arXiv subjects

Santosh Kumar

Publications and source records attributed to Santosh Kumar.

At least 19 recordsLinked to original sources

arXiv2026

Spectral and Eigenvector Crossovers in Random Mixed Graphs

We study the spectral and eigenvector properties of random mixed graphs, combining undirected and directed interactions, using random matrix theory (RMT). The network is represented by a Hermitian adjacency matrix, with undirected links as real entries and directed links as purely imaginary conjugate pairs, ensuring a real spectrum. Network density is controlled by the connection probability, while directionality sets the fraction of directed edges. We focus on the GOE-to-GUE crossover: at fixed connectivity, increasing directionality breaks time-reversal symmetry and drives spectral statistics from GOE to GUE. We show that this transition requires sufficient connectivity. In sparse networks, weak level repulsion produces Poisson statistics regardless of directionality. At fixed directionality, increasing connectivity drives a Poisson-to-GUE crossover; only above a sparsity threshold does the directionality-induced GOE-to-GUE transition emerge. In the dense regime, where the spectral density follows the Wigner semicircle law, the crossover is characterized using spacing distributions, spacing ratios, and spectral rigidity. In sparse networks, where unfolding is unreliable, spacing-ratio statistics provide an unfolding-free characterization. Eigenvector structure is examined through multifractal dimensions and component distributions, while Kullback--Leibler divergence confirms the robustness of the transitions. Applied to S&P 500 mixed graphs, the framework reveals a GOE-to-GUE crossover across four major market crashes. Denser crisis periods show sharper crossovers than sparser recovery periods. The results provide a unified picture of how connectivity and symmetry breaking govern spectral and eigenvector universality, while providing a transparent probe of changing financial-market organization.

physics.comp-ph↗

arXiv2026

Precise Photon Arrival Time Measurement via Time to Frequency Demultiplexing

We demonstrate a nonlinear-optics approach to precise measurement of photon arrival time, by translating the temporal information of single photons to a wavelength distribution of frequency conversion followed by de-multiplexed detection. It uses a multi-color, pulse-delayed pump laser to drive multiplexed frequency conversion, transducing photons to various frequency channels according to their arrival time. By photon detection in each channel, the measurement resolution and accuracy can reach picosecond level, much lower than the detectors' naive resolution and significantly beating the shot-noise limited direct detection. Distinct to any method relying on repeated, multiple sampling, our approach supports event-ready operations, capable of detecting randomly arriving single photons with no dead window. It is thus particularly suitable for practical applications of ranging, sensing, and communications in the dynamic, photon-starving environment.

physics.optics↗

arXiv2026

Emerging Non-Volatile Opto-electronic Resistive Memories for Next-Generation Photonic Integrated Circuits

Photonic integrated circuits have emerged as a powerful platform for high speed communication, sensing, and information processing due to their large bandwidth, low latency, and inherent parallelism. However, the absence of efficient, scalable, and non-volatile memory elements remains a fundamental limitation for realizing fully programmable and adaptive photonic systems. Conventional electronic memories introduce significant energy overhead, latency, and architectural inefficiencies due to repeated optical electrical conversions. Non volatile opto electronic resistive memories or OERMs have recently emerged as a promising solution to address these challenges by integrating memory functionality directly within the photonic domain. These devices combine resistive switching mechanisms with optical readout, enabling persistent state retention, multilevel programmability, and energy efficient operation. In this review, we provide a comprehensive overview of OERMs, spanning from fundamental physical mechanisms to system level applications. We first discuss the underlying resistive switching phenomena, including filamentary conduction, interface type switching, phase change transitions, and ionic migration, with particular emphasis on their interaction with confined optical modes. We then examine key material platforms such as metal oxides, transparent conducting oxides, phase change materials, and emerging two-dimensional systems, highlighting their performance trade-offs. Furthermore, we analyse device architectures and benchmark their performance in terms of switching energy, speed, endurance, and optical modulation efficiency. The integration of OERMs into programmable photonic circuits, neuromorphic systems, and in-memory optical computing architectures is critically discussed. Finally, we outline the major challenges and future research directions toward scalable, reliable

