Dr Peter Fawdon

ESA have allocated funds to maintain the employment of key ExoMars Rover team members who currently have no permanent academic position. Peter Fawdon will the UK PanCam instruments nominated recipient of this funding. There is a 2+1 years funding period and the funding can be reduced if and when Peter wins additional funds from other sources.

This fellowship proposes to explore locations most conducive to sustained habitability on Mars and to support the Rosalind Franklin (RF) rover mission, capable of identifying extant life and ancient biosignatures. This addresses the ExoMars mission goal: “to find evidence of past or present life on Mars” and asks a question central to human exploration; have we always been alone? RF will identify evidence of life by detecting complex organic molecules from samples collected in the landing site; Oxia Planum. This will only be possible, and the result be meaningful, if we understand where biomarkers may have formed, how they have been deposited in Oxia Planum, and if they have been preserved for RF to detect. I have been involved in understanding the geological context of Oxia Planum since the start of landing site selection. By drawing on this experience, and my geological remote sensing skills, I will continue to improve our understanding of Oxia Planum by: (1) investigating the sediment fans in Oxia Planum, which potentially deposited biomarkers; (2) observing impact craters from orbit and from the rover to determine when preservation potential has been risked by erosion, and; (3) investigating the relative timing of volcanic activity and ancient lakes to consider biomarker formation. This research will maximise the impact of the UK investment in the ExoMars mission. Through publishing my findings, I will provide insight into the geological evolution of Oxia Planum and the history of Mars for the scientific community. These insights will contribute to mission preparation through my participation in the PanCam team and Rover Science Operation Working Group (RSOWG) and by publishing the first geological maps of the landing site I will contribute to scientific decisions made during the mission. I will use this fellowship to build an independent research career, to continue to support students, and to inspire the general public with the excitement of exploration through the ExoMars mission.

The ESA ExoMars Trace Gas Orbiter spacecraft has been in orbit around Mars since 2016. It carries a variety of instruments including CaSSIS, a high resolution camera with 4 bands (i.e. it can see in 4-'colours') and stereo capability to measure the shape of the surface. CaSSIS adds a new dimension to the already enormous archive of Mars images generated by previous NASA and ESA spacecraft. In fact, the sheer scale of the Mars imaging dataset has made it difficult to use - no one person, or even a group of people, can look at every image in detail. For this reason, this project aims to use new Deep Learning (DL, a type of 'AI')techniques to provide a means of analysing these huge datasets; in essence, doing some of the 'leg work' before humans then check and correct errors in the the automated results and proceed with in-depth analysis. A key feature of visual DL type approaches is that they use "training data" - usually digitised records of '"truth" made by trained humans - to learn from them what is right and wrong when identifying features or textures. With a large, well-designed training set, DL can rapidly analyse huge numbers of images, and produce similar outputs to human operators. This project covers three research areas, each using DL techniques to analyse large datasets. Each builds on human mapping and digitising results our group has already done to support the ESA ExoMars Rover mission (delayed from 2022to 2028) and also makes use of our ongoing CaSSIS team membership, allowing new CaSSIS images to be rapidly targeted and acquired. The three areas are: (i) Greatly expanding the current geological map of the ExoMars Rover landing site. This needs to be done to prepare for the 2028 launch, as the area covered by the existing 2022 map is unlikely to be suitable for the 2028 mission. The problem is that the image data available are of such high resolution (up to 25 cm/pixel) that the current map took years to produce. Manually extending the map to an even larger areas is unrealistic. However, we will use our existing map to train a DL model to identify the surface textures and colours (using CaSSIS) of the various geological units, thus greatly improving the speed we can do the new mapping. (ii) Measuring the orientations of large wind-blown ripples (known as Transverse Aeolian Ridges, or TARs) on a global scale, and comparing these with wind directions generated by advanced computer models of the martian atmosphere. Ithas been observed that TARs are generally immobile, yet they must once have been active as they are very common on Mars. As we also know that Mars' recent climate has changed both systematically and in repeatable cycles over the last 20 years, we can use different climate models setups to see which climate period best matches the measured TAR orientations - and thus when they were active. We will use a DL 'TAR-finder' to make the orientation measurements automatically (we already have a working prototype) making use of CaSSIS and other high resolution data to train the models. (iii) Mapping the population of km-scale mounds (small hills) across the 'dichotomy' region of Mars (the boundary between the ancient southern highlands and the younger northern lowlands). In a previous study, we mapped out ~15,000 mounds manually, yet perhaps ten times this number are present along the dichotomy. The mounds are very important, as our recent study of just one section of the dichotomy shows that they represent ancient parts of the highlands that were almost obliterated by erosion, more than 3.7 billion years ago. We want to find out if this is true across the whole dichotomy region, so we will use DL to map the mounds automatically, and then use CaSSIS and other data to test whether they are also outliers from the highlands. The outcome will be new Mars science results, plus a new 'blueprint' for applying DL to Mars data in preparation for the start of the ExoMars Rover mission in 2028.

A small project to "label" (identify) areas of the proposed ExoMars landing sites to support ESA machine learning technology development. The labelling “trains” the system to recognise different landform types.

This fellowship proposes to explore locations most conducive to sustained habitability on Mars and to support the Rosalind Franklin (RF) rover mission, capable of identifying extant life and ancient biosignatures. This addresses the ExoMars mission goal: “to find evidence of past or present life on Mars” and asks a question central to human exploration; have we always been alone? RF will identify evidence of life by detecting complex organic molecules from samples collected in the landing site; Oxia Planum. This will only be possible, and the result be meaningful, if we understand where biomarkers may have formed, how they have been deposited in Oxia Planum, and if they have been preserved for RF to detect. I have been involved in understanding the geological context of Oxia Planum since the start of landing site selection. By drawing on this experience, and my geological remote sensing skills, I will continue to improve our understanding of Oxia Planum by: (1) investigating the sediment fans in Oxia Planum, which potentially deposited biomarkers; (2) observing impact craters from orbit and from the rover to determine when preservation potential has been risked by erosion, and; (3) investigating the relative timing of volcanic activity and ancient lakes to consider biomarker formation. This research will maximise the impact of the UK investment in the ExoMars mission. Through publishing my findings, I will provide insight into the geological evolution of Oxia Planum and the history of Mars for the scientific community. These insights will contribute to mission preparation through my participation in the PanCam team and Rover Science Operation Working Group (RSOWG) and by publishing the first geological maps of the landing site I will contribute to scientific decisions made during the mission. I will use this fellowship to build an independent research career, to continue to support students, and to inspire the general public with the excitement of exploration through the ExoMars mission.