JP5586131B2 - Method and system for analyzing the presence of a radioisotope, medium storing instructions for analysis - Google Patents

Description

  The present invention relates to detecting radioactive material that can be inside or shielded from a container.

  Efforts are underway to develop technologies that can detect the presence of a substance that can be placed inside a container of interest to transport the substance to a destination. Examples of harmful substances that may be most important to identify are radioactive factors, explosive factors, biological factors, and / or chemical factors.

  Current radioisotope identification is based on peak finding and pattern matching algorithms. While these techniques may be sufficient in laboratories and in some industrial applications (eg, commercial reactors), these techniques attempt to detect shielded radioisotopes in transit. Is insufficient. This is mainly because current algorithms are not sufficient to account for the interaction between the emitted radiation and surrounding objects. The main problem in developing isotope identification software consists of two elements.

  First, the detected spectrum of shielded radioisotopes is generally weak, such as natural background radiation and nearby naturally radioactive legitimate sources (eg, bananas, cat toilets, medical May be obscured by noise from radiation emanating from the isotope. These weak spectra are difficult to analyze. This is because one of the main limitations of research in this area is that the trade flow is not delayed more than necessary by the detection process. Therefore, the longer detector integration time used to acquire a stronger signal is not practical in real-world applications. However, even with stronger signals, it is not clear that shielded radioisotopes, including some that are considered dangerous, can be located.

  Secondly, the detection and identification of specific radioisotopes depends on the interaction of ambient and radiation. This environmental interaction is generally unknown and results in one of the most difficult aspects of the radioisotope detection and identification scenario. Currently used analytical techniques ignore the effects of surrounding materials in an attempt to detect and identify threat radioisotope material in a container or shielded environment.

  What is needed is a technique to analyze radiation signature data (in the form of a spectrum) from any available detector device to quickly and accurately determine the presence of a threat substance in space. It is.

  In summary, systems and methods are provided for analyzing spectral data generated by a detector device to determine the presence of one or more threat agents of interest in the space monitored by the detector. The Substances expected to be in the detection path of the detector, including threat substances of interest and non-threat substances, are classified into one of a plurality of substance groups based on the cross-sectional area similarity. Data describing a typical energy versus cross-sectional curve for each substance group is selected from the cross-sectional areas of the individual substances in the respective substance group. The interaction of one or more of the substance groups (shielding substance groups) with each substance of interest (source) is calculated using a typical energy versus cross-section curve for each substance group. To generate spectral data calculations for the material of interest. In this way, a library of spectral data is constructed from the individual spectral data calculated for each source / shielding material combination. Spectral data generated by the detector is analyzed against a library of spectral data calculations to determine the presence of threat substances in space.

  The present invention further provides the following means.

(Item 1)
A method of analyzing spectral data generated by a detector that examines a substantially enclosed space for the presence of one or more substances of interest comprising:
a. Classifying a substance expected to be in the detection path of the detector into one of a plurality of substance groups based on the cross-sectional area similarity of the substance;
b. Selecting data describing a typical energy versus cross-section curve for each substance group from the cross-sectional areas of the individual substances within each substance group;
c. To generate spectral data for the material of interest, the representative energy versus cross-section curve for the respective material group is used to create one or more of the material groups and each interested material. Calculating the interaction of matter,
d. Generating a library of spectral data including the spectral data calculated for each substance of interest;
e. Analyzing the spectral data generated by the detector against a library of spectral data to determine the presence of a threat substance in the space.

(Item 2)
The method of item 1, wherein (c) calculating comprises calculating the interaction of each substance of interest with the substance group in various combinations and permutations.

(Item 3)
2. The method of item 1, wherein (c) calculating further comprises calculating the effect of electron transfer on the interaction of one or more of the group of substances with each of the substances of interest.

(Item 4)
2. The method of item 1, further comprising identifying a substance of interest determined to be present in the space based on the analyzing.

(Item 5)
(A) classifying includes classifying a substance into one of a number of substance groups based on one or both of a photon cross section and a neutron cross section; The method according to item 1, wherein the number of substance groups is substantially less than the number of substances in all of the substance groups to be combined.

(Item 6)
Item 6. The method according to Item 5, wherein the number of the substance groups is 10 or less.

(Item 7)
A method for determining from a spectral data generated by a detector that examines a space whether a threat substance of interest exists in the space, the interaction of one or more substance groups with the substance of interest Analyzing the spectral data from the detector as compared to a library of spectral data representing each group of substances comprising a substance expected to be present in the detection path of the detector. The material has a similar cross-sectional area.

