Talks

Machine learning has significantly improved the way scientists model and interpret large datasets across a broad range of the physical sciences; yet, its "black box" nature often limits our ability to trust and understand its results. Interpretable and explainable AI is ultimately required to realize the potential of machine-assisted scientific discovery. I will review efforts toward explainable AI focusing in particular in applications within the field of Astrophysics. I will present an explainable deep learning framework which combines model compression and information theory to achieve explainability. I will demonstrate its relevance to cosmological large-scale structures, such as dark matter halos and galaxies, as well as the cosmic microwave background, revealing new physical insights derived from these explainable AI models.

As we've seen at this workshop, exciting progress has recently been made in the study of neural networks by applying ideas and techniques from theoretical physics. In this talk, I will discuss a precise relation between quantum field theory and deep neural networks, the NN/QFT correspondence. In particular, I will go beyond the level of analogy by explicitly constructing the QFT corresponding to a class of networks encompassing both vanilla feedforward and recurrent architectures. The resulting theory closely resembles the well-studied O(N) vector model, in which the variance of the weight initializations plays the role of the 't Hooft coupling. In this framework, the Gaussian process approximation used in machine learning corresponds to a free field theory, and finite-width effects can be computed perturbatively in the ratio of depth to width, T/N. These provide corrections to the correlation length that controls the depth to which information can propagate through the network, and thereby sets the scale at which such networks are trainable by gradient descent. This analysis provides a non-perturbative description of networks at initialization, and opens several interesting avenues to the study of criticality in these models.

Radio observations are essential for studying galaxy formation and evolution, yet analyzing low-frequency interferometric data is challenging due to radio frequency interference (RFI) contamination and other system issues. To streamline this process, we developed GARUDA, an automated pipeline for analyzing GMRT data, employing AI/ML-based algorithms for efficient RFI identification and artifact removal. GARUDA enables fast and consistent data reduction, handling ~10-12 GB GSB data in 20-30 minutes and ~400 GB GWB data in under three hours on standard servers. In this presentation, I will discuss GARUDA’s capabilities and showcase results, including some of the deepest GMRT radio continuum images at the L-band, HI emission in galaxies, and one of the most sensitive galactic HI absorption lines (using frequency switching observation with GWB).

Measurements of magnetic fields at the outskirts of galaxy clusters provide crucial insights into the evolution of cosmic magnetic fields. Radio relics are known to trace merger shocks in galaxy clusters propagating in the periphery of galaxy clusters. Many relics show a very high linear polarisation, which is puzzlingly aligned with the orientation of the relics. In this talk we will briefly review current research on radio relics and what these observations tell us about the structure and evolution of magnetic fields. We use Rotation Measure (RM) synthesis and QU fitting methods to spatially resolve the properties of the dominant components in Faraday space. I will show how these methods allow us to constrain the properties of the magnetic fields in the cluster outskirts through a combined analysis of Faraday depths and depolarisation.

In this talk, I will provide an update on the current status of the SKA Observatory, which is currently under construction. I will also describe the role of India in this project, and summarise the various activities going on in India, coordinated by the SKA India Consortium, towards India's participation in the SKAO.