Engineering topics

Wolf, Karl

Publications and source records attributed to Wolf, Karl.

11 records

NASA NTRS2002

An End-to-End Architecture for Science Goal Driven Observing

New observatories will have greater on-board storage capacity and on-board processing capabilities. The new bottleneck will be download capacity. The cost of downlink time and limitations of bandwidth will end the era where all exposure data is downloaded and all data processing is performed on the ground. In addition, observing campaigns involving inherently variable targets will need scheduling flexibility to focus observing time and data download on exposures that are scientifically interesting. The ability to quickly recognize and react to such events by re-prioritizing the observing schedule will be an essential characteristic for maximizing scientific returns. It will also be a step towards increasing spacecraft autonomy, a major goal of NASA's strategic plan. The science goal monitoring (SGM) system is a proof-of-concept effort to address these challenges. We are developing an interactive distributed system that will use on-board processing and storage combined with event-driven interfaces with ground-based processing and operations, to enable fast re-prioritization of observing schedules, and to minimize time spent on non-optimized observations. SGM is initially aimed towards time-tagged observing modes used frequently in spectroscopic studies of varying targets. In particular, the SGM is collaborating with the proposed MIDEX-class mission Kronos team. The variable targets that Kronos seeks to study make an adaptive system such as SGM particularly valuable for achieving mission goals. However, the architecture and interfaces will also be designed for easy adaptability to other observing platforms, including ground-based systems and to work with different scheduling and pipeline processing systems. This talk will focus on our strategy for developing SGM and the technical challenges that we have encountered. We will discuss the SGM architecture as it applies to the Kronos mission and explain how it is scalable to other missions.

Jones, Jeremy↗

NASA NTRS2002

Science Goal Driven Observing: A Step Towards Maximizing Science Returns and Spacecraft Autonomy

While recent missions have made great strides in the ability to autonomously monitor and react to changing health and physical status of spacecraft, little progress has been made in responding quickly to science driven events. Also, the new generation of space-based telescopes will generate data at an unprecedented rate. Yet, while on-board data processing and storage capability will increase rapidly, bandwidth for downloading data will not increase as fast and can become a significant cost of a science program. For observations of inherently variable targets and targets of opportunity, the ability to recognize early if an observation will not meet the science goals, and react accordingly, can have a major positive impact on the overall scientific returns of an observatory and on its operational costs. If the observatory can reprioritize the schedule to focus on alternate targets, discard uninteresting observations prior to downloading, or download them at a reduced resolution its overall efficiency will be dramatically increased. We are investigating and developing tools for a science goal monitoring (SGM) system. The SGM will have an interface to help capture higher-level science goals from the scientists and translate them into a flexible observing strategy that SGM can execute and monitor. SGM will then monitor the incoming data stream and interface with data processing systems to recognize significant events. When an event occurs, the system will use the science goals given it to reprioritize observations, and react appropriately and/or communicate with ground systems - both human and machine - for confirmation and/or further high priority analyses.

Jones, Jeremy↗

NASA NTRS2002

Science Goal Driven Observing and Spacecraft Autonomy

Spacecraft autonomy will be an integral part of mission operations in the coming decade. While recent missions have made great strides in the ability to autonomously monitor and react to changing health and physical status of spacecraft, little progress has been made in responding quickly to science driven events. For observations of inherently variable targets and targets of opportunity, the ability to recognize early if an observation will meet the science goals of a program, and react accordingly, can have a major positive impact on the overall scientific returns of an observatory and on its operational costs. If the onboard software can reprioritize the schedule to focus on alternate targets, discard uninteresting observations prior to downloading, or download a subset of observations at a reduced resolution, the spacecraft's overall efficiency will be dramatically increased. The science goal monitoring (SGM) system is a proof- of-concept effort to address the above challenge. The SGM will have an interface to help capture higher-level science goals from the scientists and translate them into a flexible observing strategy that SGM can execute and monitor. We are developing an interactive distributed system that will use on-board processing and storage combined with event-driven interfaces with ground-based processing and operations, to enable fast re-prioritization of observing schedules, and to minimize time spent on non-optimized observations. This paper will focus on our strategy for developing SGM and the technical challenges that we have encountered. We will discuss the SGM architecture as it applies to the proposed MIDEX-class mission Kronos. However, the architecture and interfaces will also be designed for easy adaptability to other observing platforms, including ground-based systems and to work with different scheduling and pipeline processing systems.

