Data Management and Retrieval for an Atmospheric Probe Mission to Venus
Data Management and Retrieval for an Atmospheric Probe Mission to Venus
After nearly 40 years without a dedicated U.S. mission to Venus, the Rocket Lab Mission to Venus is planning to launch a small probe to analyze the composition of Venus’ cloud layers. As the probe descends through the atmosphere, it will spend around five minutes in the cloud deck, from 66 km to 48 km above the surface, and roughly 20 minutes total in the atmosphere [French et al., 2022]. The probe’s primary scientific instrument, the Autofluorescence Nephelometer (AFN), will gather data by measuring the light scattering off particles, providing insight into their chemical composition based on refractive index and particle size [Baumgardner et al., 2022]. Unfortunately, the natural phenomena described by Mie scattering [Mie, 1908], the physics theory underpinning the AFN, holds that light scattering for a small solid angle is fundamentally degenerate: different combinations of refractive index and particle size can lead to identical light scattering. This degeneracy limits scientists’ ability to uniquely determine physical parameters of interest, leading some previous authors to rely upon helpful, but perhaps limiting, assumptions that mitigate this degeneracy. Complicating matters still further, the probe’s communication with Earth is subject to a strict data budget, limiting the amount of AFN measurements that may be used for analysis to begin with.
I addressed two important problems associated with the Rocket Lab Mission to Venus:
1) how to mitigate the light scattering degeneracy with minimal assumptions and
2) how to transmit valuable information within the limited data budget.
To address the first problem, I introduce a data retrieval algorithm, based upon Bayesian statistical inference [Lindley, 1965], which combines a physical model of the instrument and a prior probability distribution describing each physical property.
To address the second problem, I propose a data strategy for limited data missions like the Rocket Lab Mission to Venus. The method developed in this work relies upon Gaussian Mixture Models, which can efficiently represent multiple measurements as 3 a probability distribution.
[I will link my thesis document when it is publicly available. I am currently writing the two journal articles that will come out of my thesis work.]
https://venuscloudlife.com/small-mission/