physics.optics↗

arXiv2026

Supervised machine learning of compressible flow past a rotating cylinder

High-fidelity numerical simulations of compressible flow past a rapidly rotating cylinder are used to investigate the evolution of aerodynamic loads and flow instability over a wide range of Reynolds numbers (Re = 1000 to 6000). The study reveals a transition from periodic vortex shedding to complex multi-mode oscillatory states, with a critical bifurcation identified near Re = 5650. Spectral analysis of lift and drag signals shows the emergence and interaction of multiple dominant frequencies, accompanied by amplitude modulation and nonlinear mode coupling in the post-bifurcation regime. To model these highly nonlinear dependencies, data-driven approaches are systematically explored using a database of 101 high-fidelity simulations (1 million core hours). Polynomial regression provides baseline fits but fails to capture localized fluctuations near bifurcation. Bayesian regression frameworks employing B-spline and Gaussian radial basis functions improve flexibility and uncertainty quantification, with spline-based models demonstrating superior performance in capturing piecewise nonlinear trends. Artificial neural networks (ANNs) are then developed as high-capacity surrogate models, achieving excellent predictive accuracy for maximum lift coefficient and instability onset time, while maintaining reasonable fidelity for the more challenging drag coefficient. Beyond regression, the ANN is further evaluated as a generative model to reconstruct flow behavior at unseen Re. A hierarchical refinement strategy is introduced, and results show that when trained on high-fidelity data, ANN-based models can serve as efficient and reliable surrogates for complex fluid dynamics problems.

physics.flu-dyn↗

arXiv2026

Phonon-enhanced strain sensitivity of quantum dots in two-dimensional semiconductors

Two-dimensional semiconductors have attracted considerable interest for integration into emerging quantum photonic networks. Strain engineering of monolayer transition-metal dichalcogenides (ML-TMDs) enables the tuning of light-matter interactions and associated optoelectronic properties, and generates new functionalities, including the formation of quantum dots (QDs). Here, we combine spatially resolved micro-photoluminescence ($\mu$-PL) spectroscopy from cryogenic (4$\text{-}$94 K) to room temperature with micro-Raman spectroscopy at room temperature to investigate the strain-dependent emission energies of thousands of individual QDs in ML-WS$_2$ and ML-WSe$_2$, integrated across multiple heterostructures and a piezoelectric device. Compared with delocalized excitons, QDs in both materials exhibit enhanced strain sensitivities of their emission energies $-$ approximately fourfold in WS$_2$ and twofold in WSe$_2$ $-$ leading to pronounced broadening of the ensemble emission linewidth. Temperature-dependent $\mu$-PL spectroscopy combined with dynamic strain tuning experiments further reveal that the enhanced strain sensitivity of individual QDs originates from strengthened interactions with low-energy phonons induced by quantum confinement. Our results demonstrate a versatile strain-engineering approach with potential for spectral matching across solid-state, atomic, and hybrid quantum photonic networks, and provide new insights into phonon-QD interactions in two-dimensional semiconductors.

quant-ph↗

arXiv2026

Gate-tuneable single-photon emitters in WSe2 monolayer created via AFM nanoindentation on rigid SiO2/Si substrates

