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Retrieving the physical parameters from spectroscopic observations of exoplanets is key to understanding their atmospheric properties. Exoplanetary atmospheric retrievals are usually based on approximate Bayesian inference and rely on sampling-based approaches like MCMC and Nested sampling to compute the parameter posterior distributions. Accurate or repeated retrievals, however, can result in very long computation times due to the sequential nature of sampling-based algorithms. In this era of JWST where we are facing a huge influx of observational data, it is imperative to reduce the retrieval time without compromising on the accuracy of the posteriors. I am interested in applying simulation based inference (SBI) using machine learning to amortize the retrieval process in order to reduce the inference time. 
Source: Wikipedia
Neural posterior estimation (NPE) for exoplanetary atmospheric retrieval

Sequential nature of sampling-based algorithms lead to very long computation times. We aim to amortize exoplanetary atmospheric retrieval using neural posterior estimation (NPE), a simulation-based inference algorithm based on variational inference and normalizing flows. In this way, we aim,


(i)  to strongly reduce inference time,

(ii) to scale inference to complex simulation models with many nuisance parameters or intractable likelihood functions, and

(iii) to enable the statistical validation of the inference results.


Amortization of the posterior inference makes repeated inference on several observations computationally inexpensive since it does not require on-the-fly simulations, making the retrieval efficient, scalable, and testable.

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arXiv preprint arXiv:2301.06575


1. Vasist, M., Rozet, F., Absil, O., Mollière, P., Nasedkin, E., & Louppe, G. (2023). Neural posterior estimation for exoplanetary atmospheric retrieval. arXiv preprint arXiv:2301.06575.
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