arXiv subjects

Aastha Jain

Publications and source records attributed to Aastha Jain.

5 recordsLinked to original sources

arXiv2026

Weyl semimetallic state with antiferromagnetic order in Rashba-Hubbard model

We study the phase diagram of Rashba-Hubbard model by employing the Hartree-Fock meanfield theory, and thereby establish the existence of an antiferromagnetically ordered Weyl semimetallic state with in-plane magnetic moments. This phase is found to be sandwiched in between the antiferromagnetic insulator and Rashba metal in the interaction vs spin-orbit coupling phase diagram. The antiferromagnetically-ordered topological semimetallic state exists in the presence of combined time-reversal and inversion symmetry though individually both are broken. The study of the static magnetic susceptibility indicates the robustness of the antiferromagnetic order within a realistic range of interaction and spin-orbit coupling parameters. In addition to the edge states associated with the Weyl points, we also investigate the spin-resolved quasiparticle interference, which provides important insight into the possible spin texture of the bands especially in the vicinity of Weyl points.

cond-mat.str-el↗

arXiv2025

Non-reciprocal spin-wave excitations in Rashba-Hubbard ferromagnets

We explore the nonreciprocity of spin-wave excitations in the Rashba-Hubbard ferromagnet on a square lattice. Our study reveals that the propagation of spin-wave excitations exhibit non-reciprocal behavior, i.e., spin waves traveling in opposite directions display asymmetry in energy dispersion $ω({\bf q}) \ne ω(-{\bf q)}$, which also results in an asymmetric behavior of group velocity, spin stiffness, etc. We find that this asymmetric behavior arises only when the magnetic moments are aligned inside the atomic plane, while the excitations remain symmetric for out-of-plane magnetization. The first dominating term in the low-energy dispersion is linear. However, if the magnetic moments are out-of-plane, then the first dominant term is quadratic instead. The low-energy non-quadratic behavior is examined in the intermediate-to-strong coupling regime for various strengths of Rashba spin-orbit coupling.

cond-mat.str-el↗

arXiv2025

Comparing skill of historical rainfall data based monsoon rainfall prediction in India with NWP forecasts

The Indian summer monsoon is a highly complex and critical weather system that directly affects the livelihoods of over a billion people across the Indian subcontinent. Accurate short-term forecasting remains a major scientific challenge due to the monsoon's intrinsic nonlinearity and its sensitivity to multi-scale drivers, including local land-atmosphere interactions and large-scale ocean-atmosphere phenomena. In this study, we address the problem of forecasting daily rainfall across India during the summer months, focusing on both one-day and three-day lead times. We use Autoformers - deep learning transformer-based architectures designed for time series forecasting. These are trained on historical gridded precipitation data from the Indian Meteorological Department (1901--2023) at spatial resolutions of $0.25^\circ \times 0.25^\circ$, as well as $1^\circ \times 1^\circ$. The models also incorporate auxiliary meteorological variables from ECMWFs reanalysis datasets, namely, cloud cover, humidity, temperature, soil moisture, vorticity, and wind speed. Forecasts at $0.25^\circ \times 0.25^\circ$ are benchmarked against ECMWFs High-Resolution Ensemble System (HRES), widely regarded as the most accurate numerical weather predictor, and at $1^\circ \times 1^\circ $ with those from National Centre for Environmental Prediction (NCEP). We conduct both nationwide evaluations and localized analyses for major Indian cities. Our results indicate that transformer-based deep learning models consistently outperform both HRES and NCEP, as well as other climatological baselines. Specifically, compared to our model, forecasts from HRES and NCEP model have about 22\% and 43\% higher error, respectively, for a single day prediction, and over 27\% and 66\% higher error respectively, for a three day prediction.

cs.LG↗

arXiv2025

Orbital correlations in bilayer nickelates: roles of doping and interlayer coupling

We study the nature of orbital correlations present in the bilayer nickelate within a minimal two-orbital tight-binding model to gain insights into their possible role in stabilizing the less-known weakly-insulating state. The latter has been observed experimentally at ambient pressure. In order to achieve this objective, we examine the static orbital susceptibilities within the random-phase approximation. Our study highlights the sensitivity of orbital correlations to various factors including the interlayer coupling, carrier concentration, band-structure details such as the orbital contents, the number of bands contributing at the Fermi level etc. We relate this sensitiveness to the modification of the Fermi surfaces as well as their orbital contents dependent on aforementioned factors.

cond-mat.str-el↗

arXiv2023

Causal Categorization of Mental Health Posts using Transformers

With recent developments in digitization of clinical psychology, NLP research community has revolutionized the field of mental health detection on social media. Existing research in mental health analysis revolves around the cross-sectional studies to classify users' intent on social media. For in-depth analysis, we investigate existing classifiers to solve the problem of causal categorization which suggests the inefficiency of learning based methods due to limited training samples. To handle this challenge, we use transformer models and demonstrate the efficacy of a pre-trained transfer learning on "CAMS" dataset. The experimental result improves the accuracy and depicts the importance of identifying cause-and-effect relationships in the underlying text.

cs.CL↗