Single-photon emitters (SPEs) hosted by two-dimensional (2D) semiconducting materials are envisioned for next-generation quantum applications. However, SPE creation in 2D semiconductors on rigid substrates like SiO2/Si via nanoindentation is a technological gap, critical for interfacing SPEs with photonic circuits and cavities. Here, we report a protocol for deterministically creating SPEs in monolayer WSe2 on SiO2/Si substrates using a sharp diamond AFM (atomic force microscope) tip. A displacement-controlled indentation process is developed, allowing indent depths > 150 nm necessary for creating SPEs. Sharp defect peaks (~200 {\mu}eV) are observed in cryogenic (4K) photoluminescence (PL) spectrum at nanoindented sites and are stable upto ~ 120K. 76% of sites exhibit sharp defect-bound peaks confirmed by power-dependent, temperature-dependent, and time-resolved PL (TRPL). AFM and PL mapping link these peaks to indent periphery. The peaks show sub-linewidth spectral jitter, no blinking, and single-photon nature in second-order autocorrelation measurements. SPEs can be switched on/off, and background emissions suppressed using electrical gating. Gate-voltage dependent TRPL indicate that SPE dynamics can be tuned, depending on nature of SPE, pointing the way to higher-purity SPEs. Our work is directly applicable to other 2D materials and photonic circuit/cavity compatible rigid substrates and is a significant step for scalable SPE technologies.

cond-mat.mes-hall↗

arXiv2025

Autonomous battery research: Principles of heuristic operando experimentation

Unravelling the complex processes governing battery degradation is critical to the energy transition, yet the efficacy of operando characterisation is severely constrained by a lack of Reliability, Representativeness, and Reproducibility (the 3Rs). Current methods rely on bespoke hardware and passive, pre-programmed methodologies that are ill-equipped to capture stochastic failure events. Here, using the Rutherford Appleton Laboratory's multi-modal toolkit as a case study, we expose the systemic inability of conventional experiments to capture transient phenomena like dendrite initiation. To address this, we propose Heuristic Operando experiments: a framework where an AI pilot leverages physics-based digital twins to actively steer the beamline to predict and deterministically capture these rare events. Distinct from uncertainty-driven active learning, this proactive search anticipates failure precursors, redefining experimental efficiency via an entropy-based metric that prioritises scientific insight per photon, neutron, or muon. By focusing measurements only on mechanistically decisive moments, this framework simultaneously mitigates beam damage and drastically reduces data redundancy. When integrated with FAIR data principles, this approach serves as a blueprint for the trusted autonomous battery laboratories of the future.

physics.ins-det↗

arXiv2025

Operational entanglement of collective quantum modes at room temperature

Quantum entanglement is commonly assumed to be fragile at ambient temperature and over macroscopic distances, where thermal noise and dissipation are expected to rapidly suppress nonclassical correlations. Here we show that this intuition fails for collective quantum modes whose dynamics is governed by reduced open-system channels rather than by microscopic thermal equilibrium. For two spatially separated collective modes, we derive an exact entanglement boundary based on the positivity of the partial transpose, valid in the symmetric resonant limit. From this result we obtain an explicit minimum collective fluctuation amplitude, expressed entirely in measurable noise, bandwidth, dissipation, and distance-dependent coupling parameters, required to sustain steady-state entanglement at finite temperature. We further show that large collective occupation suppresses but does not eliminate quantum phase diffusion, so the steady state remains phase symmetric and does not collapse to a classical mean-field despite macroscopic signal amplitudes. Stochastic simulations of the reduced open-system dynamics, together with matched classical correlated-noise null models analyzed through an identical pipeline, confirm that entanglement witnesses are violated only in the quantum regime. Our results establish a minimal, platform-independent framework connecting collective-mode dynamics, noise injection, distance, and operational certification of macroscopic entanglement.

quant-ph↗

arXiv2025

Partial Collapse and Ensemble Invariance under Continuous Quantum Measurement

Wavefunction collapse is commonly associated with unavoidable physical disturbance of the measured system. Here we show that in driven-dissipative quantum systems, continuous measurement can induce strong trajectory-level collapse while leaving the ensemble-averaged steady state strictly invariant. We identify measurement-invariant steady states whose unconditional density matrix remains unchanged under continuous monitoring, despite pronounced measurement-induced localization in conditioned quantum trajectories. This separation between trajectory-level collapse and ensemble invariance defines a regime of partial collapse, in which measurement-induced localization is continuously counteracted by dissipative dynamics. We derive a necessary and sufficient condition for steady-state invariance under continuous measurement and identify Liouvillian symmetry as a concrete dynamical mechanism enforcing it. Our results clarify the distinction between conditional collapse and physical disturbance in open quantum systems and provide a framework for non-invasive continuous monitoring in driven-dissipative settings.

