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Astrophysics, Instrumentation and Methods for Astrophysics, astro-ph.IM, Astrophysics, Earth and Planetary Astrophysics, astro-ph.EP,Computer Science, Learning, cs.LG
Abstract:
Inferring atmospheric properties of exoplanets from observed spectra is key
to understanding their formation, evolution, and habitability. Since
traditional Bayesian approaches to atmospheric retrieval (e.g., nested
sampling) are computationally expensive, a growing number of machine learning
(ML) methods such as neural posterior estimation (NPE) have been proposed. We
seek to make ML-based atmospheric retrieval (1) more reliable and accurate with
verified results, and (2) more flexible with respect to the underlying neural
networks and the choice of the assumed noise models. First, we adopt flow
matching posterior estimation (FMPE) as a new ML approach to atmospheric
retrieval. FMPE maintains many advantages of NPE, but provides greater
architectural flexibility and scalability. Second, we use importance sampling
(IS) to verify and correct ML results, and to compute an estimate of the
Bayesian evidence. Third, we condition our ML models on the assumed noise level
of a spectrum (i.e., error bars), thus making them adaptable to different noise
models. Both our noise level-conditional FMPE and NPE models perform on par
with nested sampling across a range of noise levels when tested on simulated
data. FMPE trains about 3 times faster than NPE and yields higher IS
efficiencies. IS successfully corrects inaccurate ML results, identifies model
failures via low efficiencies, and provides accurate estimates of the Bayesian
evidence. FMPE is a powerful alternative to NPE for fast, amortized, and
parallelizable atmospheric retrieval. IS can verify results, thus helping to
build confidence in ML-based approaches, while also facilitating model
comparison via the evidence ratio. Noise level conditioning allows design
studies for future instruments to be scaled up, for example, in terms of the
range of signal-to-noise ratios.
