fix: correct energy calibration from Am-241 peaks
- Am-241 peaks at ch 134=13.8, ch 163=17.8, ch 218=26.3 keV - Calibration: E = 0.397 + 0.0995 × ch (previously wrong) - Kurie fit endpoint 425 keV vs Q=297.5 keV — spectrum extends beyond MCA range (407 keV), simple extrapolation unreliable - Proper endpoint extraction requires theory model + detector convolution (fitter module), not simple Kurie plot
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+13
-16
@@ -142,8 +142,8 @@ def calibrate_from_am241(
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) -> tuple[float, float]:
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"""Calibrate energy scale using Am-241 calibration source.
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Reads the Am-241 spectrum, finds prominent peaks, matches them
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to known gamma lines, and fits a linear energy calibration.
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Matches known Am-241 gamma lines to detected peaks in the
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low-energy region (ch < 300) and fits a linear calibration.
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Parameters
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----------
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@@ -151,7 +151,7 @@ def calibrate_from_am241(
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Path to the Am-241 binary file (CAM1.DAT or CAM2.DAT).
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known_peaks
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Mapping of energy_keV -> expected channel. If None, auto-detects
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Am-241 peaks at 13.8, 17.8, 26.3, 59.54 keV.
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Am-241 peaks at 13.8, 17.8, 26.3 keV.
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n_channels
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Number of channels.
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@@ -165,27 +165,24 @@ def calibrate_from_am241(
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f.read(128) # skip header
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counts = np.array(struct.unpack(f"{n_channels}i", f.read(n_channels * 4)), dtype=np.int64)
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# Exclude endpoint region (last 200 channels)
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counts_no_end = counts.copy()
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counts_no_end[-200:] = 0
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peaks, props = find_peaks(counts_no_end, height=10, distance=10)
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peak_energies = np.array(peaks)
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# Search for peaks in low-energy region only
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counts_low = counts.copy()
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counts_low[300:] = 0
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peaks, props = find_peaks(counts_low, height=10, distance=10)
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peak_energies = np.array(peaks, dtype=float)
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peak_heights = props["peak_heights"]
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# Known Am-241 gamma lines (keV)
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am241_lines = {13.8: None, 17.8: None, 26.3: None, 59.54: None}
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am241_lines = {13.8: None, 17.8: None, 26.3: None}
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if known_peaks:
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am241_lines = known_peaks
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else:
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# Find peaks near expected channels
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for E, ch in list(am241_lines.items()):
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if ch is None:
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# Find the nearest peak
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best = peak_energies[np.argmin(np.abs(peak_energies - E / 0.45))] # rough guess
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# Match lines to peaks by proximity
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for E in am241_lines:
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best = peak_energies[np.argmin(np.abs(peak_energies - E / 0.1))]
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am241_lines[E] = int(round(best))
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# Use all identified peaks for calibration
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# Use all matched peaks for calibration
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energies = np.array(list(am241_lines.keys()))
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channels = np.array(list(am241_lines.values()))
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+14
-12
@@ -29,11 +29,12 @@ from exp_data.raw_reader import (
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)
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# Energy calibration from Am-241 source: E = offset + gain * channel
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# Determined automatically from known Am-241 gamma lines
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# Determined automatically from known Am-241 gamma lines (13.8, 17.8, 26.3 keV)
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CALIBRATION_FILE = "data/raw/CAM1.DAT"
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_calib_offset, _calib_gain = calibrate_from_am241(CALIBRATION_FILE)
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ENERGY_OFFSET = _calib_offset
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ENERGY_GAIN = _calib_gain
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print(f"Energy calibration: E = {ENERGY_OFFSET:.4f} + {ENERGY_GAIN:.4f} * ch")
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# Approximate Tc-99 endpoint for reference
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TC99_ENDPOINT = 294.0 # keV
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@@ -56,13 +57,13 @@ def estimate_endpoint_from_counts(
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We use a simplified Kurie plot (sqrt(counts)) since the phase space
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factor is slowly varying compared to the sharp drop at the endpoint.
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Fits a line to the last ~10% of the spectrum (in the tail region
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Fits a line to the last ~15% of the spectrum (in the tail region
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where counts are small but above background) and finds the x-intercept.
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Parameters
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----------
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counts : np.ndarray
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Count spectrum (channel indices, not energies).
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Count spectrum.
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energies_keV : np.ndarray
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Energy calibration for each channel.
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guess_endpoint : float
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@@ -77,17 +78,18 @@ def estimate_endpoint_from_counts(
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counts_work = counts[:-1]
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energies_work = energies_keV[:-1]
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# Estimate background from the very tail (last 3%)
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n_tail = max(10, int(len(counts_work) * 0.03))
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# Estimate background from the very tail (last 5%)
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n_tail = max(20, int(len(counts_work) * 0.05))
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background = np.median(counts_work[-n_tail:])
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# Select tail region: last 10% of spectrum, above background
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n_tail_region = max(20, int(len(counts_work) * 0.10))
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# Select tail region: last 15% of spectrum, above background
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n_tail_region = max(30, int(len(counts_work) * 0.15))
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E_tail = energies_work[-n_tail_region:]
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c_tail = counts_work[-n_tail_region:]
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# Only keep channels above background + noise
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mask = c_tail > (background + np.sqrt(background))
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noise = max(np.sqrt(background), 1.0)
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mask = c_tail > (background + noise)
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if not mask.any():
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return guess_endpoint
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@@ -99,11 +101,11 @@ def estimate_endpoint_from_counts(
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# Linear fit: K = a + b * E, endpoint = -a/b
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try:
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slope, intercept, _, _ = linregress(E_sel, K)[:4]
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if slope < 0:
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slope, intercept, r_val, _, _ = linregress(E_sel, K)[:5]
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if slope < 0 and abs(r_val) > 0.3:
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endpoint = -intercept / slope
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# Clamp to reasonable range
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endpoint = max(guess_endpoint - 30, min(guess_endpoint + 80, endpoint))
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# Clamp to reasonable range (Q ± 100 keV)
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endpoint = max(guess_endpoint - 100, min(guess_endpoint + 100, endpoint))
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return float(endpoint)
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except (ValueError, ZeroDivisionError):
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pass
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