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>
58 lines
2.6 KiB
Python
58 lines
2.6 KiB
Python
import os
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import numpy as np
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from pathlib import Path
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STATE_PATH = Path(os.environ.get("STATE_PATH", "/data/monitor_state.json"))
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CPS_LOG_PATH = Path(os.environ.get("CPS_LOG_PATH", "/data/cps_log.jsonl"))
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BACKGROUND_PATH = Path(os.environ.get("BACKGROUND_PATH", "/data/background_24h.npy"))
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BACKGROUND_SNAPSHOT_PATH = Path(os.environ.get("BACKGROUND_SNAPSHOT_PATH", "/data/background_snapshot.json"))
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LOG_DIR = Path(os.environ.get("LOG_DIR", "/logs"))
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ISOTOPE_INDEX_PATH = Path(os.environ.get("ISOTOPE_INDEX_PATH", "/models/vega_isotope_index.txt"))
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ENERGY_OFFSET = float(os.environ.get("ENERGY_CALIBRATION_OFFSET", "0.33"))
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ENERGY_SLOPE = float(os.environ.get("ENERGY_CALIBRATION_SLOPE", "2.97"))
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NUM_CHANNELS = 1023 # Last channel (1023) is overflow bin, excluded from display
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ENERGY_MIN = 30.0 # keV - detector lower limit
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ENERGY_MAX = 3000.0 # keV - detector upper limit (3 MeV)
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# CsI(Tl) non-linear response correction parameters
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# Matches the detector's non-proportional scintillation response
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CSI_NONLINEAR_ALPHA = float(os.environ.get("CSI_NONLINEAR_ALPHA", "0.37"))
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CSI_NONLINEAR_BETA = float(os.environ.get("CSI_NONLINEAR_BETA", "100.0"))
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def correct_csi_nonlinear(spectrum, num_channels=1023):
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"""Apply inverse CsI(Tl) non-linear response correction.
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Remaps spectrum channels so peaks appear at their theoretical energy
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positions, correcting for the detector's non-proportional scintillation
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response that shifts low-energy peaks upward.
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"""
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alpha = CSI_NONLINEAR_ALPHA
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beta = CSI_NONLINEAR_BETA
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output_channels = np.arange(num_channels, dtype=np.float64)
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e_true = ENERGY_OFFSET + ENERGY_SLOPE * output_channels
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e_apparent = e_true * (1 + alpha * np.exp(-e_true / beta))
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source_channels = (e_apparent - ENERGY_OFFSET) / ENERGY_SLOPE
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source_channels = np.clip(source_channels, 0, num_channels - 1.001)
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lower = np.floor(source_channels).astype(int)
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upper = np.minimum(lower + 1, num_channels - 1)
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frac = source_channels - lower
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return spectrum[lower] * (1 - frac) + spectrum[upper] * frac
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def energy_axis():
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"""Generate energy axis in keV from channel numbers, clipped to detector range."""
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axis = [round(ENERGY_OFFSET + ENERGY_SLOPE * i, 2) for i in range(NUM_CHANNELS)]
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return [e for e in axis if ENERGY_MIN <= e <= ENERGY_MAX]
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def energy_mask():
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"""Return boolean mask of channels within detector energy range."""
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return [ENERGY_MIN <= ENERGY_OFFSET + ENERGY_SLOPE * i <= ENERGY_MAX for i in range(NUM_CHANNELS)]
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def clip_to_range(arr):
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"""Clip array to detector energy range using energy mask."""
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mask = energy_mask()
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return [arr[i] for i in range(len(arr)) if mask[i]] |