Search Talks

We study the nonequilibrium stationary state (NESS) induced by quantum resetting of a system of N noninteracting bosons in a harmonic trap. Under repeated resetting, the system reaches a NESS where the positions of bosons get strongly correlated due to simultaneous resetting induced by the trap. We fully characterize the steady state by analytically computing several physical observables such as the average density, extreme value statistics, order and gap statistics, and also the distribution of the number of particles in a region [−L,L], known as the full counting statistics (FCS). This is a rare example of a strongly correlated quantum many-body NESS where various observables can be exactly computed.

Emergence of different interesting phenomena in different scale is the heart of different physical system in mother nature. In this presentation, we show explicitly how the strong correlation appears in double frequency sine-Gordon field theory. Our study is the detail quantum field theoretical derivation of strongly correlated quantum Ising model.

Legendre transform between thermodynamic quantities such as the Helmholtz free energy and entropy plays a key role in the formulation of the canonical ensemble. In the standard treatment, the transform exchanges the independent variable from the system’s internal energy to its conjugate variable—the inverse temperature of the heat reservoir. In this article, we formulate a microscopic version of the trans- form between the free energy and Shannon entropy of the system, where the conju- gate variables are the microstate probabilities and the energies (scaled by the inverse temperature). The present approach gives a non-conventional perspective on the connection between information-theoretic measure of entropy and thermodynamic entropy. We focus on the exact differential property of Shannon entropy, utilizing it to derive central relations within the canonical ensemble. Thermodynamics of a system in contact with the heat reservoir is discussed in this framework. Other approaches, in particular, Jaynes’ maximum entropy principle is compared with the present approach. Ref: R.S. Johal, Microscopic Legendre Transform, Canonical Ensemble and Jaynes’ Maximum Entropy Principle, Foundations of Physics, 55:12 (2025). https://doi.org/10.1007/s10701-025-00824-7

Motility-induced phase separation (MIPS) describes how self-propelling particles phase-separate due to their motility, a phenomenon observed in many natural systems. Many motile organisms thrive in fluid environments, and chaotic background flows often act as mixing agents. This raises the question: how does background flow affect the phase separation of motile organisms? To explore this, we study active Brownian particles (ABPs) in a periodic four-roll-mill flow using numerical simulations. Without flow, the system undergoes MIPS. We maintain a packing fraction of 0.7 and investigate the effect of flow on phase separation, referred to as flow-induced phase separation (FIPS). To probe the FIPS regime, we define an order parameter that provides clear insights into this type of phase separation. Additionally, we explore the FIPS region by varying the Peclet number (Pe) and packing fraction (ϕ), which enhances our understanding of how these factors influence the phase separation dynamics.

We study extreme events of avalanche activities in finite-size two-dimensional self- organized critical (SOC) models, specifically the stochastic Manna model (SMM) and Bak-Tang-Weisenfeld (BTW) sandpile model. Employing the approach of block maxima, the study numerically reveals that the distributions for extreme avalanche size and area follow the generalized extreme value (GEV) distribution. The extreme avalanche size follows the Gumbel distribution with shape parameter $\xi=0$ while in case of the extreme avalanche area, we report $\xi>0$. We propose scaling functions for extreme avalanche activities that connect the activities on different length scales. With the help of data collapse, we estimate the precise values of these critical exponents. The scaling functions provide an understanding of the intricate dynamics for different variants of the sandpile model, shedding light on the relationship between system size and extreme event characteristics. Our findings give insight into the extreme behavior of SOC models and offer a framework to understand the statistical properties of extreme events.