unzhakov
5f99566a0e
- Analytical detector response: gaussian, gaussian_tail, tikhonov models - Low-energy tail modeling for charge collection effects - Response matrix convolution via R^T · (Φ·ΔW) - Declarative API: create_detector_from_config(), convolve_detector() - BetaSpectrumAnalyzer.convolved_spectrum() for auto-convolution - Integration tests: TestBetaSpectrumIntegration, TestDeclarativeDetectorResponse - Documentation: docs/08-detector-response.md
182 lines
7.5 KiB
Markdown
182 lines
7.5 KiB
Markdown
______________________________________________________________________
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title: "08 – Detector Response Convolution"
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date: 2026-05-01
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tags:
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- beta-spectrum/detector-response
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status: active
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aliases: [Convolution, Response Matrix, Smearing]
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cssclasses: []
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related_components: \[[00-beta-spectrum-overview]\], \[[06-radiative-corrections]\]
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______________________________________________________________________
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# Detector Response Convolution — $M(E_j) = \\sum_i \\Phi(W_i) \\cdot R(E_j; W_i) \\cdot \\Delta W_i$
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## Overview
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Detector response convolution bridges the gap between theoretical beta spectra and experimentally measured data. A theoretical spectrum $\\Phi(W)$ assumes perfect energy resolution, while real detectors smear the true energy distribution due to finite resolution, charge collection inefficiencies, and electronic noise.
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The convolution operation:
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$$M_j = \\sum_i \\Phi(W_i) \\cdot R(E_j; W_i) \\cdot \\Delta W_i$$
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where:
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- $\\Phi(W_i)$ — theoretical differential spectrum at true energy $W_i$
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- $R(E_j; W_i)$ — detector response probability: probability that an electron of true energy $W_i$ deposits energy $E_j$ in channel $j$
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- $\\Delta W_i$ — energy bin width at $W_i$
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- $M_j$ — predicted measured counts in detector channel $j$
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## Two Modes of Operation
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### Tabulated Response
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Receive a pre-computed response matrix from Monte Carlo simulations (GEANT4, EGSNRC) or dedicated calibration measurements. The matrix has shape $(N\_{\\text{calib}}, N\_{\\text{channels}})$, where each row $i$ is the detector response to mono-energetic electrons at calibration energy $E_i$.
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```python
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from beta_spectrum import DetectorResponse
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# Load from MC simulation output
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detector = DetectorResponse.from_mc_simulation(
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channel_energies=channel_grid,
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response_matrix=mc_response,
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calibration_energies=mc_energies,
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)
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```
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### Analytical Model
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Parameterized model with Gaussian core + exponential tail, suitable for quick estimates when no tabulated response is available.
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```python
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detector = DetectorResponse.from_gaussian_params(
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channel_energy_range=(1.0, 1.6), # in m_e units
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n_channels=4096,
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sigma_a=0.0, # energy-independent resolution (m_e)
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sigma_b=0.0, # sqrt(E) contribution (m_e^{-1/2})
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tail_fraction=0.0, # fraction of events in exponential tail
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tau=0.01, # decay constant of tail (m_e)
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model="gaussian", # "gaussian" | "gaussian_tail" | "tikhonov"
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fano_factor=0.0, # Fano factor (0.12 for Si, ~0 for MMC)
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)
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```
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## Analytical Response Models
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### Gaussian Core
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Pure Gaussian peak with energy-dependent resolution:
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$$G(E; E_0, \\sigma) = \\frac{1}{\\sigma\\sqrt{2\\pi}} \\exp\\left(-\\frac{(E - E_0)^2}{2\\sigma^2}\\right)$$
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### Gaussian + Exponential Tail
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Models low-energy tailing from charge collection inefficiencies in semiconductor detectors (standard for Si-PIPS detectors):
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$$f(E) = (1 - f\_{\\text{tail}}) \\cdot G(E; E_0, \\sigma) + f\_{\\text{tail}} \\cdot T(E; E_0, \\sigma, \\tau)$$
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where $T$ is the Tikhonov function:
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$$T(E) = \\frac{1}{2\\tau} \\exp\\left(\\frac{\\tau(E - E_0)}{2} + \\frac{\\sigma^2\\tau^2}{8}\\right) \\cdot \\text{erfc}\\left(\\frac{E - E_0}{\\sqrt{2}\\sigma} + \\frac{\\sigma\\tau}{\\sqrt{2}}\\right)$$
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### Energy-Dependent Resolution
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$$\\sigma(E) = \\sqrt{\\sigma_a^2 + (\\sigma_b \\sqrt{E})^2 + F \\cdot E \\cdot w}$$
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- $\\sigma_a$ — energy-independent contribution (electronic noise, etc.)
