Research project P4S/251/ADAMS (Research action P4S)
Mars’ atmosphere is composed of 95% carbon dioxide and around 4% molecular nitrogen and argon. However, many trace gases that have an important impact on the atmosphere’s chemistry are still to be assessed. We can now enter the details and quantify the trace species down to the part per billion, thanks to the NOMAD instrument, the BIRA-IASB suite of spectrometers on board ESA’s Trace Gas Orbiter. The high spectral resolution of NOMAD and its various viewing geometries enable us to capture the distribution of trace species better. The NOMAD instrument can measure, among other phenomena, atmospheric absorption via solar occultation. Those measurements provide an unrivalled method for inferring the presence of trace gases, owing to the high intensity of the solar signal and the rapid acquisition of measurements by NOMAD, which yields a very fine vertical resolution. Many key species in the Martian atmosphere exhibit infrared absorption features. In this project, we will target molecules predicted by chemical models to be present in the Martian atmosphere but not regularly observed.
The first goal of this project is to develop an upper-detection scheme to be integrated into the NOMAD science pipeline recently initiated by the Planetary Atmospheres group. We need to define the optimal spectral ranges for studying each molecular species. This first part is critical and timely, as we now have a better understanding of the instrument's function and measurement uncertainties. This work will confirm their accuracy and, if necessary, adjust them, thereby providing essential feedback. We will need to best fit the carbon dioxide and water spectral structures across the full spectral range of NOMAD-SO. These have a signal that is several times larger than that of the targeted species. The retrievals will be automated, and reliable criteria for upper-limit calculations will be established. The second goal is to develop a new method for inferring spectral signatures using unsupervised machine learning tools. Machine learning has made significant advances in the last decade, and well-established methods are now easy to implement thanks to their widespread documentation and availability of algorithms. Many interesting methods can be investigated to infer the tiny presence of spectral features in data. The third goal is to provide a consistent set of upper limits or quantification of the amount of the targeted species and to examine how these results constrain the main chemical cycles.
