## 11 The Role and Quality of Synthetic Spectra  
RIID algorithm performance assessment is currently based on real data collections such as those described in the previous section. Because computer models such as MCNP-X [32] and GEANT4 [33] can model the source, geometry, and shielding with high fidelity, it is likely that synthetic spectra can augment real data for algorithm testing and improvement. The main research area remaining in generating high-fidelity synthetic spectra is the DRF. Various aspects of NaI detectors such as the scintillator crystal imperfections and PMT electronics are very difficult to model using first principles. Therefore, various empirical or semi-empirical options are being developed [34].  
Regarding the DRF, there are three key features of the spectra to consider, including the Gaussian broadening due to various physical effects such as the PMT as previously described; the exponential tails associated with peaks, and the \"flat continua\" that are often called the \"Compton\" continua. As an aside, a first principles physics model of the associated background counts (that must be added to the source counts in order to mimic real spectra) is difficult to model in general. In principle, one could assess the particular background sources, shielding, and geometries and attempt to model the background, but this would involve too large an effort for practical use. In practice, software such as GADRAS instead allows the user to fit the measured background using a small collection of isotopes that are ubiquitous in the environment, such as \({}^{232}\)Th and \({}^{226}\)Ra.  
It is currently believed that accurate background simulation is less important for low resolution detectors (NaI) than for relatively high resolution detectors such as HpGe detectors. Qualitatively, this is because NaI detectors smooth/average over closely-separated peaks and therefore are not as likely to mistake a background-radiation-induced peak for a source peak. Also, the background need not always be modeled; for example, the effects of a relevant measured background could in many contexts be combined with the modeled source spectrum.  
Figure 7 is an example of real and simulated NaI spectra (real or simulated counts recorded in one second) for \({}^{60}\)Co. The simulation used MCNP-X, and either small, medium, or large strength background was assumed, while normalizing the simulated counts to roughly match the real counts in one region of interest. Details of this simulation are available in [35] and available by request. We are not aware of any numerical studies that compare RIID algorithm performance on real spectra to performance on corresponding simulated spectra. Instead, studies make qualitative comparisons such as visual inspection of Figure 7 to assess simulation quality.  
Reference [34] reviews three options to estimate DRFs: experimental, computer model, or semi-empirical involving the use of both real and model results to characterized the three key features mentioned above (Gaussian broadening, exponential tails of peaks, and flat continua). Because RIID algorithms must essentially solve the \"inverse DRF\" problem by mapping a measured spectrum to an estimated \"true\" spectrum at the detector surface arising from the source of interest, improved DRF models should lead to improved isotope ID algorithms.