Talks

The principle dynamical components in the large scale ocean (i.e. spatial scales larger than O(100m)) are dominated by mesoscale eddies, submesoscale currents and interia gravity waves comprising both storm-forced Near-Inertial Waves (NIWs) and tidally generated internal waves (TIWs).  While mesoscale eddies (length scales of 10s of kms), generated primarily through baroclinic instability are the principal reservoir of kinetic energy in the world oceans, submesoscale currents (O(1-10 km)) comprising mixed layer eddies and fronts interact strongly with mesoscales and are critical for  regulating biogeochemical and air-sea fluxes. Here we use the so-called coarse-graining approach to compute cross-scale energy fluxes that shed light on dynamical interactions between these three components and their impact on the upper ocean. By first decomposing the oceanic velocity field into rotational and divergent components on one hand (i.e. a Helmholtz decomposition) and separately using an eddy-IG...

Mesoscale Eddies are rotating structures found in the ocean with size of 50 to 500 km with a lifespan of 10 to 100 days. Eddies can be detected from Sea Surface Height (SSH) data. These circulations play a major role in transportation of heat, salt, carbon and nutrients throughout the ocean and are also associated with complex atmosphere-ocean interactions. That is why the detection of the eddies is extremely important to understand the big picture of the climate. Various, physics based methods were used for eddies detection but dependent upon the threshold values and human expertise which leads to the false detection. Therefore, advance techniques of artificial intelligence are needed to overcome these challenges. Various Deep Learning based works were done over the South China Sea and South Atlantic Ocean but Bay of Bengal is less explored. In this study we trained deep learning models, U-Net and Attention U-Net, for detecting the mesoscale ocean eddies over Bay of Bengal from daily ...

Present-day galaxies still contain a multitude of clues about their formation histories. But which properties should we best look at and what do they actually reveal about the past? Using cosmological simulations, I will show what insights about a galaxy's formation and merger history can be gained from a diverse set of measurements. For this, I will show how the overall galaxy shapes and tidal features in the outskirts are related to the inner kinematics. These results give us indications about the correlation with the recent merger histories of the galaxies. Finally, I will discuss how early gas-rich mergers can be revealed through the ages of globular cluster populations.

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Hilbert spaces are incomprehensibly vast and rich. Model Hamiltonians are space ships. They could take us to new worlds, such as cold \textit{spin liquid oasis} in hot regions in Hilbert space deserts. Exact decomposition of isotropic Heisenberg Hamiltonian on a Honeycomb lattice into a sum of 3 non-commuting (permuted) Kitaev Hamiltonians, helps us build a degenerate \textit{manifold of metastable flux free Kitaev spin liquid vacua} and vector Fermionic (Goldstone like) collective modes. Our method, \textit{symmetric decomposition of Hamiltonians}, will help design exotic metastable quantum scars and exotic quasi particles, in nonexotic real systems.
G. Baskaran, arXiv:2309.07119

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