Towards Probabilistic Modelling of Venus Express/SOIR’s Acousto-Optic Tunable Filter

The SOIR (Solar Occultation in InfraRed) spectrometer onboard Venus Express (VEx) collected high-resolution solar occultation spectra of the Venusian atmosphere from 2006 to 2014. SOIR combines an echelle grating with an Acousto-Optic Tunable Filter (AOTF), the latter enabling the rapid selection of diffraction orders across the 2.2 to 4.3 microns spectral range [1]. Accurate characterisation of the AOTF transfer function (TF) is essential as the TF directly impacts the spectral calibration and the accuracy of the atmospheric retrievals.  

 

The SOIR AOTF TF was characterised in-flight using dedicated calibration observations of the Sun, called miniscans, during which the AOTF driving radio frequency was stepped across defined frequency intervals centred on deep solar Fraunhofer lines. Because these solar lines are significantly narrower than the AOTF bandwidth, they provide an effective probe of the instrument response function. Early calibration work used 42 miniscans and approximately 250 solar lines distributed across the spectral range to derive the AOTF’s tuning relation and bandpass [2]. A subsequent study introduced a more advanced reconstruction approach based on the combined analysis of multiple solar lines and demonstrated that the SOIR AOTF TF could be approximated using a sum of five sinc² functions with frequency-dependent coefficients [3]. A later investigation extended the calibration analysis to nearly 300 miniscans available at the time [4]. By the end of the mission in 2014, however, the complete SOIR archive contained nearly 500 miniscans.

 

Although the analytical multi-sinc² model reproduces the main lobe structure of the AOTF TF, discrepancies remain, particularly in the sidelobes and in spectral regions affected by order overlap. In addition, the partial exploitation of the complete SOIR miniscan archive leaves open questions regarding the variability of the TF across the spectral domain, its long-term evolution throughout the mission lifetime, and potential instrumental dependencies not yet detectable in previous analyses.

 

This work introduces a probabilistic machine learning framework for the SOIR AOTF TF characterisation, with a particular focus on Gaussian Processes (GPs). GPs provide a flexible non-parametric framework well suited to heterogeneous, medium-sized calibration datasets, while naturally incorporating uncertainty estimation. The GP covariance structure can be physically informed, while the existing analytical model may be incorporated as a prior mean function, ensuring the framework remains grounded in instrument physics [5]. The objective is to investigate whether GP-based modelling can improve reconstruction of the TF shape, especially in the sidelobe regions, while also enabling the exploration of dependencies on wavelength, instrumental temperature, and observation epoch.

 

The ongoing study aims to extend the analysis to the complete SOIR miniscan archive acquired over the full operational lifetime of the instrument. Preliminary developments of the GP framework and data preparation pipeline are underway. Initial results from the first application of GP-based AOTF TF modelling to SOIR calibration data will be presented.

 

 

[1]        Nevejans, D., Neefs, E., van Ransbeeck, E., Berkenbosch, S., Clairquin, R., de Vos, L., Moelans, W., Glorieux, S., Baeke, A., Korablev, O., Vinogradov, I., Kalinnikov, Y., Bach, B., Dubois, J-P., and Villard, E., “Compact high-resolution spaceborne echelle grating spectrometer with acousto-optical tunable filter based order sorting for the infrared domain from 2.2 to 4.3 μm”, Applied Optics, vol. 45, no. 21, pp. 5191-5206, 2006.

[2]        Mahieux, A., Berkenbosch, S., Clairquin, R., Fussen, D., Mateshvili, N., Neefs, E., Nevejans, D., Ristic, B., Vandaele, A. C., Wilquet, V., Belyaev, D., Fedorova, A., Korablev, O., Villard, E., Montmessin, F., and Bertaux, J-L., “In-flight performance and calibration of SPICAV SOIR onboard Venus Express”, Applied Optics, vol. 47, no. 13, pp. 2252-2265, 2008.

[3]        Mahieux, A., Wilquet, V., Drummond, R., Belyaev, D., Federova, A., and Vandaele, A. C., “A new method for determining the transfer function of an Acousto optical tunable filter”, Optics Express, vol. 17, no. 3, p. 2005, 2009.

[4]        Mahieux, A., “Inversion of infrared spectra recorded by the SOIR instrument on board Venus Express”, PhD Thesis, BIRA-IASB & ULB, Belgium, 2011.

[5]        Rasmussen, C. E. and Williams, C. K. I., Gaussian Processes for Machine Learning, MIT Press, 2006.