Development Of A Data Retrieval Algorithm For Venus Nephelometry In Preparation For The Rocket Lab Mission To Venus

[Science Direct] The Rocket Lab Mission to Venus (RLMV) will carry a single primary instrument, the Autofluorescence Nephelometer (AFN), to interpret atmospheric particle properties from light-scattering measurements. This process is fundamentally limited by degeneracy, i.e., multiple combinations of particle size and refractive index can produce indistinguishable intensities.

The broad degeneracy of scattered polarized intensities for the single wavelength and small collection angles of the AFN, has motivated us to explore a Bayesian retrieval framework that improves the interpretation of the light scattered by individual particles. Our approach narrows the parameter space in a statistically rigorous way with minimal bias and without excluding information.

One primary advantage is the ability to capture the instrument noise model in the likelihood function, enabling posterior probabilities to identify and distinguish disjoint regions of particle size and refractive index consistent with the data. Crucially, the Bayesian framework combines the effects of both instrument noise and parameter-space degeneracy, outperforming deterministic grid searches by providing posterior probability distributions rather than only single best-fit solutions.

We demonstrate that this method yields accurate multimodal distributions of particle properties from simulated Venus-like data, with quantified uncertainties that reflect both instrument noise and degeneracy. The RLMV probe will use a direct-to-Earth communications link, limiting total data transmission. Rather than onboard inference, which would produce complex posterior distributions, we propose to return compact two-dimensional histograms of parallel and perpendicular intensities as a function of altitude interval.

The Bayesian method framework significantly improves our ability to characterize aerosol physical properties from limited datasets, with direct application to upcoming Venus missions and broader implications for atmospheric retrievals across planetary science.