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Students from AAU contribute to NASA's Mars Mission
![Students from AAU contribute to NASA's Mars Mission](https://prod-aaudxp-cms-001-app.azurewebsites.net/media/iyieknb1/motormus-x-curiosity.png?width=2)
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Students from AAU contribute to NASA's Mars Mission
![Students from AAU contribute to NASA's Mars Mission](https://prod-aaudxp-cms-001-app.azurewebsites.net/media/iyieknb1/motormus-x-curiosity.png?width=2)
Students from AAU contribute to NASA's Mars Mission
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By Mette Hjorth Rasmussen and Trine Jensen, AAU Communication & Public Affairs
Christian Bager Bach Houmann, Ivik Lau Dalgas Hostrup and Patrick Frostholm Østergaard, who are students from the master's program in Software at Aalborg University, have been given an unique opportunity to collaborate with NASA's Mars Science Laboratory mission. Through their project work, they have contributed important knowledge and innovative solutions which help NASA in the search for life on Mars.
The students' work has helped to improve the analysis of data from NASA's ChemCam and SuperCam instruments, which are mounted on the Mars rovers Curiosity and Perseverance. They have developed methods to combine different models and techniques that efficiently process and analyze data. Their code has automated processes that were previously manual and time-consuming, and they have added new functionality that helps researchers to better understand the complex data.
They have also developed a catalog that makes it easy for researchers to identify the most effective methods for finding elements in Martian rocks and soils. In addition, they have created a tool that makes it possible to carry out large-scale experiments with different models and techniques. This tool was crucial to their own results and has attracted great interest among the scientists on NASA's team.
The students were invited to present their methods to the calibration team, which analyzes and interprets data from Mars. This team works to improve our understanding of the geological composition of Mars and contributes to the mission's overall goal of finding signs of life on the planet.
Working so close to a mission that could potentially yield new knowledge about life on Mars has been a dream come true for the three students. For over a year, the students have had a close collaboration with their supervisors Daniele Dell'Aglio and Juan Manuel Rodriguez from the Department of Computer Science and have had regularly meetings with their co-supervisor Jens Frydenvang from the University of Copenhagen as well as researchers from the USGS, who are all significant members of NASA's missions, and has been able to provide a unique insight into the missions' development and future goals.
- It has been incredible to be so close to the missions. Maybe we won't become astronauts, but our contribution can help us learn more about what's on Mars, says one of the students.
The students' results are so promising that the researchers on NASA's team have already implemented them in their analysis work. They have also received a certificate from two of the primary researchers on the ChemCam and SuperCam teams, confirming the students' important contribution to the mission.
The three students' contribution and collaboration with NASA has not only been a learning experience, but also an inspiring journey that has the potential to shape their future careers in software and space. The project collaboration with NASA has strengthened their ambitions to work with something that makes a real difference. As software engineers, they have the opportunity to work broadly across fields and influence many different areas, including space exploration.
Facts
In their thesis project, the students have improved the analysis method for data from laser-induced breakdown spectroscopy (LIBS). Their work makes it possible to determine the amount and composition of various minerals and oxides in geological samples with greater precision. They use advanced machine learning techniques and a combination of different regression models to solve problems that have many variables or to identify relationships between variables from a limited amount of data.