For several years she led the Spaceship EAC initiative, a student-centred team working on the technological challenges of future missions to the Moon. She was then assigned to the role of ESA reserve astronaut, which allowed her to earn her initial qualifications in EVA and robotics, as well as certification as flight engineer of the Russian spacecraft Soyuz.Īfter completing post-flight tasks for her Futura Mission, Samantha was given technical and management duties at the European Astronaut Centre, which included serving on technical evaluation boards for exploration-related projects. She joined ESA in September 2009, and completed her basic astronaut training in November 2010. Samantha was selected as an ESA astronaut in May 2009. Upon her return to Italy, she was assigned to fly the AM-X ground attack fighter at the 51st Bomber Wing in Istrana.
Following her graduation in 2005, she attended the Euro-NATO Joint Jet Pilot Training program at Sheppard Air Force Base in the United States, where she earned her fighter pilot wings in 2006. She was admitted to the Air Force Academy as an officer candidate, and served as class leader for four years. However, the cloud presented a new challenge: navigating security protocols.In 2001, Samantha joined the Italian Air Force. Leveraging a cloud computing cluster would seem to be a natural solution with plenty of storage and compute. But because of the largeness of the data sets, Kirk and Attié were forced to leverage external storage via USB, creating significant I/O limitations. To counter the delays in data movement and long processing times, the team attempted to leverage a desktop workstation designed for general GPU computing with just under 1 TB of SATA-based storage, 32GB of RAM, and one NVIDIA 1080 TI GPU. Delays and wait time were becoming unacceptable. Additionally, for the mission monitoring and analyzing the surface of the sun, 1.5 TBs of new data was collected daily. Between the amount of data movement that was required and the limited computing capabilities, it would take up to a few years for the team to see any results. For this, the team collaborates with Benoit Tremblay, scientist at the National Solar Observatory (NSO), to use a deep neural network trained on simulation data to understand plasma flows and their relation to magnetic fields.ĭue to cloud computing and network limitations, pulling out complete data archives in the 100s of TBs simply was not feasible. In addition to monitoring active magnetic regions of the sun, the scientists also focus on mapping the flow of plasma on the sun. And while the unimaginable temperatures and heat that a solar flare lets off are not necessarily impactful to the earth or its atmosphere, electromagnetic radiation and energetic particles can enter and alter the Earth’s upper atmosphere, disrupting signal transmission from GPS satellites that orbit the earth. They leverage AI to map localized plasma motions on the sun to classify regions based on magnetic activity, enabling them to focus on areas prone to solar flares. The first aspect of their mission is classification. This presents opportunities for AI to be utilized on board satellites, rovers, planes, or balloons.ĮSG spoke with Michael Kirk (ASTRA llc.), research astrophysicist, and Raphael Attié (George Mason University), solar astronomer at NASA’s Goddard Space Flight Center to understand their mission of monitoring the sun’s atmosphere and the type of technology they rely on to complete this ongoing mission using AI.
In fact, it’s impossible to analyze all of this data simply due to its size and the limited computing capabilities on a spacecraft hundreds of miles away, all but forcing NASA to prioritize sending certain types of telemetry data down to earth, which vary depending on the mission.
The amount of data collected on a spacecraft to finetune humanity’s understanding of solar physics and derived models is massive, amounting to tens, if not hundreds of TBs per day. NASA is just scratching the surface in its use of AI for mission enhancement and enablement. Based on collected data and scientific models that describe a physical process, AI can be exploited to make scientific models more robust and reliable by enabling wider parameter exploration. At NASA, AI is leveraged to monitor regions of space and automatically detect if something interesting just happened or is going to happen. AI is primarily used today to help with the detection of “things” and model enhancement.
Based on the amount of data collected for any given NASA mission or project, it’s no surprise that AI is an enabling technology.
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