(Item 8)
To select data describing a typical energy versus cross-section curve for each substance group from the cross-sectional areas of the individual substances in each of the substance groups, and to generate a library of the spectral data Using the representative energy versus cross-sectional area curve for the respective group of substances to calculate the interaction of one or more of the group of substances with each substance of interest. The method according to item 7.

(Item 9)
9. The method of item 8, wherein calculating the interaction of each substance of interest comprises calculating the interaction of each substance of interest with various combinations and permutations of the substance group.

(Item 10)
Calculating the interaction of each of the substances of interest further comprises calculating the effect of electron transfer on the interaction of each of the substances of interest with one or more of the groups of substances. 9. The method according to 9.

(Item 11)
8. The method of item 7, further comprising identifying a substance of interest determined to be present in the space based on the analyzing.

(Item 12)
8. A method according to item 7, wherein the number of substance groups is substantially less than the number of substances in all of the substance groups to be combined.

(Item 13)
Item 8. The method according to Item 7, wherein the number of the substance groups is 10 or less.

(Item 14)
A system for determining the presence of a threat substance of interest in a space based on spectral data generated by a detector that examines the space,
a. A data storage that stores a library of spectral data representing the interaction of one or more substance groups with a radioactive substance of interest, each substance group being expected to be in the detection path of the detector Data storage comprising a substance having a similar cross-sectional area;
b. A computational resource coupled to the data storage, wherein the computational resource is compared with the library of spectral data to determine whether the spectral data indicates the presence of a substance of interest in the space. And a computational resource for analyzing the spectral data.

(Item 15)
The library of spectral data in the data storage uses data describing a typical energy versus cross-sectional curve for each group of substances, and uses one or more of the group of substances and each substance of interest. 15. A system according to item 14, derived from calculating an interaction with.

(Item 16)
15. The system of item 14, wherein the computational resource further identifies a substance of interest determined to be present in the space.

(Item 17)
A computer readable medium, which, when executed by a computer, is compared to a library of spectral data representing the interaction of one or more substance groups with a substance of interest. Storing instructions by analyzing the spectral data to cause the computer to determine from the spectral data generated by a detector that examines the space whether or not the threat substance of interest is present in the space; A group of materials includes a material that is expected to be present in the detection path of the detector, the material having a similar cross-sectional area.

(Item 18)
When executed by a computer, the computer selects from the cross-sectional areas of individual substances within each substance group data relating to a representative energy versus cross-sectional area curve for each substance group; To generate a library of spectral data, the representative energy versus cross-section curve for the respective substance group is used to correlate one or more of the substance groups with each substance of interest. Item 18. The computer readable medium of item 17, further comprising instructions for performing an action.

(Item 19)
Item 19. The computer of item 18, wherein the instructions that cause the computer to calculate the interaction include instructions that cause the computer to calculate interactions between the substance groups of various combinations and permutations and the substances of interest. A readable medium.

(Item 20)
The instructions that cause the computer to calculate the interaction include instructions that cause the computer to further calculate the effect of electron transfer on the interaction between one or more of the substance groups and the substance of interest. Item 20. The computer-readable medium according to Item 19.

(Item 21)
Item 18. The computer readable medium of item 17, further comprising instructions that cause the computer to identify a substance of interest determined to be present in the space.
(Summary)
For each of a small number of substance groups, generated by the detector device against stored data representing the calculated interaction of the spectra of known radioisotopes with a representative cross section, Systems and methods are provided for analyzing radiation signature data (ie, spectral data). Each substance group includes substances expected to be in the detection path of the detector, and each substance group exhibits a similar cross-sectional area. A comparative analysis is performed from the spectral data received from the detector for the threat substance to determine whether the spectral data indicates the presence of the threat substance in the interrogated space. The system and method are not limited to any particular detector type and can be used with any detector that produces spectral data.

