KAHIBARO
10.5. Source Energy
Table of Contents
Monoenergetic sources
In GATE, every particle is generated with an energy value expressed in Geant4 units, such as keV or MeV. The simplest case is a monoenergetic source, where all primary particles are emitted with exactly the same kinetic energy.
A monoenergetic source is often used when you want to isolate a specific process or to validate your geometry and scoring. For example, you may simulate a 511 keV gamma beam for PET studies, or a 140 keV gamma source for Tc‑99m SPECT.
In OpenGATE Python, a monoenergetic source is configured by assigning a single energy value to the source, typically through a parameter like source.energy.mono. You always multiply by the corresponding GATE unit, for example:
source.energy.mono = 511 * keVThis tells GATE that every particle created by this source has a kinetic energy of 511 keV.
Monoenergetic sources are especially useful for:
- Basic detector characterization, such as energy resolution and photopeak position.
- Comparing GATE results with analytical formulas or textbook examples.
- Debugging the digitizer chain, where a single known energy simplifies interpretation of spectra.
Because all primaries have the same energy, any spread in the detected energy spectrum is due to physical interactions in the geometry and to the digitization model, not to the source itself.
For monoenergetic sources, always specify the energy in GATE units, for example 511 keV or 1 MeV. Forgetting the unit or using the wrong unit is one of the most common and serious mistakes in source definition.
Energy distributions
Realistic sources in medical physics are rarely strictly monoenergetic. X‑ray tubes, beta emitters, and many gamma emitters have continuous or multi‑line energy spectra. To represent this in GATE, the source energy is sampled from a probability distribution rather than fixed at a single value.
Conceptually, an energy distribution defines a probability density function (PDF) $p(E)$, so that each primary particle energy $E$ is drawn according to that PDF. Over many events, the histogram of simulated energies reproduces the chosen distribution.
There are several common types of energy distributions used in GATE:
- Uniform distributions: energies are sampled uniformly between a lower and upper bound, for example between 100 keV and 200 keV. This is convenient for tests, but rarely physically realistic.
- Gaussian (normal) distributions: energies are sampled around a mean value with a given standard deviation. This can approximate slightly broadened lines, but is more often used in digitization than at the source level.
- Analytical distributions: for example, simple formula based models of X‑ray spectra or beta spectra, defined by a mathematical expression that GATE samples from.
- Discrete multi‑line distributions: several discrete energies, each with a relative probability or weight, for example multiple gamma lines from a radionuclide.
- Tabulated distributions: energy values and their probabilities are read from an external file or defined as arrays, so that you can reproduce measured or precomputed spectra.
In OpenGATE, the energy distribution is usually configured by specifying a distribution type and its parameters, for example by setting attributes that select "mono", "gauss", "range", or a tabulated spectrum. For tabulated spectra you typically provide two arrays or columns: one for energy values and one for relative probabilities. GATE normalizes these internally and samples accordingly.
When you define a distribution, pay attention to the energy range and the binning. The minimum and maximum energies must cover the physically relevant range, and the resolution must be fine enough to represent important features, such as characteristic peaks.
When using energy distributions, make sure that:
- All energies are provided in correct GATE units.
- The probability array matches the energy array in length and order.
- The probabilities are non‑negative. GATE will normalize them, but inconsistent arrays can silently distort your spectrum.
Energy spectra
In GATE, an energy spectrum is both an input and an output concept. As input, an energy spectrum is the distribution from which primary energies are sampled, such as an X‑ray tube spectrum or a radionuclide gamma emission spectrum. As output, an energy spectrum is a histogram of detected energies that you obtain from hits, singles, or coincidences.
A simulated input energy spectrum typically comes from physics data or measured beam characteristics. For example, you may import a tabulated X‑ray spectrum measured for a given tube voltage and filtration, then use it to define the source energy distribution. GATE will then generate primaries such that their initial energies reproduce that spectrum.
On the output side, you often record and analyze energy spectra in actors or in the digitizer chain. For instance, an energy deposition actor can save the energy deposited in a detector volume, which you then bin into a histogram with analysis tools like ROOT or Python. After the digitizer, singles data let you build realistic detector energy spectra that include blurring, thresholds, and energy windows.
The difference between input and output spectra is significant. The input spectrum describes the particle energies at the source. The output spectrum is shaped by interactions in the materials, geometry, and detector response. For example, a monoenergetic 511 keV source will produce a narrow line at 511 keV in the input, but the detected spectrum in a scintillation detector will show a photopeak, Compton continuum, and possibly escape peaks.
When validating a GATE simulation, you frequently compare measured detector spectra with simulated output spectra. Agreement in peak position, shape, and relative peak to Compton ratio provides strong evidence that your source energy, physics list, geometry, and digitizer are correctly configured.
A common workflow is:
- Define an input energy spectrum for the source, monoenergetic or distributed.
- Run the simulation and record per‑event energy deposition or singles energy.
- Build a histogram of deposited or detected energy.
- Normalize and compare with analytical models or experimental data.
Key points for using energy spectra in GATE:
- The input energy spectrum is part of the source definition and must match the real system you are modeling.
- The output energy spectrum depends on physics processes, geometry, and digitizer settings. Differences from the input spectrum are expected and physically meaningful.
- Always check that the observed photopeak energies in the simulated detector spectrum match the known gamma energies. Large shifts usually indicate unit errors or digitizer misconfiguration.
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