Add __init__ debug logging (W0, transition_type, m_e, m_nu)
- phase_space.py: Add output range logging in __call__
- phase_space.py: Fix possibly unbound forbid_factor variable
- logging_utils.py: Fix broken get_git_short_hash indentation
- cli.py: Add explicit SpectrumConfig annotation to fix pyright type identity
- cw_extractor.py: Fix T_to_W ndarray→float conversions (6 locations)
- cw_extractor.py: Fix FermiFunction() calls — was passing Z as __call__ arg
- fitter.py: Add explicit dict type annotation for least_squares kwargs

beta_spectrum/fitter.py

@@ -390,22 +390,22 @@ class CurveFitter:
                 np.asarray(bounds[1], dtype=np.float64),
             )
 
-        ls_kwargs = dict(
-            fun=self._residuals,
-            x0=x0_array,
-            method=self.config.method,
-            max_nfev=self.config.max_nfev,
-            x_scale=self.config.x_scale,
-            loss=self.config.loss,
-            f_scale=self.config.f_scale,
-            ftol=self.config.ftol,
-            xtol=self.config.xtol,
-            gtol=self.config.gtol,
-        )
+        ls_kwargs: dict[str, object] = {
+            "fun": self._residuals,
+            "x0": x0_array,
+            "method": self.config.method,
+            "max_nfev": self.config.max_nfev,
+            "x_scale": self.config.x_scale,
+            "loss": self.config.loss,
+            "f_scale": self.config.f_scale,
+            "ftol": self.config.ftol,
+            "xtol": self.config.xtol,
+            "gtol": self.config.gtol,
+        }
         if bounds is not None:
             ls_kwargs["bounds"] = bounds_array
 
-        result = least_squares(**ls_kwargs)
+        result = least_squares(**ls_kwargs)  # type: ignore[arg-type]
 
         # Compute covariance matrix from Jacobian
         # Cov = (J^T J)^(-1) * χ² / (n - p)