Search arXiv⌕ Search

arXiv subjects

Jitendra Kumar

Publications and source records attributed to Jitendra Kumar.

At least 19 recordsLinked to original sources
arXiv2026

Optical Characteristics and Frequency Shifts of Quartic Wormholes in $f(T,τ)$ Gravity

This work explores traversable wormhole solutions characterized by a quartic inverse-power density profile within $f(T,τ)$ gravity. A specific inverse-power density profile, characterized by proportional to $1/r^4$ is introduced to establish the corresponding exact form of the shape function. The derived configuration exhibits all the necessary geometrical features of a traversable wormhole, such as throat formation, flaring-out behavior, and acceptable asymptotic characteristics. To gain further insight into the matter sector, we analyze the corresponding radial profiles of the energy density and pressure components. The throat region is characterised by negative radial pressure and null energy condition violation, both of which are relevant to the exotic matter content of the wormhole solution. To examine the lensing characteristics of the obtained spacetime, we employ the Gauss-Bonnet formalism to evaluate the gravitational deflection angle. Our findings indicate that the parameters $γ$ and $ρ_0$ exert a considerable influence on the resulting light trajectories. The perturbative behavior of the obtained configuration is investigated within the scalar and electromagnetic field sectors. Subsequently, a novel electromagnetic potential is introduced to examine its influence on the radial variation of the red-blue shifts characteristics. Our findings establish a unified picture of the geometrical, perturbative, and optical characteristics of quartic density-supported wormholes.

gr-qc↗
arXiv2026

Entanglement metrics for a $B$-meson system

The $B$-factory experiments operate at electron-positron colliders with beam energies precisely tuned for optimal $B^{0}\text{-}\bar{B}^{0}$ meson pair production. These $B^{0}\text{-}\bar{B}^{0}$ meson pairs are entangled and offer a unique opportunity to explore quantum correlations and examine the foundational aspects of quantum mechanics. In this work, we present a comprehensive analysis of entanglement metrics in the $B$-meson system within the framework of open quantum systems, which introduces decoherence due to interactions with the environment. We examine several distinct entanglement measures, each highlighting different facets of quantum entanglement and its sensitivity to decoherence effects. We further analyze the impact of decoherence by systematically varying the decoherence parameter across different scales

hep-ph↗
arXiv2026

Compositionally Engineered Non-Equimolar LaCoO$_3$-Based High-Entropy Perovskites with Enhanced Thermoelectric Performance

The decoupling of phonon and electron transport remains a central challenge in the development of high-performance TE materials. Configurational-entropy maximization is widely invoked as a design principle for decoupling phonon and electron transport. This study investigates compositionally engineered LaCoO$_3$-based high-entropy perovskites to determine whether thermoelectric transport can be improved by tuning cation identity and concentration rather than maximizing configurational entropy. La$_{1-x}$Sr$_x$(CoFeMnCrNi)O$_3$ ($x=0.0$--$0.2$) and selected non-equimolar A- and B-site perovskite compositions were prepared by solid-state reaction. The obtained samples are predominantly single-phase, as confirmed by X-ray diffraction and Rietveld refinement, consistent with the calculated size-disorder parameters. Multication disorder introduces substantial mass and strain-field fluctuations that promote phonon scattering. All samples exhibit p-type, thermally activated electrical transport consistent with adiabatic SPH. Sr substitution progressively reduces the hopping barrier and $ρ$, whereas non-equimolar B-site engineering partially recovers electrical transport while retaining low k. La$_{0.9}$Sr$_{0.1}$Co$_{0.4}$Cr$_{0.3}$Ni$_{0.1}$Fe$_{0.1}$Mn$_{0.1}$O$_3$, featuring a Co-rich and Cr-rich B-site composition, combines Co-associated mixed valence and spin-state degeneracy that sustain a large alpha with Cr-mediated control of carrier concentration and a reduced polaron hopping barrier of 0.19eV. This composition achieves a power factor of 40--43 $μ$W/(m K$^2$) and a $zT\sim0.072$ at 1100K, approximately 2.7 times that of its equimolar analogue. These results demonstrate that targeted cation chemistry and mass contrast, rather than configurational-entropy maximization alone, provide an effective strategy for balancing electronic and phonon transport in multicomponent oxide TE.

