Commit Graph

5 Commits

Author SHA1 Message Date
091d7d9eb8 Fix: mask channels below 30 keV in inference and training to prevent misidentification
Below ~30 keV the detector signal is dominated by X-ray fluorescence (L-shell)
and artifacts not modelled in training data. This spurious low-energy continuum
caused the model to misidentify Am-241 as Th-232/U-235. Masking channels <30 keV
before inference fixes Am-241 detection from 2% to 99%. Same masking applied in
the synthetic spectrum generator for consistent retraining.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-05-21 21:55:34 +02:00
0847a3fc80 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>
2026-05-21 17:35:22 +02:00
c764a5c264 Dash web: crosshair, zoom/pan X, scale log/lin, continuum extraction, background resume
- Tooltip entier (intersect:false) + ligne verticale crosshair sur tous les graphes
- Zoom molette/pinch sur l'axe X, pan souris, limites clamped 30-3000 keV
- Toggle échelle log/linéaire onglet Background
- Extraction continuum détecteur (isotope peaks subtracted + Gaussian smoothing)
- Reprise snapshot précédent au démarrage capture_background.py
- Suppression refs "Théorique" et "Bruit capteur" de l'interface
- Plugin chartjs-plugin-zoom + hammerjs via CDN
- Fix Chart constructor spread operator

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-05-19 23:26:28 +02:00
1e0c1a5ea5 Dashboard web FastAPI + Chart.js
- 4 vues : spectre temps reel, historique detections, background, timeline CPS
- API REST : /api/status, /api/spectrum/current, /api/spectrum/difference,
  /api/background, /api/background/spectrum, /api/history, /api/cps/timeline
- Frontend vanilla JS + Chart.js (pas de Node.js, leger pour Pi 4)
- Moniteur modifie pour exporter son etat dans /data/monitor_state.json
  et le CPS dans /data/cps_log.jsonl chaque cycle
- Nouveau conteneur Docker 'web' sur port 8080
- Theme sombre, calibration energie (E = 0.33 + 2.97 * canal)

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-05-19 13:33:07 +02:00
745a64b342 Pipeline complet Radiacode 103 - identification automatique d'isotopes
- VegaModel CNN-FCNN 34.5M params, 82 isotopes, val acc 99.89%
- Generation 50k spectres synthetiques 1D (12-24h durees)
- Entrainement 100 epochs sur RTX 5060 Ti (CUDA 12.8, Blackwell)
- Detection continue avec soustraction du background
- Capture background 24h avec gestion deconnexion
- Docker Compose : conteneur train (GPU) + detect (CPU/USB)
- Modele entraite inclus (vega_best.pt, 395 Mo)

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-05-19 12:29:56 +02:00