Jones, Jeremy↗

NASA NTRS2002

Science Goal Driven Observing and Spacecraft Autonomy

Spacecraft autonomy will be an integral part of mission operations in the coming decade. While recent missions have made great strides in the ability to autonomously monitor and react to changing health and physical status of spacecraft, little progress has been made in responding quickly to science driven events. For observations of inherently variable targets and targets of opportunity, the ability to recognize early if an observation will meet the science goals of a program, and react accordingly, can have a major positive impact on the overall scientific returns of an observatory and on its operational costs. If the onboard software can reprioritize the schedule to focus on alternate targets, discard uninteresting observations prior to downloading, or download a subset of observations at a reduced resolution, the spacecraft's overall efficiency will be dramatically increased. The science goal monitoring (SGM) system is a proof-of-concept effort to address the above challenge. The SGM will have an interface to help capture higher level science goals from the scientists and translate them into a flexible observing strategy that SGM can execute and monitor. We are developing an interactive distributed system that will use on-board processing and storage combined with event-driven interfaces with ground-based processing and operations, to enable fast re-prioritization of observing schedules, and to minimize time spent on non-optimized observations.

Koratkar, Amuradha↗

NASA NTRS2002

Science Goal Driven Observing: A Step Towards Maximizing Science Returns and Spacecraft Autonomy

In the coming decade, the drive to increase the scientific returns on capital investment and to reduce costs will force automation to be implemented in many of the scientific tasks that have traditionally been manually overseen. Thus, spacecraft autonomy will become an even greater part of mission operations. While recent missions have made great strides in the ability to autonomously monitor and react to changing health and physical status of spacecraft, little progress has been made in responding quickly to science driven events. The new generation of space-based telescopes/observatories will see deeper, with greater clarity, and they will generate data at an unprecedented rate. Yet, while onboard data processing and storage capability will increase rapidly, bandwidth for downloading data will not increase as fast and can become a significant bottleneck and cost of a science program. For observations of inherently variable targets and targets of opportunity, the ability to recognize early if an observation will not meet the science goals of variability or minimum brightness, and react accordingly, can have a major positive impact on the overall scientific returns of an observatory and on its operational costs. If the observatory can reprioritize the schedule to focus on alternate targets, discard uninteresting observations prior to downloading, or download them at a reduced resolution its overall efficiency will be dramatically increased. We are investigating and developing tools for a science goal monitoring (SGM) system. The SGM will have an interface to help capture higher-level science goals from scientists and translate them into a flexible observing strategy that SGM can execute and monitor. SGM will then monitor the incoming data stream and interface with data processing systems to recognize significant events. When an event occurs, the system will use the science goals given it to reprioritize observations, and react appropriately and/or communicate with ground systems - both human and machine - for confirmation and/or further high priority analyses.

Koratkar, Anuradha↗

NASA NTRS2001

Linking Science Analysis with Observation Planning: A Full Circle Data Lifecycle

A clear goal of the Virtual Observatory (VO) is to enable new science through analysis of integrated astronomical archives. An additional and powerful possibility of the VO is to link and integrate these new analyses with planning of new observations. By providing tools that can be used for observation planning in the VO, the VO will allow the data lifecycle to come full circle: from theory to observations to data and back around to new theories and new observations. The Scientist's Expert Assistant (SEA) Simulation Facility (SSF) is working to combine the ability to access existing archives with the ability to model and visualize new observations. Integrating the two will allow astronomers to better use the integrated archives of the VO to plan and predict the success of potential new observations more efficiently, The full circle lifecycle enabled by SEA can allow astronomers to make substantial leaps in the quality of data and science returns on new observations. Our paper examines the exciting potential of integrating archival analysis with new observation planning, such as performing data calibration analysis on archival images and using that analysis to predict the success of new observations, or performing dynamic signal-to-noise analysis combining historical results with modeling of new instruments or targets. We will also describe how the development of the SSF is progressing and what have been its successes and challenges.

