Astrophysics

In my talk, I will argue that symmetry strongly changes the behaviour of massive gravity relative to its massless sibling.   After reviewing the formulation of dRGT theory of ghost-free massive gravity, I will examine the minimal model and next-to-minimal model in spherical symmetry. Although the latter has been argued to have a good Vainshtein mechanism in spherical symmetry, I will derive a restriction on non-relativistic matter that is at odds with a reasonable phenomenology. Moreover, the theory cannot reproduce the same behaviour as GR for a scalar field collapse in the small mass limit without encountering a singularity at some point in the evolution.   Since symmetry is the epitome of non-genericity, the resolution may well be that we should forgo symmetry and focus on more generic behaviour in massive gravity to study the screening mechanism. This talk is based on https://arxiv.org/pdf/2409.18802

In modern astronomy, artificial intelligence (AI) is increasingly utilized to analyze large volumes of data, significantly reducing the need for human computational resources and time. Machine learning (ML) techniques are at the forefront of revealing astronomical mysteries by analyzing observed data. Here, we will introduce the application of machine learning to Intensity Interferometry (II) data for high-resolution optical astronomy, aiming to overcome the limitations of traditional image reconstruction methods. In this presentation, we demonstrate successful image reconstruction of a fast-rotating star using conditional Generative Adversarial Networks (cGANs), a supervised machine learning approach. Simulations of II are based on an assembly of four telescopes similar to existing arrays. However, the sensitivity of the signal and high resolution are expected to improve with additional baselines. It makes the current and future Cherenkov Telescope Array Observatory (CTAO) an ideal candidate for II applications. Our approach is highly relevant and innovative, addressing key challenges in phase reconstruction and proposing novel solutions that could revolutionize high-resolution imaging in astronomy.

Even with dense sampling of the uv plane intensity correlations only contain half the information required to reconstruct an image. Intensity correlations do contain the full information of the image power spectrum and therefore of the image 2-point correlation function. With some practice one can gain intuitive understanding in interpreting 2-point correlation function "images". This is illustrated with both toy examples and modeling of real astronomical images. In some assumptions one can even interpret these 2-point correlation function "images" with only a few baselines.