ITR: Collaborative research: software for interpretation of cosmogenic isotope inventories - a combination of geology, modeling, software engineering and artificial intelligence Marek Zreda, U. Arizona - isotopes, modeling Elizabeth Bradley, U. Colorado - artificial intelligence Ken Anderson, U. Colorado - software engineering Funded by: NSF/IIS/EAR Duration: Budget: $1.6M

Software for all occasions Software will satisfy computation and information needs in six areas: (1) production rate calibration and scaling; (2) calculation of individual sample age using production rates from item 1 above; (3) calculation of surface exposure age (also called apparent age ) of the landform from multiple sample ages (each calculated from item 2); (4) calculation of model age of the landform (correcting item 3 for geological processes that affect apparent age); (5) compiling information necessary for production rate calculations (raw physical, chemical, and geological data); (6) compiling information on existing and potential applications (what can be dated and how).

Structure of software The artificial intelligence core links databases (knowledge, information and data storage) and models (geological and mathematical) on one side, with field and laboratory data on the other side, to produce results (numerical output, interpretation, speculations and theorizing, and advice on further course of action). Solid lines and arrows show input of information and data; dashed lines and arrows show requests and feedback.

Two challenges Challenge 1: Integration. Effective analysis of geochemical data requires a heterogeneous collection of tools and techniques, applied in the right order to the right data, and guided by the right interpretation. Each analysis method rests upon different facets of the underlying science and demands different software algorithms. Current analysis tools are useful from the computational point of view, but they share the same two shortcomings: they do not have user-friendly interfaces, and so they are practically unusable by scientists other than their authors; and they are not integrated with programs that calculate exposure ages from cosmogenic concentrations. Our software: We will construct a software architecture and component framework for the analysis and interpretation of cosmogenic nuclides in terrestrial environments. The use of components will ease the construction of new analysis tools by transforming the current (and manual) “development from scratch” process into an automated “development by assembly” process. The software architecture will provide a uniform framework that combines the computational models, scientific databases, and AI capabilities into a cohesive whole accessed by a usable, intuitive, and flexible user interface.

Two challenges Challenge 2: Complexity. The complexity of the cosmogenic nuclide dating poses another IT challenge. Our software: We will incorporate AI techniques directly into the software tool, in order to explicitly capture the properties and dependencies of the analysis process - e.g., that one should collect additional samples if the numerical analysis of geochemical results indicates that there is too much noise in the data to produce the desired temporal resolution - and advise the user accordingly. This kind of dedicated, knowledge-based data-analysis support facility will be useful in assisting both naive and experienced users. Building it will be a significant knowledge engineering problem, and it will require effort and communication from all the PIs, working together.

Objectives To develop, within a uniform framework: (1) Geological-mathematical models to quantitatively describe the accumulation of cosmogenic nuclides in evolving landforms, and to invert field and isotopic data to obtain landform ages and rates and frequencies of geological processes that act on these landforms. (2) Databases of basic and applied knowledge and data useful in the evaluation of cosmogenic data, drawn from all fields relevant to cosmogenic nuclide geochemistry: nuclear physics and cosmic-ray physics, chemistry and geochemistry, various fields of geology, atmospheric sciences, magnetism and paleomagnetism, statistics and mathematics.

Objectives (3) An artificial intelligence system for analysis of cosmogenic data, connecting logically all existing information and models (from objectives 1 and 2) with any new, user-generated data. The system will have three main uses: research design (construction of testable hypotheses), analysis and interpretation of isotopic data, and speculation under conditions of uncertain or absent information. (4) A software architecture that supports the construction of a state-of- the-art component-based software system that enables “plug-and- play” assembly of software tools that embody the features of objectives 1-3 above.

Not an objective It is not our objective to improve calibrations or scaling functions, or to improve the interpretations of published applications of cosmogenic dating. Our goal is to develop a new integrated analysis tool for cosmogenic isotope dating, explore its potential and show its versatility, computational feasibility and accuracy. For improved calibrations and scaling - we have CRONUS!

Three-tiered architecture