Data and artificial intelligence: the fuel behind space discovery

As maintaining leadership in space is a primary goal, particularly across the United States Space Force, NASA and other federal agencies, the U.S. remains focused on space exploration as a critical domain for missions, science investigation and national security.One way to sustain a leading position is by achieving data dominance, leveraging artificial intelligence (AI) tools such as machine learning (ML) algorithms onboard space missions to facilitate and enable real-time decision-making.These technologies can be used for engineering analysis and opportunistic science measurements. Data analytics and ML algorithms can also optimize resources, prioritize data to send back to Earth and identify patterns promptly.The goal of these strategies is to develop spacecraft capable of real-time situational analysis, enabling them to make autonomous decisions and further optimize space missions. Developing and achieving autonomous science and exploration spacecraft requires a fundamental shift in the approach to space exploration. And beyond that, space organizations must navigate several technical considerations to successfully implement this vision, including environmental constraints and adapting solutions for specific mission objectives.Data-fueled space missionsData analytics and ML algorithms are a driving force behind space missions. They can optimize resources, such as fuel and energy usage, assist in planning and scheduling processes of observation strategies for in-space telescopes and support the prioritization of the data to first send back to Earth.While ML algorithms on Earth can help identify patterns or correlations in massive datasets promptly, (Earth science mission teams are not scaling with the large amounts of data on hand) ML models onboard a spacecraft could help make missions even more efficient. A ML-enabled spacecraft on a life detection mission, for example, could analyze the data it gathers, identify the organic compound signatures in real-time and in the end prioritize other sampling locations without ground-in-the-loop intervention.A long-term goal of this approach would be to have in situ analysis with spacecraft operating and analyzing in real-time, making autonomous decisions that prioritize scientific goals without depending solely on Earth-based operations.Imagine a spacecraft on Saturn’s moon Enceladus, collecting data from the plumes being ejected at the south pole, then analyzing the data onboard and reprioritizing other operations without having to wait for a transmission from scientists on Earth — this could all be based on data collections using onboard AI-based models, software analysis and edge computing.While this onboard implementation would help make decisions in situ to optimize resources and scientific returns, several hurdles must be overcome to see this vision through for a more efficient future.Space exploration: data and challengesOne primary challenge in implementing AI-enhanced spacecraft is the limited onboard computing power, constrained by strict power and weight limitations that make distributing power among communication, mobility, running onboard experiments, computing and much more a difficult balancing act. Also, the “space-proofing” process — including thermal control, radiation shielding, and protection from meteoric and orbital debris complicates hardware development — and raises costs.Bandwidth limitations and communication delays present another challenge in data transmission. Moreover, when the planetary target is not in Earth’s direct line of sight, communication becomes entirely impossible with traditional spacecraft.Of all things, trusting the AI-driven strategy is a major challenge, particularly for life-detection missions. ML models are often seen as “black boxes,” making it difficult for scientists to fully trust the appropriate algorithms’ outcomes.Embracing AI and ML in space exploration inspires true optimism and curiosity, and scientific discovery. To achieve this future, the industry must prioritize solutions like the development of hardware that allows real-time AI computations, advancement of data transmission tools and continuing investments in the Deep Space Network (DSN) to further enhance the efficiency of data transmission for missions.One of the main difficulties is that the space industry must show that new hardware is truly impactful. Space missions rely on flight heritage — to prove that this new hardware works, industry needs a process to test and demonstrate tech that will have the new hardware onboard and show the success of the mission.The testing of data-driven-enabled data prioritization must occur too — currently, space missions are designed to collect the amount of data that can be sent back to Earth. With AI-enhanced spacecraft, a fundamental shift can occur, as the ability to transmit data back to Earth will no longer be the significant bottleneck as data prioritization could be implemented. The end goal is to collect as much data as the instrument onboard can, then have a smart algorithm onboard to prioritize the “most interesting” data to send back to Earth. More opportunities to test algorithms onboard during simulation and on low-risk science missions will bolster solutions’ technology readiness level.Space missions have relied on pre-programmed instructions and extensive ground-in-the-loop analysis. This approach is a total change to the paradigm of space research and development, but becoming data-driven in space is necessary for success, especially when exploring targets farther away in our solar system.Data-driven futurePrivate sector collaboration is key to helping transform space missions — involving expert perspectives would provide innovative solutions and strategies to help space teams develop spacecraft that can process, transport and interpret critical data.This collaboration could be leveraged to enable real-time AI computations directly onboard the spacecraft, while also enhancing data processing pipelines for operations teams, from data collection to prioritization. Moreover, this collaboration could help accelerate the development of AI processors for space applications, ensuring they remain radiation-proof and extremely power-efficient. These collaborations are already occurring, such as NASA and IBM’s AI partnership.Space agencies and the overall space industry must also implement an intelligent data collection solution, or data processing pipeline, that includes data collection, data labeling, analysis and then managing it appropriately so teams can access and make decisions in near real-time for mission-critical operations.Data can also be leveraged in ML models for various applications, including anomaly detection, hardware failure prediction, and science data analysis. This can be done through training models on Earth and then fine-tuning them for specific space mission targets.Using big data more effectively will also allow teams on Earth to develop visualization and simulation tools. These could involve digital twin investigations — virtual replicas of spacecraft and planetary environments to simulate missions and test algorithms before deployment — leading to smarter, more decisive actions for mission operations.Cultivating a data-driven environment is not just about implementing next-gen tools, but it is a catalyst for the next frontier of space discovery and exploration.Advancing AI-enhanced space exploration requires interdisciplinary collaboration (among AI experts, software engineers, astrobiologists and so forth) ensuring tools and models are adaptable and scalable. As computing power and onboard capabilities improve, data-intensive tasks (such as spectral analysis through ML) could increasingly be performed in space, to enable real-time insights and collaborative science discoveries, unlocking the next frontier of space exploration.Victoria Da Poian is Lead Data Scientist at Tyto Athene.Eric Lyness is Lead Software Engineer at Tyto Athene. Both Victoria and Eric support NASA’s Goddard Space Flight Center and they both work for the Planetary Environments Laboratory within GSFC and on ExoMars and Dragonfly missions.SpaceNews is committed to publishing our community’s diverse perspectives. Whether you’re an academic, executive, engineer or even just a concerned citizen of the cosmos, send your arguments and viewpoints to [email protected] to be considered for publication online or in our next magazine. The perspectives shared in these op-eds are solely those of the authors.