Radiation interacts with objects in a well-known manner. The emission intensity I from a source of monochromatic photons along any given path is a fraction of the original intensity I ₀ and has the relationship:

によって与えられ、ここで、（本発明のコンテクストにおいては、光子または中性子のいずれかの）粒子が、密度ρと、（特定の粒子に依存するだけでなく、粒子が物質と相互作用するときには、粒子のエネルギーＥにも依存する）相互作用に対する全断面積σと、粒子が移動する経路の長さｘとを有する単一の物質を通って移動する。より一般的な関係が、複数の物質との相互作用に対して記述され得る。従って、放射線が（空気を含む）様々な物質を通って移動すると、検出されるスペクトル（サイン）は、弱められ（振幅が減少させられ）、スペクトルの様々なピークは、エネルギーを偏移させられる傾向にある。各スペクトル特徴は、残りのスペクトルから様々に影響され得る。従って、特定の放射性同位体の識別は、信号対雑音の条件の問題であるだけでなく、エンドユーザが利益を得る方法で、問題を有する放射線の相互作用を理解すること、モデリングすること、および符号化することをも含む。

Where the particle (either photon or neutron in the context of the present invention) has a density ρ and (not only depends on the particular particle, but also when the particle interacts with matter) It travels through a single substance with a total cross-sectional area σ for interaction (which also depends on the energy E of the particle) and the length x of the path the particle travels. More general relationships can be described for interactions with multiple substances. Thus, as the radiation travels through various substances (including air), the detected spectrum (signature) is weakened (amplitude is reduced) and the various peaks of the spectrum are shifted in energy. There is a tendency. Each spectral feature can be influenced differently from the rest of the spectrum. Thus, identification of specific radioisotopes is not only a matter of signal-to-noise conditions, but also understanding, modeling, and modeling the radiation interactions in question in ways that benefit the end user, and It also includes encoding.

  These various interactions are generally understood and are well modeled (eg, using Monte Carlo computer software known in the art as “MCNP” or “PENELOPE”) However, these various interactions require that the entire geometry (including material properties) be specified. Once the geometry is defined, these software algorithms are distributed by the Monte Carlo method and an extensive cross-section library (for example, distributed by National Nuclear Data Center, the Radiation Safety Information Computational Center, and Nuclear Reaction Data Centers. The "ENDF-VI" library; which may require a large amount of computer storage space) simulates a particular problem. If the material, physical properties (eg density) and specific chemical composition of the material, and the location and geometry of each of the materials in question are not fully defined, these software algorithms are Less than optimal.

  In accordance with the present invention, an isotope identification algorithm is provided, which allows a user to interrogate spectral data (provided from any previously known or later developed detector), and This makes it possible to make a reliable operation decision based on the result. This algorithm takes into account the interaction between radiation and surrounding objects. In physics, the concept of cross-sectional area is used to represent the possibility of interactions between particles.

  First, referring to FIG. 1, one possible operating environment is shown for an isotope identification algorithm according to the present invention. The algorithm indicated by reference number 100 may be embodied by computer software executed by one or more computational resources 200. Execution of the algorithm 100 involves the use of a priori determined cross-sectional data stored in a database referred to below as the “gross material” cross-sectional database 300. The computational resource 200 operates on data generated by a detector 210 that interrogates a substantially enclosed space. The algorithm 100 is not limited to use with any particular type of detector 210, but nevertheless examples of such detectors 210 are silicon, sodium iodide, high purity germanium, zinc cadmium telluride, tetrabromide, Detectors based on thorium tetrabromide, lanthanoids, or actinide halides, or derivatives thereof, or detectors based on physical “gating” or time of flight. In general, the detector 210 can be any detector or detector system that produces a spectrum. The computational resource 200 may be a computer running on software, as described above, or may include an application specific integrated circuit, programmable logic, a digital signal processor programmed with appropriate firmware, etc. obtain.

  The detector 210 is a container 10 (1), 10 (2)..., Or other body that is scanned or inspected to determine whether it contains a radioactive source material of interest, ie, a threat substance. Arranged to detect the associated radiation. The term “container” is meant to include smaller sized containers such as, but not limited to, transport containers, trucks, rail cars, wooden boxes, and handheld containers. The detector 210 is positioned relative to the container 10 (1), 10 (2), etc. so that the detector 210 is normally positioned during operation. No special arrangement or configuration of detectors is required to perform the techniques described herein. The computational resource 200 captures the spectral data generated by the detector 210 and compares it to a library of spectral data derived from the database 300 to execute each algorithm 10 on the spectral data 10 (1 ) Determine whether radioactive threat substances exist in 10 (2) and the like. Further, the algorithm 100 may identify specific threat substances. In this way, the computational resource 200 outputs an indication of whether the threat substance is present in the container, and outputs (possibly) information identifying the specific threat substance. A library of spectral data (also called library spectra) is calculated according to the technique described below with respect to FIG.

