Ahmed Almheiri, Thomas Hartman, Juan Maldacena, Edgar Shaghoulian, Amirhossein Tajdini

In this review, we describe recent progress on the black hole information problem that involves a new understanding of how to calculate the entropy of Hawking radiation. We show how the method for computing gravitational fine-grained entropy, developed over the past 15 years, can be extended to capture the entropy of Hawking radiation. This technique reveals large corrections needed for the entropy to be consistent with unitary black hole evaporation.

Sihao Cheng, Yuan-Sen Ting, Brice Ménard, Joan Bruna

Parameter estimation with non-Gaussian stochastic fields is a common challenge in astrophysics and cosmology. In this paper we advocate performing this task using the scattering transform, a statistical tool rooted in the mathematical properties of convolutional neural nets. This estimator can characterize a complex field without explicitly computing higher-order statistics, thus avoiding the high variance and dimensionality problems. It generates a compact set of coefficients which can be used as robust summary statistics for non-Gaussian information. It is especially suited for fields presenting localized structures and hierarchical clustering, such as the cosmological density field. To demonstrate its power, we apply this estimator to the cosmological parameter inference problem in the context of weak lensing. Using simulated convergence maps with realistic noise, the scattering transform outperforms the power spectrum and peak counts, and is on par with the state-of-the-art CNN. It retains the advantages of traditional statistical descriptors (it does not require any training nor tuning), has provable stability properties, allows to check for systematics, and importantly, the scattering coefficients are interpretable. It is a powerful and attractive estimator for observational cosmology and, in general, the study of physically-motivated fields.