NASA to use AI to help search for life on Mars and beyond

US space agency NASA has stepped closer to allowing remote onboard computers to direct the search for life on other planets. Scientists from the NASA Goddard Space Flight Center (GSFC) have announced first results from new intelligent systems, to be installed in space probes, capable of identifying geochemical signatures of life from rock samples.

Allowing these intelligent systems to choose both what to analyse and what to tell us back on Earth will overcome severe limits on how information is transmitted over huge distances in the search for life from distant planets. The systems will debut on the 2022/23 ExoMars mission, before fuller implementation on more distant bodies in the solar system.

Presenting the work at the Goldschmidt Geochemistry Conference, lead researcher Victoria Da Poiana (GSFC) said, “This is a visionary step in space exploration. It means that over time we’ll have moved from the idea that humans are involved with nearly everything in space, to the idea that computers are equipped with intelligent systems, and they are trained to make some decisions and are able to transmit in priority the most interesting or time-critical information.”

Eric Lyness, software lead in the Planetary Environments Lab at NASA GSFC, emphasised the need to have smart instruments for planetary exploration.

“It costs a lot of time and money to send the data back to Earth which means scientists can’t run as many experiments or analyse as many samples as they would like. By using AI to do an initial analysis of the data after it is collected but before it is sent back to Earth, NASA can optimise what we receive, which greatly increases the scientific value of space missions.”

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Mars Organic Molecule Analyser (MOMA)

Victoria Da Poian and Eric Lyness have trained artificial intelligence systems to analyse hundreds of rock samples and thousands of experimental spectra from the Mars Organic Molecule Analyser (MOMA), an instrument that will land on Mars within the ExoMars Rosalind Franklin Rover in 2023.

MOMA is a state-of-the-art mass spectrometer-based instrument, capable of analysing and identifying organic molecules in rocks samples. It will search for past or present life on the Martian surface and subsurface through analysis of rock samples. The system to be sent to Mars will still transmit most data back to Earth, but later systems for the outer solar system will be given autonomy to decide what information to return to Earth.

First results show that when the system’s neural network algorithm processes a spectrum from an unknown compound, this can be categorized with up to 94% accuracy and matched to previously seen samples with 87% accuracy. This will be further refined until being incorporated into the 2023 mission.

Victoria Da Poian continued: “What we get from these unmanned missions is data, lots of it; and sending data over hundreds of millions of kilometres can be very challenging in different environments and extremely expensive; in other words, bandwidth is limited. We need to prioritize the volume of data we send back to Earth, but we also need to ensure that in doing that we don’t throw out vital information. This has led us to begin to develop smart algorithms which can for now help the scientists with their analysis of thesample and their decision-making process regarding subsequent operations, and as a longer-term objective, algorithms that will analyse the data itself, will adjust and tune the instruments to run next operations without the ground-in-the-loop, and will transmit home only the most interesting data.”

The team used the raw data from initial laboratory tests with an Earth-based MOMA instrument to train computers to recognize familiar patterns. When new raw data is received, the software tells the scientists what previously encountered samples match this new data.

The researchers note that data is expensive to send back from Mars, and gets more expensive as landers get further from Earth. “Data from a rover on Mars can cost as much as 100,000 times as much as data on your cell phone, so we need to make those bits as scientifically valuable as possible,” said EricLyness.

(With inputs from agency) (Image Twitter/NASA)

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