Grosvenor, Sandy↗

NASA NTRS2001

Linking Science Analysis with Observation Planning: A Full Circle Data Lifecycle

A clear goal of the Virtual Observatory (VO) is to enable new science through analysis of integrated astronomical archives. An additional and powerful possibility of the VO is to link and integrate these new analyses with planning of new observations. By providing tools that can be used for observation planning in the VO, the VO will allow the data lifecycle to come full circle: from theory to observations to data and back around to new theories and new observations. The Scientist's Expert Assistant (SEA) Simulation Facility (SSF) is working to combine the ability to access existing archives with the ability to model and visualize new observations. Integrating the two will allow astronomers to better use the integrated archives of the VO to plan and predict the success of potential new observations. The full circle lifecycle enabled by SEA can allow astronomers to make substantial leaps in the quality of data and science returns on new observations. Our paper will examine the exciting potential of integrating archival analysis with new observation planning, such as performing data calibration analysis on archival images and using that analysis to predict the success of new observations, or performing dynamic signal-to-noise analysis combining historical results with modeling of new instruments or targets. We will also describe how the development of the SSF is progressing and what has been its successes and challenges.

Jones, Jeremy↗

NASA NTRS2001

Code Sharing and Collaboration: Experiences from the Scientist's Expert Assistant Project and their Relevance to the Virtual Observatory

In the Virtual Observatory (VO), software tools will perform the functions that have traditionally been performed by physical observatories and their instruments. These tools will not be adjuncts to VO functionality but will make up the very core of the VO. Consequently, the tradition of observatory and system independent tools serving a small user base is not valid for the VO. For the VO to succeed, we must improve software collaboration and code sharing between projects and groups. A significant goal of the Scientist's Expert Assistant (SEA) project has been promoting effective collaboration and code sharing between groups. During the past three years, the SEA project has been developing prototypes for new observation planning software tools and strategies. Initially funded by the Next Generation Space Telescope, parts of the SEA code have since been adopted by the Space Telescope Science Institute. SEA has also supplied code for SOFIA, the SIRTF planning tools, and the JSky Open Source Java library. The potential benefits of sharing code are clear. The recipient gains functionality for considerably less cost. The provider gains additional developers working with their code. If enough users groups adopt a set of common code and tools, defacto standards can emerge (as demonstrated by the success of the FITS standard). Code sharing also raises a number of challenges related to the management of the code. In this talk, we will review our experiences with SEA - both successes and failures - and offer some lessons learned that may promote further successes in collaboration and re-use.

Jones, Jeremy↗

NASA NTRS1991

Transportable Application Environment (TAE Plus)

Programmer's tool for fast and easy development of user interfaces, TAE Plus allows applications to be moved from one computer to another without affecting end user. Based on X-Windows and Motif.

Costenbader, Jay↗

NASA NTRS1991

Program For Generating Interactive Displays

Sun/Unix version of Transportable Applications Environment Plus (TAE+) computer program provides integrated, portable software environment for developing and running interactive window, text, and graphical-object-based application software systems. Enables programmer or nonprogrammer to construct easily custom software interface between user and application program and to move resulting interface program and its application program to different computers. Plus viewed as productivity tool for application developers and application end users, who benefit from resultant consistent and well-designed user interface sheltering them from intricacies of computer. Available in form suitable for following six different groups of computers: DEC VAX station and other VMS VAX computers, Macintosh II computers running AUX, Apollo Domain Series 3000, DEC VAX and reduced-instruction-set-computer workstations running Ultrix, Sun 3- and 4-series workstations running Sun OS and IBM RT/PC and PS/2 compute

Costenbader, Jay↗

NASA NTRS1991

Another Program For Generating Interactive Graphics

VAX/Ultrix version of Transportable Applications Environment Plus (TAE+) computer program provides integrated, portable software environment for developing and running interactive window, text, and graphical-object-based application software systems. Enables programmer or nonprogrammer to construct easily custom software interface between user and application program and to move resulting interface program and its application program to different computers. When used throughout company for wide range of applications, makes both application program and computer seem transparent, with noticeable improvements in learning curve. Available in form suitable for following six different groups of computers: DEC VAX station and other VMS VAX computers, Macintosh II computers running AUX, Apollo Domain Series 3000, DEC VAX and reduced-instruction-set-computer workstations running Ultrix, Sun 3- and 4-series workstations running Sun OS and IBM RT/PC's and PS/2 computers running AIX, and HP 9000 S

Costenbader, Jay↗