ICTS

I will explain how to derive an open effective field theory for interacting fermions, using holographic techniques

Binaries in different astrophysical environments can have significantly different spin alignment, mass distribution, and eccentricity. We use eccentricity to identify the differences in BBH mergers using GW data in a multibanding scenario. The key idea of multi-banding is the following: we only `hear' a relatively small number of high-frequency cycles just before the merger in the earth-based GW detectors. But BBHs emit in the eLISA and LGWA bands for years before eventually chirping into these high frequencies. Combining signals from LISA, LGWA, and 3G detectors can further constrain and unfold the BBH formation channels more strongly than the single detector network. The eccentricity of a BBH-emitting GW rapidly decays with increasing frequency due to radiation reactions. Therefore, the binary orbit formed with a higher eccentricity circularises way before it chirps into the frequency band of the ground-based detectors. Applying combined observations from detectors `active' in different frequency ranges therefore has the potential to retrace the eccentricity evolution, unveiling their formation history.

In classical dynamical systems, a chaotic map can be stochastically controlled onto unstable periodic orbits leading to controlled and uncontrolled phases as a function of the rate at which the control is applied. Our recent work on classical and quantum Bernoulli maps demonstrates that these control transitions persist in the quantum regime, where local measurements and unitary feedback serve as quantum proxies for classical control. This work applies these control protocols to the classical and quantum kicked top model, a paradigmatic model of quantum chaos. A key feature of this model is its manifestation of classical, semiclassical, and quantum dynamics, with wave packet interference evident after the Ehrenfest timescale. The interplay of control protocols with quantum interference effects offers an intriguing playground to test the controllability of quantum chaotic dynamics in the presence of these effects. To our knowledge, such stochastic control has not been investigated for physical Hamiltonian dynamics lacking analytical knowledge of fixed points or periodic orbits. We show that the classical and quantum dynamics of the kicked top can be controlled. In the quantum regime, we demonstrate the existence of a coherence transition, in addition to the control transition, manifesting at different rates of stochastic control.

Though dynamical systems serve as models to a wide array of physical and engineering systems, one of the main challenges in developing such models is the estimation of parameters or coefficients in the model. Since parameters in a dynamical system can change both qualitative and quantitative aspects of a solution, their accurate estimation is of utmost importance. In our recent work, we investigate how one can estimate parameters for a finite dimensional dynamical system from partial observations of the state. Indeed we show, under certain assumptions, we can simultaneously achieve state and parameter estimation for a dynamical system by constructing a suitable observer for the same. We provide a convergence result for the linear case and numerical simulations as evidence of convergence for the nonlinear case. The idea is generic, easy to implement and applicable to a large class of systems.

The Lensing of gravitational wave occurs when it passes nearby a massive objects like galaxy and galaxy clusters that bend its path. The detection of first lensed gravitational wave is expected within next few years. The next generation detectors such as Lunar gravitational wave detector is expected to detect the gravitational waves in decihertz range coming from intermediate mass blackhole mergers and white dwarf binaries. A small fraction of these gravitational waves would be lensed. In our study, we calculate the lensing fraction for strongly lensed gravitational waves using Lunar gravitational wave detector. For binary blackhole mergers which are lensed by galaxy clusters, the lensing fraction lie between 0.3% to 0.6%.

The pathways behind the formation and subsequent mass growth of the supermassive blackholes(SMBHs), having masses (10^6 - 10^9) M_\solar and found at the centre of almost all the galaxies is quite an open question. They are thought to be formed from the light seeds (black hole remnants of Pop-III stars) or heavy seeds (black holes formed from direct collapsed gas clouds) at very high redshift. They eventually increased their mass via accretion and mergers to show up as the SMBHs that we see today. Our main aim in this project is to probe how well can we distinguish between different models of SMBH formation and evolution using gravitational wave observations using LISA, LGWA and 3G detectors, i.e., multibanding. We will generate simulated observations assuming different SMBH formation models, and reconstruct them from the observed samples of M and z using Hierarchical Bayesian model selection. For the time being, we have approximated the individual posteriors using Fisher matrix analysis and tried to get the limits on the various hyper-parameters that are used in our model.

