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| Natura: | Preprint |
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2024
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| Accesso online: | https://arxiv.org/abs/2410.16609 |
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| author | Shabram, Megan McClelland, Ryan Wu, John Venkataram, Hamsa Shwetha Segars, Heidi Dean, Bruce Ye, Christine Moin, Aquib Ansdell, Megan Moussa, Mark Rebbapragada, Umaa Valizadegan, Hamed Perini, Dominick Ko, Glenn Da Poian, Victoria Gharib-Nezhad, Sam Cataldo, Giuseppe |
| author_facet | Shabram, Megan McClelland, Ryan Wu, John Venkataram, Hamsa Shwetha Segars, Heidi Dean, Bruce Ye, Christine Moin, Aquib Ansdell, Megan Moussa, Mark Rebbapragada, Umaa Valizadegan, Hamed Perini, Dominick Ko, Glenn Da Poian, Victoria Gharib-Nezhad, Sam Cataldo, Giuseppe |
| contents | Here we present several use cases for using Generative AI (Gen AI) to improve systems engineering and cognitive knowledge management related to the future of astronomy from a culmination of working meetings and presentations as part of the Gen AI Task Group for the NASA Habitable Worlds Observatory (HWO) Science and Technology Architecture Review Team (START) AI/ML Working Group. Collectively, our group mission statement is "Where is the Human-in-the-loop as Gen AI systems become more powerful and autonomous?" with an emphasis on the ethical applications of Gen AI, guided by using these systems to remove drudgery from human work while simultaneously increasing opportunities for humans to experience more collective creativity and innovation. The HWO mission stands to benefit dramatically from generative models for different data types including text, time series/spectra, and image data. These cover a wide range of applications in science and engineering for HWO, including: mission development acceleration, data analysis and interpretation, enhancing imaging capabilities, anomaly detection, predictive modeling and simulation, data augmentation for machine learning, instrument calibration and optimization, public engagement and education, and assisting in mission planning. As an example, through sensitivity analysis of simulated exoplanet population science data sets of various generative model complexity, we can reverse engineer the measurement uncertainty requirements for HWO instruments to produce data that can constrain population models and thus inform HWO design requirements. This approach to HWO design is one example of a strategy that can ensure that HWO remains AI-ready. Through presenting herein a combination of visionary ideas balanced with grounded validated use case examples, we aim to support the development of a long-term strategy to keep HWO AI-ready as it moves forward. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_16609 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Generative AI for Overall Mission Effectiveness at the Habitable Worlds Observatory Shabram, Megan McClelland, Ryan Wu, John Venkataram, Hamsa Shwetha Segars, Heidi Dean, Bruce Ye, Christine Moin, Aquib Ansdell, Megan Moussa, Mark Rebbapragada, Umaa Valizadegan, Hamed Perini, Dominick Ko, Glenn Da Poian, Victoria Gharib-Nezhad, Sam Cataldo, Giuseppe Instrumentation and Methods for Astrophysics Here we present several use cases for using Generative AI (Gen AI) to improve systems engineering and cognitive knowledge management related to the future of astronomy from a culmination of working meetings and presentations as part of the Gen AI Task Group for the NASA Habitable Worlds Observatory (HWO) Science and Technology Architecture Review Team (START) AI/ML Working Group. Collectively, our group mission statement is "Where is the Human-in-the-loop as Gen AI systems become more powerful and autonomous?" with an emphasis on the ethical applications of Gen AI, guided by using these systems to remove drudgery from human work while simultaneously increasing opportunities for humans to experience more collective creativity and innovation. The HWO mission stands to benefit dramatically from generative models for different data types including text, time series/spectra, and image data. These cover a wide range of applications in science and engineering for HWO, including: mission development acceleration, data analysis and interpretation, enhancing imaging capabilities, anomaly detection, predictive modeling and simulation, data augmentation for machine learning, instrument calibration and optimization, public engagement and education, and assisting in mission planning. As an example, through sensitivity analysis of simulated exoplanet population science data sets of various generative model complexity, we can reverse engineer the measurement uncertainty requirements for HWO instruments to produce data that can constrain population models and thus inform HWO design requirements. This approach to HWO design is one example of a strategy that can ensure that HWO remains AI-ready. Through presenting herein a combination of visionary ideas balanced with grounded validated use case examples, we aim to support the development of a long-term strategy to keep HWO AI-ready as it moves forward. |
| title | Generative AI for Overall Mission Effectiveness at the Habitable Worlds Observatory |
| topic | Instrumentation and Methods for Astrophysics |
| url | https://arxiv.org/abs/2410.16609 |