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_v3.py

@@ -405,6 +405,7 @@ def generate_single_sample(args: Tuple[int, dict]) -> Optional[str]:
             include_radon=bg_params['include_radon'],
             include_thorium=bg_params['include_thorium'],
             detector_name=config['detector_name'],
+            measured_background_path=config.get('measured_background_path'),
         )
         
         # Generate spectrum
@@ -437,6 +438,7 @@ def generate_training_data_v3(
     scenarios: Optional[List[SampleScenario]] = None,
     num_workers: int = None,
     random_seed: int = None,
+    measured_background_path: Optional[str] = None,
 ) -> int:
     """
     Generate training samples in parallel.
@@ -498,6 +500,7 @@ def generate_training_data_v3(
         'bg_intensity_max': bg_intensity_range[1],
         'base_seed': random_seed,
         'scenarios': scenarios,
+        'measured_background_path': measured_background_path,
     }
     
     # Create work items
@@ -560,9 +563,11 @@ def main():
                         help='Minimum activity in Bq')
     parser.add_argument('--activity_max', type=float, default=100.0,
                         help='Maximum activity in Bq')
-    
+    parser.add_argument('--measured_background', type=str, default=None,
+                        help='Path to measured background .npy file for hybrid training')
+
     args = parser.parse_args()
-    
+
     generate_training_data_v3(
         num_samples=args.num_samples,
         output_dir=Path(args.output_dir),
@@ -570,6 +575,7 @@ def main():
         activity_range=(args.activity_min, args.activity_max),
         num_workers=args.workers,
         random_seed=args.seed,
+        measured_background_path=args.measured_background,
     )