This paper investigates the phase transition behavior of a modified spin-1/2 XXZ chain in the critical regime, aiming to construct a comprehensive phase diagram connecting various critical limits. By tuning the modified Hamil- tonian to a simple XXZ chain, intriguing insights emerge, particularly when the even (or odd) bond coupling strength dominates, simplifying the residual part to the quantum Ising model. Employing mean field analysis with order parameters (BO0,BOπ, and CDW), our results align closely with Density Matrix Renormalization Group (DMRG) calculations.

The future of Gravitational Wave (GW) detectors [LVK] have made remarkable progress, with an expanding sensitivity band and the promise of exponential increase in detection rates for upcoming observing runs [O4 and beyond]. Among the diverse sources of GW signals, eccentric Binary mergers present an intriguing and computationally challenging aspect. We address the imperative need for efficient detection and classification of eccentric Binary mergers using Machine Learning (ML) techniques. Traditional Bayesian Parameter estimation methods, while accurate, can be prohibitively time-consuming and computationally expensive. To overcome this challenge, we leverage the capabilities of ML to expedite the identification and classification of eccentric GW events. I will present our approach that employs Separable Convolutional Neural Networks (SCNN) to discriminate between non-eccentric and eccentric Binary mergers and further classifying the latter into categories of low, moderate, and high eccentricity mergers.

We discuss how quantum jumps affect localized regimes in driven-dissipative disordered many-body systems featuring a localization transition. We introduce a deformation of the Lindblad master equation that interpolates between the standard Lindblad and the no-jump non-Hermitian dynamics of open quantum systems. As a platform, we use a disordered chain of hard-core bosons with nearest-neighbor interactions and subject to incoherent drive and dissipation at alternate sites. We probe both the statistics of complex eigenvalues of the deformed Liouvillian and dynamical observables of physical relevance. We show that reducing the number of quantum jumps, achievable through realistic post-selection protocols, can promote the emergence of the localized phase. Our findings are based on exact diagonalization and time-dependent matrix-product states techniques.

Astrophysical jets are powerful collimated outflows from accreting black holes, ranging from XRBs to AGNs that can extend up to galactic scales. Though there have been theoretical models formulated to explain the dynamics of jets, their formation and evolution are poorly understood. Limited observational evidences and some theoretical investigations speculate that the spin and mass of the black holes along with the magnetic flux decide the power of these jets. Several observations also confirm the presence of dynamically dominant magnetic fields in the vicinity of central black holes. Such strong magnetic fields may be generated by dynamos due to turbulence created by the onset of magneto-rotational instability(MRI). As a preliminary study, we look at the correlation between the dynamo action and jet power in such systems. We perform direct numerical simulations of disk-jet using the publicly available General Relativistic Magnetohydrodynamic code called Black-Hole Accretion Code (BHAC).

Magnetic reconnection, a fundamental plasma process occurring in astrophysical, laboratory, and space plasmas, involves a change in the topology of magnetic fields within regions of substantial magnetic shear. This phenomenon, in various avatars, including the tearing mode and the plasmoid instability, has been extensively studied in 2 and 2.5 dimensions, revealing crucial aspects of its behavior. Nevertheless, a comprehensive understanding of reconnection in full three dimensions remains largely lacking. Previous explorations of 3D reconnection have uncovered manifestations in the form of the oblique tearing mode and the kink instability. Our investigations have focused on the tearing mode of anti-parallel flux-tubes and we find a behavior that is unlike the previously studied manifestations of 3D reconnection. I shall present insights obtained through 3D magnetohydrodynamic (MHD) direct numerical simulations and some analytical results from linear stability analysis. Both, similarities and differences in reconnection behavior when transitioning from two to three dimensions will be elucidated.

In the oceanic submesoscales, where the Rossby number of a flow can be of O(1), the energy exchange between waves and balanced flow can lead to the breakdown of balanced flow. We are attempting to model the above dynamics using the barotropic vorticity equation coupled with an ML based parametrisation term. The convolutional neural network trained on the simulation data of a dynamical model has shown promising predictions and was successful in simulating the dynamics statistically. Exploration of other schemes like physics-informed neural networks and sparse equation discovery algorithms like genetic programming are under progress.