Background réaliste CsI(Tl) + hybridation mesuré/synthétique + dashboard continuum

- Remplace le continuum exponentiel par un modèle réaliste CsI(Tl) dans
  l'entraînement (bosse asymétrique ~110 keV + queue Compton)
- Ajoute l'injection de background mesuré (70% mesuré / 30% synthétique)
  via --measured_background et MEASURED_BACKGROUND_PATH
- Ajoute l'endpoint /api/background/continuum et le toggle "Continuum CsI"
  sur le dashboard background
- Exclut le canal 1023 (overflow bin) de l'affichage web (NUM_CHANNELS=1023)
- Corrige le lissage Gaussien du background (normalisation locale aux bords)
- Met à jour README.md, CLAUDE.md, TUTORIEL.md, TOTO.md, vega_ml/README.md

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

train/vega_ml/synthetic_spectra/generate_spectra.py

@@ -136,6 +136,7 @@ def generate_training_batch(
     background_only_fraction: float = 0.1,
     save_png: bool = False,
     random_seed: int = None,
+    measured_background_path: str = None,
 ) -> list:
     """
     Generate a batch of training samples with various configurations.
@@ -210,6 +211,7 @@ def generate_training_batch(
             duration,
             detector_name=detector_name,
             include_background=True,
+            measured_background_path=measured_background_path,
         )
 
         # Save spectrum (don't accumulate in memory)
@@ -240,6 +242,7 @@ def generate_training_batch(
             duration,
             detector_name=detector_name,
             include_background=True,
+            measured_background_path=measured_background_path,
         )
 
         save_spectrum(
@@ -270,6 +273,7 @@ def generate_training_batch(
             duration,
             detector_name=detector_name,
             include_background=True,
+            measured_background_path=measured_background_path,
         )
 
         save_spectrum(
@@ -295,6 +299,7 @@ def generate_training_batch(
             sources=[],  # No additional sources
             include_background=True,
             detector_name=detector_name,
+            measured_background_path=measured_background_path,
         )
 
         spectrum = generator.generate_spectrum(config)
@@ -367,6 +372,13 @@ def main():
         default=100.0,
         help="Maximum source activity in Bq (default: 100.0)"
     )
+
+    parser.add_argument(
+        "--measured_background",
+        type=str,
+        default=None,
+        help="Path to measured background .npy file for hybrid training"
+    )
     
     parser.add_argument(
         "--save_png",
@@ -402,6 +414,7 @@ def main():
         activity_range=(args.min_activity, args.max_activity),
         save_png=args.save_png,
         random_seed=args.seed,
+        measured_background_path=args.measured_background,
     )
 
     print("\n" + "=" * 60)