0847a3fc80
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>
32 lines
982 B
Python
32 lines
982 B
Python
"""
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Vega Model - CNN-FCNN with Multi-Task Heads for Gamma Spectrum Isotope Identification
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Architecture based on research findings from:
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- Wang et al. (2026): CNN-FCNN achieves 99.8% accuracy
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- Galib et al. (2021): Hybrid CNN outperforms pure architectures
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- Turner et al. (2021): 1D CNN robust to gain shifts and shielding
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Features:
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- 1D CNN backbone for spectral feature extraction
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- Multi-task heads for isotope classification + activity regression
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- Support for 82 isotopes from the synthetic spectra database
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"""
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from .model import VegaModel, VegaConfig
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from .dataset import SpectrumDataset, create_data_loaders
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def __getattr__(name):
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if name in ('train_vega', 'VegaTrainer'):
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from .train import train_vega, VegaTrainer
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return locals()[name]
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raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
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__all__ = [
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'VegaModel',
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'VegaConfig',
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'SpectrumDataset',
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'create_data_loaders',
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'train_vega',
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'VegaTrainer'
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]
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