quant-ph↗

arXiv2025

Bell-Inequality Violation for Continuous, Non-Projective Measurements

Many solid-state quantum platforms do not permit sharp, projective measurements but instead yield continuous voltage or field traces under weak, non-demolition readout. In such systems, standard Bell tests based on dichotomic projective measurements are not directly applicable, raising the question of how quantum nonlocality can be certified from continuous time-series data. Here we develop a general theoretical framework showing that Bell-CHSH inequality violation can be extracted from continuous, non-projective measurements without assuming any specific collapse model or phase distribution. We show that sufficiently long continuous measurements of a single entangled pair sample its internal phase-probability structure, enabling effective dichotomic observables to be constructed through phase-sensitive projections and coarse-graining. The resulting Bell correlator is governed by two experimentally accessible resources: intrinsic single-qubit phase spread and nonlocal phase locking between qubits. We benchmark the resulting estimator against conventional projective-measurement CHSH tests implemented via quantum-circuit simulations using Qiskit, finding quantitative agreement in the Bell-violating regime without parameter fitting. Classical deterministic correlations cannot violate the CHSH bound, whereas quantum phase-locked systems recover the nonlinear angular dependence characteristic of entanglement. Our results provide a practical route to demonstrating Bell nonlocality in platforms where measurements are inherently continuous and weak.

quant-ph↗

arXiv2025

Role of varying Reynolds number for flow past a rotating cylinder at high rotation rate

The present study reports comprehensive bifurcation analysis of flow past a rotating cylinder at a fixed rotation rate by varying free-stream Reynolds number ($Re_{\infty}$) from 1000-6000 in intervals of 50. Two-dimensional compressible Navier-Stokes equations are solved using dispersion relation preserving numerical methods over 101 test cases, amounting to $10^6$ core hours of computing. The dataset produced from high-fidelity simulations serve as useful benchmarking tools for testing compressible flow solvers, estimating unsteady force distribution and vorticity dynamics. For moderate $Re_{\infty}$, rotation induces circulation that reduces pressure drag with increasing $Re_{\infty}$. For higher $Re_{\infty}$, boundary layer becomes thinner with suppressed flow separation, but effect of rotation saturates. Thus, benefits of increasing $Re_{\infty}$ taper off and pressure recovery stalls. The bifurcation analysis reveals a critical $Re_{\infty}$ of 5650 beyond which global behavior of Magnus-Robins effect changes significantly. Supercritical flow is receptive to time-dependent instabilities and structures in wake of the cylinder become dynamically unstable. Even small changes in $Re_{\infty}$ leads to different instantaneous force distributions and sharp fluctuations in lift and drag calculations. Stronger, coherent vortices in the wake generate consistent, high-energy periodic signals, contributing to strong Fourier amplitudes in spectra. An artificial neural network (ANN) is trained using simulation datasets to serve as fast, inexpensive alternatives for calculating lift, drag, and onset time of instability. The ANN reduces time required for simulation by 99.9\%, enabling dense parametric sweeps. Maximum accuracy achieved for the ANN is between 90-99\% for the parameters examined.

physics.flu-dyn↗

arXiv2025

Prototype-based Self-Supervised Multimodal Learning for PPG and Accelerometry Signals

Modeling multi-modal time-series data is critical for capturing system-level dynamics, particularly in biosignals where modalities such as ECG, PPG, EDA, and accelerometry provide complementary perspectives on interconnected physiological processes. While recent self-supervised learning (SSL) advances have improved unimodal representation learning, existing multi-modal approaches often rely on CLIP-style contrastive objectives that overfit to easily aligned features and misclassify valid cross-modal relationships as negatives, resulting in fragmented and non-generalizable embeddings. To overcome these limitations, we propose ProtoMM, a novel SSL framework that introduces a shared prototype dictionary to anchor heterogeneous modalities in a common embedding space. By clustering representations around shared prototypes rather than explicit negative sampling, our method captures complementary information across modalities and provides a coherent "common language" for physiological signals. In this work, we focus on developing a Pulse-Motion foundation model with ProtoMM and demonstrate that our approach outperforms contrastive-only and prior multimodal SSL methods, achieving the best performance among strong prior baselines while offering additional utility for analyzing and interpreting learned features.

