Fix: CsI(Tl) non-linear response correction + detector calibration overhaul

Root cause of Am-241 misidentification: the Radiacode 103's CsI(Tl) crystal
shifts low-energy peaks upward (59.5 keV → 71.6 keV for Am-241) due to
non-proportional scintillation response. The model was trained on theoretical
peak positions and couldn't match the shifted real peaks.

Changes:
- Add inverse CsI(Tl) non-linear correction to inference pipeline
  (radiacode_monitor.py, web/config.py, test_detection.py)
  E_apparent = E_true * (1 + 0.37 * exp(-E_true/100))
  Corrects channel mapping so peaks appear at theoretical energies
- Fix energy calibration: DetectorConfig now uses E = 0.33 + 2.97*ch
  with 1023 channels, matching the real detector (was energy_min=20,
  skip_first_channel=True, different channel width)
- Add K-escape peaks for CsI(Tl) iodine X-ray escape (E - 28.5 keV)
- Add asymmetric peak shapes for low-energy tails (< 200 keV)
- Add log1p normalization in dataset and inference (replaces max-norm)
- Add background-subtracted training mode (subtract_background flag)
- Add low-signal augmentation (0.01-5 Bq activities, 30-300s durations)
- Update docker-compose.yml: batch_size=32, duration=30-300s,
  CSI_NONLINEAR_ALPHA/BETA env vars for detect and web
- Web dashboard: apply CsI correction to displayed spectra
- Various UI fixes (Chart.js width, zoom/pan, isotope lines)

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

train/vega_ml/synthetic_spectra/generate_spectra.py

@@ -128,19 +128,21 @@ def generate_training_batch(
     num_samples: int,
     output_dir: Path,
     detector_name: str = "radiacode_103",
-    duration_range: tuple = (60, 300),
+    duration_range: tuple = (30, 300),
     activity_range: tuple = (1.0, 100.0),
-    single_isotope_fraction: float = 0.4,
-    dual_isotope_fraction: float = 0.3,
-    multi_isotope_fraction: float = 0.2,
+    single_isotope_fraction: float = 0.3,
+    dual_isotope_fraction: float = 0.2,
+    multi_isotope_fraction: float = 0.15,
     background_only_fraction: float = 0.1,
+    low_signal_fraction: float = 0.15,
+    subtracted_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.
-    
+
     Args:
         num_samples: Total number of samples to generate
         output_dir: Output directory for spectra and labels
@@ -151,9 +153,11 @@ def generate_training_batch(
         dual_isotope_fraction: Fraction of two-isotope samples
         multi_isotope_fraction: Fraction of 3+ isotope samples
         background_only_fraction: Fraction of background-only samples
+        low_signal_fraction: Fraction of low-activity samples (0.01-5 Bq)
+        subtracted_fraction: Fraction of background-subtracted samples
         save_png: Whether to also save PNG images
         random_seed: Random seed for reproducibility
-    
+
     Returns:
         List of generated spectra
     """
@@ -181,11 +185,13 @@ def generate_training_batch(
     n_dual = int(num_samples * dual_isotope_fraction)
     n_multi = int(num_samples * multi_isotope_fraction)
     n_background = int(num_samples * background_only_fraction)
-    
+    n_low_signal = int(num_samples * low_signal_fraction)
+    n_subtracted = int(num_samples * subtracted_fraction)
+
     # Adjust to ensure we hit exactly num_samples
-    remaining = num_samples - (n_single + n_dual + n_multi + n_background)
+    remaining = num_samples - (n_single + n_dual + n_multi + n_background + n_low_signal + n_subtracted)
     n_single += remaining
-    
+
     total_generated = 0
 
     print(f"\nGenerating {num_samples} synthetic spectra:")
@@ -193,6 +199,8 @@ def generate_training_batch(
     print(f"  - Dual isotope: {n_dual}")
     print(f"  - Multi isotope (3+): {n_multi}")
     print(f"  - Background only: {n_background}")
+    print(f"  - Low signal (0.01-5 Bq): {n_low_signal}")
+    print(f"  - Background-subtracted: {n_subtracted}")
     print()
 
     sample_num = 0
@@ -314,6 +322,77 @@ def generate_training_batch(
 
         sample_num += 1
 
+    # Generate low-signal samples (weak sources, 0.01-5 Bq)
+    print("Generating low-signal samples...")
+    for i in range(n_low_signal):
+        isotope = np.random.choice(isotope_pool)
+        activity = np.random.uniform(0.01, 5.0)
+        duration = np.random.uniform(*duration_range)
+
+        spectrum = generate_single_isotope_sample(
+            generator,
+            isotope,
+            activity,
+            duration,
+            detector_name=detector_name,
+            include_background=True,
+            measured_background_path=measured_background_path,
+        )
+
+        save_spectrum(
+            spectrum,
+            spectra_dir,
+            save_image=True,
+            image_format='npy'
+        )
+        del spectrum
+
+        sample_num += 1
+
+        if sample_num % 100 == 0:
+            print(f"  Generated {sample_num}/{num_samples} samples...")
+
+    # Generate background-subtracted samples (simulates inference pipeline)
+    print("Generating background-subtracted samples...")
+    for i in range(n_subtracted):
+        num_iso = np.random.choice([1, 2, 3], p=[0.5, 0.3, 0.2])
+        isotopes = np.random.choice(isotope_pool, size=num_iso, replace=False)
+        activities = [np.random.uniform(0.1, 50.0) for _ in range(num_iso)]
+        duration = np.random.uniform(*duration_range)
+
+        sources = [
+            IsotopeSource(
+                isotope_name=name,
+                activity_bq=activity,
+                include_daughters=True
+            )
+            for name, activity in zip(isotopes, activities)
+        ]
+
+        config = SpectrumConfig(
+            duration_seconds=duration,
+            sources=sources,
+            include_background=True,
+            subtract_background=True,
+            detector_name=detector_name,
+            measured_background_path=measured_background_path,
+        )
+
+        spectrum = generator.generate_spectrum(config)
+
+        save_spectrum(
+            spectrum,
+            spectra_dir,
+            save_image=True,
+            image_format='npy'
+        )
+        del spectrum
+
+        sample_num += 1
+
+        if sample_num % 100 == 0:
+            print(f"  Generated {sample_num}/{num_samples} samples...")
+
     total_generated = sample_num
     print(f"\nGenerated {total_generated} samples total")