Although many exoplanets have been indirectly detected in recent years, direct imaging of them with ground-based telescopes remains challenging. In the presence of atmospheric fluctuations, it is ambitious to resolve the high brightness contrasts at the small angular separation between the star and its potential partners. Post-processing of telescope images has become an essential tool to improve the resolvable contrast ratios. This paper contributes a post-processing algorithm for fast-cadence imaging, which deconvolves sequences of telescope images. The algorithm infers a Bayesian estimate of the astronomical object, as well as the atmospheric optical path length, including its spatial and temporal structures. For this, we utilize physics-inspired models for the object, the atmosphere, and the telescope. The algorithm is computationally expensive but allows us to resolve high contrast ratios despite short observation times and no field rotation. We test the performance of the algorithm with pointlike companions synthetically injected into a real data set acquired with the SHARK-VIS pathfinder instrument at the LBT telescope. Sources with brightness ratios down to 6 × 10−4 to the star are detected at 185 mas separation with a short observation time of 0.6 s.

The viral load of patients infected with SARS-CoV-2 varies on logarithmic scales and possibly with age. Controversial claims have been made in the literature regarding whether the viral load distribution actually depends on the age of the patients. Such a dependence would have implications for the COVID-19 spreading mechanism, the age-dependent immune system reaction, and thus for policymaking. We hereby develop a method to analyze viral-load distribution data as a function of the patients’ age within a flexible, non-parametric, hierarchical, Bayesian, and causal model. The causal nature of the developed reconstruction additionally allows to test for bias in the data. This could be due to, e.g., bias in patient-testing and data collection or systematic errors in the measurement of the viral load. We perform these tests by calculating the Bayesian evidence for each implied possible causal direction. The possibility of testing for bias in data collection and identifying causal directions can be very useful in other contexts as well. For this reason we make our model freely available. When applied to publicly available age and SARS-CoV-2 viral load data, we find a statistically significant increase in the viral load with age, but only for one of the two analyzed datasets. If we consider this dataset, and based on the current understanding of viral load’s impact on patients’ infectivity, we expect a non-negligible difference in the infectivity of different age groups. This difference is nonetheless too small to justify considering any age group as noninfectious.