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- $\\sigma_b$ — $\\sqrt{E}$ term (statistical fluctuations in charge creation)
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- $F$ — Fano factor ($\\approx 0.12$ for Si, $\\approx 0$ for MMCs)
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- $w$ — mean ionization energy ($\\approx 3.6$ eV for Si, converted to $m_e$ units)
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## Calculator Implementation
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> [!tip] Code mapping
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> Module: `beta_spectrum/components/detector_response.py` → `DetectorResponse` class
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>
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> - **Analytical models**: Gaussian core, Gaussian + exponential tail (Tikhonov)
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> - **Tabulated mode**: Linear interpolation from MC-calculated response matrix
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> - **Convolution**: `DetectorResponse.convolve(W, spectrum, normalize=True)`
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> - **Integration with BetaSpectrum**: `BetaSpectrum.convolve_with_detector(detector, W, config)`
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> - **Integration with BetaSpectrumAnalyzer**: `BetaSpectrumAnalyzer.convolved_spectrum(detector, normalize)`
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### API Usage
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```python
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from beta_spectrum import (
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BetaSpectrum, SpectrumConfig, DetectorResponse,
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BetaSpectrumAnalyzer, T_to_W
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)
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# 1. Create theoretical spectrum for 99Tc
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config = SpectrumConfig(
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Z_parent=43, Z_daughter=44, A_number=99,
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endpoint_MeV=0.294 # 99Tc Q-value
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)
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spectrum = BetaSpectrum.from_config(config)
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W, kinetic_MeV = spectrum.get_energy_grid(config)
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# 2. Define detector response (sigma = 1.0 keV in m_e units)
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sigma_1keV_me = 1.0 / 511.0 # convert keV to m_e units
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W0 = T_to_W(0.294)
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detector = DetectorResponse.from_gaussian_params(
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channel_energy_range=(1.0, W0 + 0.05),
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n_channels=512,
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sigma_a=sigma_1keV_me,
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sigma_b=0.0,
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tail_fraction=0.0,
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model="gaussian",
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fano_factor=0.0,
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)
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# 3. Convolve
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convolved = spectrum.convolve_with_detector(detector, W=W, config=config)
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# 4. Or use the Analyzer
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analyzer = BetaSpectrumAnalyzer(spectrum, config)
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convolved_normalized = analyzer.convolved_spectrum(detector, normalize=True)
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```
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## Convolution Algorithm
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The convolution proceeds in three steps:
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1. **Build response matrix**: For each energy bin $W_i$, compute the detector response $R(E_j; W_i)$ across all channels $j$. Result: matrix of shape $(N\_{\\text{bins}}, N\_{\\text{channels}})$.
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1. **Weight by spectrum**: Multiply each response row by the theoretical spectrum value $\\Phi(W_i)$ and bin width $\\Delta W_i$.
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1. **Sum over energies**: For each channel $j$, sum contributions from all energy bins:
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$$M_j = \\sum_i \\Phi(W_i) \\cdot R(E_j; W_i) \\cdot \\Delta W_i = (R^T \\cdot (\\Phi \\cdot \\Delta W))\_j$$
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The bin width is computed as the half-bin average: $\\Delta W_i = \\frac{1}{2}(\\Delta W\_{i-1/2} + \\Delta W\_{i+1/2})$.
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> [!note] Normalization
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> Each response function $R(E; W_i)$ is normalized to unit area before convolution. This ensures that convolving a mono-energetic line produces a response with the same total counts. The final convolved spectrum preserves the integral of the theoretical spectrum.
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## Energy Units
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All detector response computations use **total energy in units of $m_e c^2$** (natural units, $m_e = 1$):
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- $m_e c^2 = 510.998950$ keV
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- Conversion: $W = 1 + T / (m_e c^2)$ where $T$ is kinetic energy
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- A resolution of $\\sigma = 1.0$ keV corresponds to $\\sigma = 1.0 / 511.0 \\approx 0.00196$ in $m_e$ units
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> [!warning] Unit consistency
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> The detector channel energy range and the spectrum energy grid must be in the same units (total energy in $m_e$ units). Use `T_to_W()` from `beta_spectrum.utils` to convert kinetic energy in MeV to total energy in $m_e$ units.
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## Physical Interpretation
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The convolution operation smears the sharp theoretical spectrum into a realistic measured spectrum:
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- **Peak broadening**: Finite energy resolution widens spectral features
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- **Endpoint smearing**: The sharp cutoff at $W_0$ becomes a gradual fall-off
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- **Tail effects**: Charge collection inefficiencies create low-energy tails (modeled by `gaussian_tail`)
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- **Count conservation**: Total number of events is preserved (integral of convolved spectrum = integral of theoretical spectrum)
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## References
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- Hayen et al., RevModPhys **90**, 015008 (2017) — Comprehensive review of beta-decay theory
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- Tikhonov, Soviet Physics Doklady **8**, 591 (1964) — Original formulation of the response function
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- Sigmund, Phys. Rev. **130**, 1497 (1963) — Low-energy tailing in semiconductor detectors
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