cs.LG↗

arXiv2025

Spectral fluctuations and crossovers in multilayer network

We investigate spectral fluctuations in multilayer networks within the random matrix theory (RMT) framework to characterize universal and non-universal features. The adjacency matrix of a multilayer network exhibits a block structure, with diagonal blocks representing intra-layer connections and off-diagonal blocks encoding inter-layer connections. Applying appropriate scaling factors for these blocks, we equalize variances across inter- and intra-layers, enabling direct comparison of spectral statistics. We analyze eigenvalue spectra across multilayer network configurations with varying inter- and intra-layer connectivities. Introducing a crossover model for bilayer networks, we capture the smooth transition of spectral properties from block-diagonal (two independent GOEs) to single-layer (one GOE) statistics as the relative strength of inter-layer to intra-layer connection varies. Furthermore, we analyze interatomic distance networks derived from protein crystal structures, including 1EWT, 1EWK, and 1UW6, to demonstrate applicability. Our findings reveal that the universality of spectral fluctuations persists across multilayer network architectures and highlight RMT as a robust tool for probing topological and dynamical complexities of real-world networks.

math-ph↗

arXiv2025

How Many Times Do People Usually Experience Different Kinds of Stressors Each Day?

Understanding how frequently people experience different kinds of daily stressors is crucial for interpreting stress exposure and informing mental health care. But it can't be directly estimated from current assessment methods, such as diaries, end-of-day interviews, and ecological momentary assessments (EMA), that use sparse sampling to limit participant burden, and a structured response format for uniformity. In this paper, we utilize stressor data collected in a 100-day field study with 68 participants that adopted wearable-triggered prompts and a freeform format to solicit stressors soon after they occurred, but limited its prompts to a small subset to keep the burden low. We develop asymptotic models to estimate the latent frequency of different kinds of real-life stressors that address sample sparsity and sampling bias. We find that people experience 5.39 stressors per day, on average. The top three are related to work (1.76/day), health (0.59/day), and transportation (0.55/day). These estimates offer a principled benchmark for interpreting individual stressor loads. They can also inform mental health care treatments and interventions by establishing population-level baselines.

cs.HC↗

arXiv2025

Dense Associative Memory in a Nonlinear Optical Hopfield Neural Network

Modern Hopfield Neural Networks (HNNs), also known as Dense Associative Memories (DAMs), enhance the performance of simple recurrent neural networks by leveraging the nonlinearities in their energy functions. They have broad applications in combinatorial optimization, high-capacity memory storage, deep learning transformers, and correlated pattern recognition. Thus far, research on DAMs has been primarily theoretical, with implementations limited to CPUs and GPUs. In this work, for the first time to our knowledge, we propose and experimentally demonstrate a nonlinear optical Hopfield neural network (NOHNN) system for realizing DAMs using correlated patterns. Our NOHNN incorporates effective 2-body and 4-body interactions in its energy function. The inclusion of 4-body interaction scores a minimum ten-fold improvement in the number of uncorrelated patterns that can be stored and retrieved, significantly surpassing the traditional capacity limit for traditional HNNs. For correlated patterns, depending on their average correlation, up to 50 times more patterns can be stored compared to traditional HNNs. To test the system's robustness, the benchmark testing is performed on MNIST handwritten digit patterns. The results show a 5.5 times improvement in the pattern storage along with the retrieval of cleaner and less noisy patterns. These results highlight the potential of nonlinear optical DAMs for practical applications in challenging big-data optimization, computer vision and graph network tasks.

physics.optics↗

arXiv2025

Multi Moire Networks in Engineered Lateral Hetero-Bilayers: Programmable Phononic Reconfiguration and Second Harmonic Generation

