Engineering topics

Harley Thronson

Publications and source records attributed to Harley Thronson.

5 records

NASA NTRS2022

Determining Research Priorities for Astronomy Using Machine Learning

We summarize the first exploratory investigation into whether Machine Learning (ML) techniques can augment science strategic planning. We find that an approach based on Latent Dirichlet Allocation (LDA) using abstracts drawn from high-impact astronomy journals may provide a leading indicator of future interest in a research topic. We show two topic metrics that correlate well with the high-priority research areas identified by the 2010 National Academies’ Astronomy and Astrophysics Decadal Survey. One metric is based on a sum of the fractional contribution to each topic by all scientific papers (“counts”) while the other is the Compound Annual Growth Rate (CAGR) of counts. These same metrics also show the same degree of correlation with the whitepapers submitted to the same Decadal Survey. Our results suggest that the Decadal Survey may under-emphasize fast growing research. A preliminary version of our work was presented by Thronson et al. (2021).

Astronomy↗

NASA NTRS2021

Report on Workshop on Artificial Intelligence in Strategic Planning and Science Prioritization

This report details the observations from a two-day virtual workshop, held May 12-13, 2020, focused on whether, and how, artificial intelligence (AI) could assist humans in strategic planning, specifically in science and technology prioritization. The participants identified several “key challenges” that AI might tackle in this area. To further understand the value of these key challenges the workshop then developed related test cases that would demonstrate specifically how AI/machine learning (ML) could provide assistance to humans. Approximately 40 subject matter experts (SMEs), with backgrounds in AI, strategic planning for science, and scientific data, were gathered for the conference. This report collates the details of the output of the workshop. The “best” test cases include (in no particular order):Use of AI to assist in selecting Decadal Survey priorities. * Use of AI to identify new, or previously unidentified, science topics for prioritization. * Using AI to better label and increase discoverability of scientific literature and proposals. * Use of AI to enhance current observation capabilities for scientific missions. * Using AI to mitigate biases in selection of proposal reviewers and membership of advisory committees. Examination of these test cases indicates that Natural Language Processing (NLP) is a common capability found in most of the ”best” (top-rated) test cases and is a valuable, multi-purpose tool which enables ML in this area.

strategic planning↗

NASA NTRS2017

Human Space Flight and Future Major Space Astrophysics Missions: Servicing and Assembly

Some concepts for candidate future "flagship" space observatories approach the payload limits of the largest launch vehicles planned for the next few decades, specifically in the available volume in the vehicle fairing. This indicates that an alternative to autonomous self-deployment similar to that of the James Webb Space Telescope will eventually be required. Moreover, even before this size limit is reached, there will be significant motivation to service, repair, and upgrade in-space missions of all sizes, whether to extend the life of expensive facilities or to replace outworn or obsolete onboard systems as was demonstrated so effectively by the Hubble Space Telescope program. In parallel with these challenges to future major space astronomy missions, the capabilities of in-space robotic systems and the goals for human space flight in the 2020s and 2030s offer opportunities for achieving the most exciting science goals of the early 21st Century. In this paper, we summarize the history of concepts for human operations beyond the immediate vicinity of the Earth, the importance of very large apertures for scientific discovery, and current capabilities and future developments in robot- and astronaut-enabled servicing and assembly.

Hubble Space Telescope↗

NASA NTRS2015

A Future Large-Aperture UVOIR Space Observatory: Reference Designs

Our joint NASA GSFC/JPL/MSFC/STScI study team has used community-provided science goals to derive mission needs, requirements, and candidate mission architectures for a future large-aperture, non-cryogenic UVOIR space observatory. We describe the feasibility assessment of system thermal and dynamic stability for supporting coronagraphy. The observatory is in a Sun-Earth L2 orbit providing a stable thermal environment and excellent field of regard. Reference designs include a 36-segment 9.2 m aperture telescope that stows within a five meter diameter launch vehicle fairing. Performance needs developed under the study are traceable to a variety of reference designs including options for a monolithic primary mirror.

exo-planets↗

NASA NTRSDate not supplied

Transforming Science Prioritization Processes Using Artificial Intelligence

Artificial Intelligence (AI) and Machine Learning (ML) have potential to augment significantly the current labor-intensive processes of science prioritization, specifically by the National Academies’ Decadal Survey on behalf of NASA and NSF. Here we summarize what we believe to be the first exploratory demonstration-of-concept results from an application of AI/ML to Survey science prioritization. Specifically, we applied Latent Dirichlet Allocation (LDA) and Natural Language Processing (NLP) to reveal trends in published astrophysics research that may indicate science priorities and which could be applied to strategic planning. For the purpose of the work that we summarize here, AI/ML is able to analyze – that is, to “understand,” in a manner of speaking – a vast amount of text to reveal complex relationships among research topics, including the growth or decline of science community activities in those topics over time. We trained ourselves and AI/ML algorithms by using ~400,000 abstracts in the period 1998 to 2010 to “forecast” the Academies’ Astro2010 recommendations and compare with the solicited white papers. Comparing our results with actual Astro2010 recommendations allowed us to identify candidate metrics that better predicted the actual results of the Survey. We found, for example, that Compound Annual Growth Rate (CAGR) of papers published in a topic area is a good proxy measure for importance of this topic area of research. With this training complete, we identified candidate astrophysics astrophysics science priorities for the 2021+ period using the research during 2007 - 2019 . We conclude that appropriate application of AI can potentially significantly reduce the current workload of the Decadal Survey processes and reveal otherwise unrecognized characteristics in the body of astronomical research. We emphasize throughout the exploratory nature of our work, encouraging colleagues to pursue promising results further. Our most critical governing assumption was that increased (or decreased) research activity can be used to identify scientific or technology topic areas worthy of increased (or decreased) future emphasis. We discuss advantages, limitations, and recognize the “black box” nature of our technique. We note ethics issues associated, for example, with using AI/ML to reveal “hidden” meanings and biases in published work. Furthermore, inevitable improvements in AI may soon enable widespread and welcome identification of and advocacy for science and technology priorities by disparate and diverse groups and organizations. Consequently, we continue to urge a near-term, in-depth evaluation of appropriate applications of AI, including implications and consequences, as well as support for multiple follow-on assessments, of which ours is only a beginning.

Artificial Intelligence↗