## 1 Introduction  
Radioactive isotopes produce characteristic \(\gamma\)-rays of various energies and intensities that are measured by \(\gamma\) spectrometers. Such spectra serve as fingerprints that are typically used to estimate the identity and quantity of \(\gamma\) emitters in the source. Example scaled spectra from high resolution (high purity Germanium, HpGe) and low resolution (Sodium Iodide, NaI) detectors are shown in Figure 1 for \({}^{235}\)U and \({}^{239}\)Pu. The scaling was done by first converting counts \(C\) to square root of counts, \(\sqrt{C}\), and then dividing the \(\sqrt{C}\) values by the maximum value of \(\sqrt{C}\) so the plotted result lies in the interval [0,1].  
There are many applications for \(\gamma\) spectroscopy. Our focus is isotope identification in the context of nuclear nonproliferation. For example, securing national borders against terrorist threats such as dirty bombs regularly makes the news [1], as sensors at various locations screen for illicit nuclear material. Many of these sensors rely on Radio-Isotope Identification (RIID) algorithms using \(\gamma\) spectroscopy to distinguish innocent nuisance alarms arising from naturally occurring radioactive material (NORM) from alarms arising from threat isotopes. NORM examples include \({}^{40}\)K from cat litter and \({}^{232}\)Th in soil. Threat isotopes include special nuclear material (SNM) isotopes such as isotopes of U and Pu. Currently, most alarms are nuisance alarms due to innocent NORM or due to medical or industrial radioactive isotopes. Consequently, assessing and improving the performance of RIID algorithms is an important activity.  
Conventional wisdom is that increasing energy resolution as defined by peak widths improves isotope identification (see Figure 1). However, higher resolution detectors typically have lower overall efficiency, and are more costly to buy, operate and maintain. Furthermore, increasing resolution beyond some amount will not improve isotope identification, particularly if RIID algorithms are effectively tuned to each candidate resolution. Studies comparing low, medium, and high energy  
Figure 1: High resolution (HpGe) and low resolution (NaI) scaled spectra for \({}^{235}\)U and \({}^{239}\)Pu.  
resolution detectors have suggested that relatively low resolution detectors, such NaI detectors will continue to play a key role [2]. Although current RIID algorithm performance is worse than desired [3], the fact that expert spectroscopists using NaI detectors can perform well suggests there is room to improve existing RIID algorithms.  
Recent data collections for RIID testing consist of repeat measurements for each of several measurement scenarios using NaI detectors typically having 512 or 1,024 calibrated energy channels to test RIID algorithms. Each channel corresponds to a particular energy range of the incident detected \(\gamma\)-ray. It is anticipated that vendors can modify their algorithms on the basis of performance on chosen measurement scenarios and then test modified algorithms on data for other measurement scenarios. It is therefore timely to review the status of RIID algorithms. This review describes \(\gamma\) spectra from NaI detectors, measurement issues and challenges, current RIID algorithms, data preprocessing steps, the role and current quality of synthetic spectra, and opportunities for improvements.