  Now referring to FIG. 2, the nature of the problem handled by the algorithm 100 is described in more detail. The detector 210 may be positioned at some distance from the wall 12 such as the container 10 (1), 10 (2), such that the detector 210 is nominally positioned during normal operation. Wall 12 may also represent some shielding device or barrier between detector 210 and (finite and bounded) space 14 for the presence and identification of harmful radioactive materials. Scanned. It should be understood that there may be a number of walls spaced from each other or a number of walls that are not spaced from each other between the detector 210 and the space 14. There may be some applications without walls, in which case the walls need not be taken into account. The approach described herein works in both applications with walls 12 and applications without walls 12. In the example shown in FIG. 2, subject 20, subject 22, and subject 24 that do not contain or capture threat material are present in space 14, but subject 30 is harmful radioactive that is detected and identified. Contains or captures isotope threat substances. Objects 20, 22, 24, and 30 are tightly or loosely packed in space 14 in any configuration. The medium between the detector and the wall 14 can be air, water, water vapor, or any combination of these or another material.

  In accordance with the present invention, "total material" cross-sectional data is used for materials that are expected to be in the detector's detection path. This includes materials present in the space 14, present in the wall 12, and possibly in the medium between the wall 12 and the detector 210. In one embodiment, photon cross-section data is used alone. In another embodiment, a neutron cross section is used. In yet another embodiment, both photon and neutron cross section data for the expected material are used.

  Reference is now made to FIG. 3, which shows a flow diagram of a process 80 that is performed offline prior to the execution of the algorithm 100 to generate a library of spectral data. The spectral data output by the detector 210 is analyzed in comparison with the library of spectral data. At 82, the process 80 determines that all materials that are expected to be present in the detector's detection path based on the similarity of the material cross-sectional data are reduced to a small number ( Start by classifying into so-called “all substance groups” of 10 or less). In general, the number of substance groups is substantially less than the number of substances in all of the combined groups. There is a trade-off between having so many substance groups that the benefits of the described approach are not achieved and that there are very few substance groups for which the algorithm 100 is not reliably accurate. The group of substances in the detection path of the detector is also referred to herein as the “shielding substance group”. For example, the first material group can be a metal, the second material group can be a ceramic, the third material group can be an organic material, and so on. All substances considered for the purposes of the algorithm 100 are categorized into one of these groups of substances, which are not the source material of the threat of interest and the threat of interest. Source material. Next, at 84, the total cross-sectional area is selected for each substance group using known or available cross-sectional data for each substance in that substance group, thereby selecting that substance. Generate a representative energy versus cross section curve for the group. For example, FIG. 4 shows a portion of a plot of total photon cross section data for five metallic materials (as a function of energy). The similarity in the shape of the plots of the photon cross-sectional areas for these metallic materials is very clear from FIG. Thus, these metal materials are classified into the same material group (for example, designated metals). To repeat, at 82, cross-sectional data is evaluated for material collection, and materials are classified based on cross-sectional similarity. At 84, once the group is established, one of the county's materials is selected as the “representative” material, and the cross-sectional area vs. energy curve for that material becomes the “representative” disconnect for that material group. Used as an area vs. energy curve. Representative substances for a substance group are also referred to herein as “all substances”. FIG. 4 shows a plot of cross-sectional data for only five types of metallic materials. However, it should be understood that many metallic materials may have similar plots of photon cross-sectional data. Similarly, materials can be categorized by neutron cross-section data of the material (as a function of energy). Thus, in one embodiment, the material may be classified into one or both of a photon specific material group and a neutron specific material group.

  Next, at 86, a calculation is performed for each source material of interest (ie, each radioisotope), which uses data for a representative energy versus cross section curve for the group of materials. Then, the interaction between one or more of the substance groups and each source is calculated in various combinations and permutations. For example, if uranium is the source material of interest, a typical energy versus cross-section curve for a representative group of materials can be used (in various combinations and permutations) Spectral data for uranium as detected by the detector for the presence of one or more of the group of substances in various combinations and permutations by performing calculations that model the interaction of uranium with one or more of Generate a computation of In these calculations, the effect of electron transfer can be determined using any of the known calculation techniques for electron transfer models, such as MCNP and PENELOPE (either photon cross section or neutron cross section). ) Take into account. These calculations are performed for each source of interest, and the resulting spectral data is stored to generate a library of spectral data for the source of interest.

