arXiv subjects

Ben Riegler

Publications and source records attributed to Ben Riegler.

3 recordsLinked to original sources

arXiv2026

Constraining the lives and times of exoplanets through evolutionary Bayesian retrievals

Static retrieval frameworks are leading tools for interpreting exoplanet observations, yet time-independent modelling leaves them prone to degeneracy and unable to resolve exoplanets' histories. The compositions and structures of surveyed super-Earth and sub-Neptune sub-populations remain unclear, but are shaped by physics acting across Gyr timescales. Interpreting these planets as static non-evolving snapshots allows multiple degenerate scenarios to explain their observed properties. We develop a generalised parameter retrieval framework, built on asynchronous Bayesian optimisation to efficiently dispatch a multi-physics forward-model, resolving exoplanets' evolving properties from their initial magma ocean conditions to the present day. By building Bayesian retrievals into the PROTEUS framework, sensitive coupled interior-atmosphere interactions are naturally resolved and interpretations are constrained to physically permissible scenarios. We test evolutionary retrievals with three exoplanet prototypes: a young sub-Neptune, an older super-Earth, and a warm terrestrial planet - representative of the surveyed exoplanet population. Evolutionary retrieval jointly infers their mantle redox conditions, metallic core fractions, and early volatile inventories from spectroscopically accessible observables. Some scenarios remain subject to well-established degeneracies between core fractions and volatile budgets. Terrestrial-mass exoplanets benefit from strong observable-parameter correlations that lift these degeneracies; we recover post-formation volatile inventories with <20 percent error. Exoplanet science is primed for incoming JWST, PLATO, Roman, and ELT data - observations which necessitate careful interpretation. Adoption of time-evolved models lifts interpretive degeneracies, providing the means to understand the deep interiors and lifetime histories of worlds throughout our galaxy.

astro-ph.EP↗

arXiv2026

Gaussian Mean Field Variational Inference can Overestimate Predictive Variance

Mean Field Variational Inference (MFVI) is widely understood to underestimate posterior variance. By analysing conjugate Bayesian Linear Regression (BLR), we show that this characterization is incomplete: while MFVI underestimates the variance in parameter space, it can overestimate the predictive variance compared to the exact posterior. We show that if the MFVI posterior underestimates predictive variances in some directions, it necessarily overestimates them in others. Crucially, this overestimation occurs in directions where the training data concentrates. This leads to the surprising result that, for a test point drawn from the training distribution, MFVI's expected predictive variance exceeds that of the exact posterior. We demonstrate a pathological case of this effect, where the MFVI posterior fails to reduce predictive variance compared to the prior on in distribution data. We connect these results to the Cold Posterior Effect, arguing that varying the temperature can correct this overestimation, yielding predictions closer to those of the exact posterior. We validate our theory on synthetic and real-world regression tasks.

stat.ML↗

arXiv2026

Standard Acquisition Is Sufficient for Asynchronous Bayesian Optimization

Asynchronous Bayesian optimization is widely used for gradient-free optimization in domains with independent parallel experiments and varying evaluation times. Existing methods posit that standard acquisitions lead to redundant and repeated queries, proposing complex solutions to enforce diversity in queries. Challenging this fundamental premise, we show that methods, like the Upper Confidence Bound, can in fact achieve theoretical guarantees essentially equivalent to those of sequential Thompson sampling. A conceptual analysis of asynchronous Bayesian optimization reveals that existing works neglect intermediate posterior updates, which we find to be generally sufficient to avoid redundant queries. Further investigation shows that by penalizing busy locations, diversity-enforcing methods can over-explore in asynchronous settings, reducing their performance. Our extensive experiments demonstrate that simple standard acquisition functions match or outperform purpose-built asynchronous methods across synthetic and real-world tasks.

stat.ML↗