diff --git a/rubin_sim/maf/batches/science_radar_batch.py b/rubin_sim/maf/batches/science_radar_batch.py
index 8ac40867..6865ae40 100644
--- a/rubin_sim/maf/batches/science_radar_batch.py
+++ b/rubin_sim/maf/batches/science_radar_batch.py
@@ -1104,7 +1104,7 @@ def science_radar_batch(
                 summary_metrics=summaryStats,
                 info_label=f"dm {dM} interval {time_interval} RRc Year 1-2",
             )
-            #bundleList.append(bundle)
+            bundleList.append(bundle)
 
     # PulsatingStarRecovery metric (to be added; Marcella)
 
diff --git a/rubin_sim/maf/maf_contrib/periodic_star_modulation_metric.py b/rubin_sim/maf/maf_contrib/periodic_star_modulation_metric.py
index b3c18d6b..af92e052 100644
--- a/rubin_sim/maf/maf_contrib/periodic_star_modulation_metric.py
+++ b/rubin_sim/maf/maf_contrib/periodic_star_modulation_metric.py
@@ -187,20 +187,24 @@ def run(self, data_slice, slice_point=None):
                     noise = np.random.randn(true_lc.size) * dmag
                     # Suppress warnings about failing on covariance
                     fit_obj = PeriodicStar(t_subrun["filter"])
-                    with warnings.catch_warnings():
-                        warnings.simplefilter("ignore")
-                        # If it fails to converge,
-                        # save values that should fail later
-                        try:
-                            parm_vals, pcov = curve_fit(
-                                fit_obj,
-                                t_subrun["time"],
-                                true_lc + noise,
-                                p0=true_params,
-                                sigma=dmag,
-                            )
-                        except RuntimeError:
-                            parm_vals = true_params * 0 + np.inf
+                    # check if we have enough points
+                    if np.size(true_params) >= np.size(fit_obj):
+                        parm_vals = true_params * 0 + np.inf
+                    else:
+                        with warnings.catch_warnings():
+                            warnings.simplefilter("ignore")
+                            # If it fails to converge,
+                            # save values that should fail later
+                            try:
+                                parm_vals, pcov = curve_fit(
+                                    fit_obj,
+                                    t_subrun["time"],
+                                    true_lc + noise,
+                                    p0=true_params,
+                                    sigma=dmag,
+                                )
+                            except RuntimeError:
+                                parm_vals = true_params * 0 + np.inf
                     fits[i, :] = parm_vals
 
                 # Throw out any magnitude fits if there are no