  FIG. 5 graphically depicts the calculation at 86 being modeled. In particular, for each source material of interest (the presence of which is detected in space 14), the calculation of the interaction at 86 indicates that the detector 210 is between the source material of interest and the detector 210. In various combinations and permutations, model the spectral data that generates whether the source material of interest is present in space with one or more of the group of substances. This calculation is performed for each material source of interest. FIG. 5 is simplified to illustrate this concept and shows schematic geometries and combinations for each of the various materials that may be present in the detection path. In this manner, various source spectral dependencies of the source spectral response to the environment can be incorporated into the database 300.

  Turning to the flowchart shown in FIG. 6, the algorithm 100 will now be described in detail. FIG. 6 shows that the functions in the flow chart are performed “online” after the database 300 is configured with the calculated spectral data of the library and after detection events. It is intended to indicate that this is a function that is performed after and after. At 110, data generated by the detector during a detection event is captured. Next, at 120, the cross-sectional area pattern of the commonly encountered substance is screened from the cross-sectional area pattern associated with the threat substance, so that the spectral data output by the detector indicates the presence of the threat substance. In order to determine and to identify the threat material, the spectral data is analyzed in comparison to a library of spectral data generated as described above with respect to FIG. This analysis can be based on spectral data output by the detector to determine the presence of a threat substance and to identify any threat pattern that is currently known, or any spectral pattern that will be developed in the future, to identify the threat substance. It may include using a fitting algorithm.

  At 130, it is determined whether a signature is present in the signature data, the signature data indicating the presence of a threat substance in the space 14 based on the analysis performed at 120. If so, the process continues to 134; otherwise, the process continues to 132. At 132, a non-threat signal or non-threat indication is generated with information identifying the detected non-threat radiological material (if possible), or no signal or alert is generated. At 134, a threat alert signal is generated (if possible) with information identifying the detected threat material. The user may then choose to examine more thoroughly the container or other body being scanned to identify and isolate the threat substance. At 140, preparation for the next detection event is made. That is, provision is made for another container or another part of the body of interest.

  The results at 132 and 134 can be displayed in the running application at the computational resource 200 (FIG. 1) or sent to other hardware to provide an alert. Additional outputs may include error measurements and other auxiliary information, such as interactive plots, images, surfaces, or other customized visualizations.

  Software that implements the algorithm 100 may be written in any suitable computer language. In one example, algorithm 100 is written using Interactive Data Language (IDL) and a software environment known as ENVI, both Interactive Data Language (IDL) and ENVI are marketed by ITT Corporation. . ENVI is written in IDL and has become the de facto standard for the processing, utilization, and analysis of multispectral and hyperspectral datasets. The software for algorithm 100 may be implemented as a “plug-in” in the ENVI software package, but implemented using other techniques that allow for integration into large systems (eg, as part of a detector system). obtain.

  The systems and methods described herein may be embodied in other forms without departing from the spirit or essential characteristics of the invention. Accordingly, the above embodiments are to be considered in all respects as illustrative and not as restrictive.

FIG. 1 is a block diagram illustrating a container scanning system according to an embodiment of the present invention. FIG. 2 is a schematic diagram illustrating examples of commonly encountered materials that a detector according to an embodiment of the present invention must separate. FIG. 3 is a flow diagram illustrating the steps of offline data collection and analysis that generate data used in an identification technique according to an embodiment of the present invention. FIG. 4 is a plot of photon cross-section as a function of energy for a group of metallic materials used as part of the separation and identification process of the present invention. FIG. 5 is a diagram depicting the calculations performed during the process depicted in FIG. FIG. 6 is a flow diagram illustrating an identification technique according to an embodiment of the present invention.

Explanation of symbols

10 (1), 10 (2) Container 12 Wall 14 Space 20, 22, 24 and 30 Object 100 Isotope identification algorithm 200 Computational resource 210 Detector 300 “All substances” cross-sectional area database

Claims (21)

