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>
74 lines
2.5 KiB
Python
74 lines
2.5 KiB
Python
"""
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CsI(Tl) detector response continuum for Radiacode 103.
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Models ONLY the detector's noise continuum. Photopeaks from environmental
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isotopes depend on measurement location and are NOT included.
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Auto-calibrated from measured background using smoothing spline (GCV)
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when available. Falls back to a simple parametric model otherwise.
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"""
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import numpy as np
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from app.config import ENERGY_OFFSET, ENERGY_SLOPE, NUM_CHANNELS
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def _get_continuum_cps():
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"""Try to load calibrated spline continuum from measured data."""
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try:
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from app.bg_calibration import load_or_calibrate
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calibrated = load_or_calibrate()
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if calibrated and "continuum_cps" in calibrated:
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return np.array(calibrated["continuum_cps"])
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except Exception:
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pass
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return None
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def generate_continuum_only(cps: float = 6.0, live_time_s: float = 3600.0):
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"""Detector response continuum only (no photopeaks, no noise)."""
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channels = np.arange(NUM_CHANNELS, dtype=np.float64)
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energy_axis = ENERGY_OFFSET + ENERGY_SLOPE * channels
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total_counts = cps * live_time_s
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# Try calibrated spline first (fits measured background)
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continuum_cps = _get_continuum_cps()
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if continuum_cps is not None and len(continuum_cps) == NUM_CHANNELS:
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# Scale calibrated CPS to match requested total counts
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continuum = continuum_cps.copy()
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if continuum.sum() > 0:
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continuum *= total_counts / continuum.sum()
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else:
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# Fallback: parametric model
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continuum = _fallback_continuum(energy_axis, total_counts)
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return {
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"energy_kev": [round(float(E), 2) for E in energy_axis],
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"counts": [round(float(c), 1) for c in continuum],
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"cps": round(cps, 2),
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"live_time_s": round(live_time_s, 1),
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}
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def _fallback_continuum(energy_axis, total_counts):
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"""Simple parametric fallback when no measured data available."""
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E = energy_axis
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# Asymmetric hump
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hump_center, sigma_left, tail_decay_right = 110.0, 40.0, 100.0
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left = np.exp(-0.5 * ((E - hump_center) / sigma_left) ** 2)
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right = np.exp(-(E - hump_center) / tail_decay_right)
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hump = np.where(E <= hump_center, left, right)
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# Housing absorption
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absorption = 1.0 * (1.0 - np.exp(-E / 20.0))
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# Compton tail
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compton = 0.5 / (np.maximum(E, 1.0) + 15.0) ** 1.3
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continuum = (hump + compton) * absorption
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if continuum.sum() > 0 and total_counts > 0:
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continuum *= total_counts / continuum.sum()
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return continuum |