Moire engineering in two-dimensional transition metal dichalcogenides enables access to correlated quantum phenomena. Realizing such effects demands simultaneous control over twist angle and material composition to modulate phonons, excitons, and their interactions. However, most studies rely on exfoliated flakes, limiting scalability and systematic exploration. Here, we demonstrate a scalable multi-moire network by vertically stacking CVD-grown monolayer lateral heterostructures. Signatures of moire non-rigidity, including phonon frequency softening, linewidth broadening, and strain localization, are attributed to two lattice relaxation modes; rotational reconstruction and volumetric dilation. Micro-angle-resolved photoemission spectroscopy reveals that interfacial orbital interactions modulate interlayer coupling. At aligned angles, molybdenum diselenides exhibit reduced valley polarization and Davydov splitting, indicating strain-induced symmetry breaking and chiral phonon effects. Notably, SHG modulation was obderved with variation in twist angle due to lower coherence and band-offset-driven phase delay. First-principles calculations support these findings. This work provides a route to programmable, scalable multi-moire platforms for opto-straintronics, quantum sensing, and on-chip photonics.

cond-mat.mes-hall↗

arXiv2025

Layered semiconductors integrated with polyimide thin films for high-quality valleytronic and quantum-photonic systems

Dielectric integration of layered semiconductors is a prerequisite for fabricating high-quality optoelectronic, valleytronic, and quantum-photonic devices. While hexagonal boron nitride (hBN) is the current benchmark dielectric, exploration of the most suitable dielectric materials covering the complete substrates continues to expand. This work demonstrates the formation of high optical-quality excitons in two widely explored layered semiconductors, WSe$_2$ and WS$_2$, integrated into polyimide (PI) thin films of thicknesses $\approx$500 nm. The photoluminescence (PL) studies at $T$ = 296 K show the formation of neutral excitons $\left(X^0\right)$ and trions in fully-PI-encapsulated 1L-WSe$_2$ with 2-sigma ($2\sigma$) spatial-inhomogeneity of 4.5 (3.4) meV in $X^0$ emission energy (linewidth), which is $\approx$1/3rd (1/5th), respectively, that of inhomogeneity measured in fully-hBN-encapsulated 1L-WSe$_2$. A smaller $2\sigma$ of 2.1 (2.3) meV in $X^0$ emission energy (linewidth) has been shown for fully-PI-encapsulated 1L-WS$_2$. Polarization-resolved and excitation power-dependent PL measurements of PI-isolated 1L-TMDs at $T$ = 4 K further reveal formations of high-quality neutral-biexcitons and negatively-charged biexcitons, with degrees of valley-polarization up to 21$\%$ under non-resonant excitation. Furthermore, the fully-PI-encapsulated 1L-WSe$_2$ also hosts single quantum emitters with narrow linewidths and high-spectral stability. This work indicates that PI thin films may serve the purpose of high-quality dielectric material for integrating the layered materials on a wafer scale.

cond-mat.mes-hall↗

arXiv2025

Superheavy Nuclei and the Changing Face of Nuclear Magicity

Using a relativistic mean field formalism, we analyzed the magic number sequence for finite nuclei in the superheavy valley. The result for the IOPB-I parameter set is compared with the well-known NL3 force. The magic numbers obtained from IOPB-I and NL3 interactions are found to be similar. Analysing the single-particle levels and the number of nucleons occupied in it, we find the close shell sequence as 2, 8, 18, 34, 50, 58, 80, 82, 92, 114, 120, 120, 138, 164, 172, 184 and 198 for the $^{318}{120}$ mass region. Again, with a careful inspection, we noticed large shell gaps at nucleon numbers 2, 8, 18, 34, 50, 58, 80, 92, 120, 138, 164, 172, 184, and 198, which may be considered as the magic number sequence for the superheavy nuclei. This change may be due to the shape change of the nuclear potential as compared to the stability valley.

nucl-th↗