A method of analyzing spectral data generated by a detector that examines a substantially enclosed space for the presence of one or more substances of interest comprising:
a. Classifying a substance expected to be in the detection path of the detector into one of a plurality of substance groups based on the similarity of the reaction cross-sections of the substance;
b. From the reaction cross-sectional area of each material in each material group, select one of a plurality of data describing the curve of energy versus reaction cross section which definitive in that substance group as a representative of materials of the material group To do
c. To generate spectral data for the material of interest, the energy versus reaction cross section curve for the respective material group is used to determine one or more of the material groups and each material of interest. Calculating the interaction with photons or neutrons;
d. Generating a library of spectral data including the spectral data calculated for each substance of interest;
e. Analyzing the spectral data generated by the detector against a library of spectral data to determine the presence of a threat substance in the space.   The method of claim 1, wherein the calculating (c) comprises calculating the interaction of various combinations and permutations of the group of materials with the photons or neutrons of each material of interest.   The calculating (c) further comprises calculating the effect of electron transfer on the interaction of one or more of the group of materials with the photons or neutrons of each material of interest. The method according to 1.   The method of claim 1, further comprising identifying a substance of interest determined to be present in the space based on the analyzing.   The classification (a) includes classifying a substance into one of a number of substance groups based on one or both of a photon reaction cross section and a neutron reaction cross section. The method of claim 1, wherein the number of substance groups is substantially less than the number of substances in all of the substance groups to be combined.   The method according to claim 5, wherein the number of the substance groups is 10 or less. A method for determining from a spectral data generated by a detector that examines a space whether a threat substance of interest is present in the space, wherein one or more substance groups and photons of the substance of interest or Including analyzing the spectral data from the detector as compared to a library of spectral data representing interactions with neutrons, each group of materials expected to be in the detection path of the detector Wherein the substance has a reaction cross-section similar to other substances in the same substance group . From the reaction cross-sectional area of each material in said respective group of substances, one of the plurality of data describing the curve of energy versus reaction cross section which definitive in that substance group as a representative of materials of the material group Selecting and using one or more of the groups of substances and each substance of interest using the energy versus reaction cross-section curves for the respective groups of substances to generate the library of spectral data 8. The method of claim 7, further comprising calculating the interaction of the photon or neutron with.   Calculating the interaction of photons or neutrons of each material of interest includes calculating the interaction of various combinations and permutations of the material groups with the photons or neutrons of each material of interest. The method according to claim 8.   Calculating the interaction of photons or neutrons of each material of interest is the effect of electron transfer on the interaction of one or more of the materials with the photon or neutron of each material of interest. The method of claim 9, further comprising calculating   8. The method of claim 7, further comprising identifying a substance of interest determined to be present in the space based on the analyzing.   The method of claim 7, wherein the number of substance groups is substantially less than the number of substances in all of the substance groups to be combined.   The method according to claim 7, wherein the number of the substance groups is 10 or less. A system for determining the presence of a threat substance of interest in a space based on spectral data generated by a detector that examines the space,
a. A data storage for storing a library of spectral data representing the interaction of one or more substance groups with photons or neutrons of a radioactive substance of interest, each substance group being present in the detection path of the detector A data storage, wherein the substance has a reaction cross-section similar to other substances in the same substance group ;
b. A computational resource coupled to the data storage, wherein the computational resource is compared with the library of spectral data to determine whether the spectral data indicates the presence of a substance of interest in the space. And a computational resource for analyzing the spectral data.   The library of spectral data in the data storage uses data describing energy versus reaction cross-section curves for each group of materials, and uses one or more of the groups of materials and photons of each material of interest. 15. A system according to claim 14, derived from calculating interactions with neutrons.   The system of claim 14, wherein the computational resource further identifies a substance of interest determined to be present in the space. A computer readable medium, which when executed by a computer, is a library of spectral data representing the interaction of one or more groups of substances with photons or neutrons of a substance of interest Comparing the spectral data to the computer to determine whether the threat substance of interest is present in the space from the spectral data generated by a detector that examines the space. Each substance group includes a substance expected to be present in the detection path of the detector, the substance having a reaction cross-sectional area similar to other substances in the same substance group , Computer readable medium. When executed by a computer, the reaction cross-sectional area of each material in each material group, the one of the plurality of data related to the curve of energy to react the cross-sectional area of definitive to the substance group representative of the substance groups One or more of the substance groups using the energy versus reaction cross-section curves for the respective substance groups to select as the data for a particular substance and to generate a library of the spectral data The computer-readable medium of claim 17, further comprising instructions that cause the computer to calculate the interaction of each material of interest with photons or neutrons.   The instructions that cause the computer to calculate the interaction perform to the computer to calculate the interaction of various combinations and permutations of the group of materials with the photons or neutrons of each material of interest. The computer-readable medium of claim 18, comprising instructions to be executed.   The instructions that cause the computer to calculate the interaction further comprise an effect of electron transfer on the interaction of one or more of the group of materials with a photon or neutron of each material of interest. The computer-readable medium of claim 19, comprising instructions that cause the computer to perform computations.   18. The computer readable medium of claim 17, further comprising instructions that cause the computer to identify a substance of interest determined to be present in the space.

Images