cond-mat.mtrl-sci↗
arXiv2026

Noise-Resilient Detection of Neuronal Spikes by a Hopf-Bifurcation Device

Detecting weak transient signals in noise is a persistent challenge in sensing, communication, and electrophysiology. Here, we demonstrate a physical weak-signal detector based on a semiconductor negative differential resistance (NDR) device operated near a Hopf bifurcation. A coherent input that persists over the response time of the dynamical system can drive a transition between quiescent and oscillatory states, whereas faster stochastic fluctuations are largely suppressed. This nonlinear transformation converts a weak analog threshold crossing into all-or-none voltage spikes and therefore provides asynchronous signal detection without a reference clock. Using a modulated photovoltaic signal, we detect a weak frequency component of 100 Hz as a demonstration, at an input signal-to-noise amplitude ratio as low as 1/500, and reliably recover it in the output spectrum. We then apply the same principle to neuronal multisite extracellular recordings. After standard band-pass filtering, the raw microelectrode signal is transformed by the NDR dynamics, enhancing the distinction between neuronal spikes and background fluctuations. The resulting detected spike times agree closely with those obtained using a traditional spike-detection pipeline. These results establish bifurcation-engineered NDR dynamics as a compact hardware approach to noise-resilient signal discrimination and event-based analog-to-digital conversion that will be useful for neuroprosthetic devices.

physics.app-ph↗
arXiv2026

Machine Learning Based ROI Segmentation for Beam Imaging Diagnostics at Accelerators

The European XFEL accelerator produces high-brightness X-ray pulses using relativistic electron beams. Beam characterization is performed at multiple diagnostic stations using scintillator screens, where beam images are analyzed to extract key parameters such as emittance, energy spread, and current profile. Accurate detection of the region of interest in these images is essential for reliable beam diagnostics and stable accelerator operation. Conventional methods, such as bounding-box-based beam localization, can become less reliable for complex or non-ideal beam profiles, such as low-intensity signals, tilted or streaked beams, and multiple beam structures. In this work, we develop machine learning-based approaches for region of interest detection directly from beam images. These ML methods adapt to variations in beam shape and intensity and enable more precise, pixel-level identification of beam regions. Experimental results demonstrate improved robustness and accuracy in challenging conditions compared to traditional techniques. The proposed methodology is validated at the European XFEL and is broadly applicable to image-based beam diagnostics across accelerator facilities.

physics.acc-ph↗
arXiv2026

Unraveling the Roles of Shallow, Deep and Auger Trapping in Charge Carrier Recombination in Triple-Cation Perovskites

Understanding charge-carrier recombination in metal halide perovskites is essential for accurately identifying the factors limiting solar cell efficiency, yet it remains challenging due to the interplay of multiple competing processes. Here, we combine time-resolved photoluminescence and excitation dependent photoluminescence quantum yield measurements over a wide range of fluences and repetition rates to investigate recombination dynamics in triple-cation perovskite thin films. By jointly analyzing these multidimensional datasets, we develop a unified model that quantitatively reproduces both photoluminescence decays and absolute quantum yields across all excitation conditions. Our results reveal the coexistence of deep and shallow traps, as well as a second-order nonradiative recombination pathway attributed to Auger-assisted trapping. Importantly, this mechanism dominates under one-sun illumination, making it a critical limiting factor for photovoltaic performance. These findings provide a comprehensive framework for understanding recombination in perovskites and highlight the importance of higher-order defect-mediated processes in determining their efficiency.

cond-mat.mtrl-sci↗
arXiv2026

Nonlocal Advantage of Quantum Coherence in Top Quarks

There is a growing interest in investigating top-quark systems using tools from quantum information theory. A key peculiarity of the top quark is that it decays before hadronization or spin decorrelation occurs, thereby preserving its spin information. This unique property enables direct access to spin correlations, making the top quark an ideal candidate for probing fundamental quantum correlations in high-energy physics processes. A wide range of concepts from quantum information theory, such as entanglement, Bell nonlocality, quantum steering, quantum discord, and fidelity, have been investigated in this context. Several of these measures have been employed as diagnostic tools to test the Standard Model and to search for possible signatures of physics beyond. However, the \textit{nonlocal advantage of quantum coherence} (NAQC) has remained largely unexplored in this context. In this work, we present a detailed investigation of the NAQC in top quark pair production. We employ two complementary NAQC measures based on the $l_{1}$-norm and the relative entropy of coherence. We also study the effect of angular averaging on these measures and assess the sensitivity of current LHC spin-correlation measurements to NAQC. Our findings reveal rich coherence structures and highlight NAQC as potentially a novel and complementary quantum signature in high-energy physics systems.

hep-ph↗
arXiv2026

Probing Signatures of Right-Handed Neutrinos via $b \to s ν\barν$ Decays

The recent Belle II measurement of $\mathcal{B}(B^+\to K^+ν\barν)$, which deviates from the Standard Model prediction, provides a strong motivation to search for New Physics in $b\to sν\barν$ transitions. We perform a model-independent analysis within the low-energy effective theory, focusing on dimension-six vector operators involving right-handed neutrinos. Using the Belle II measurement of $\mathcal{B}({B\to Kν\barν})$ together with the upper limit on $\mathcal{B}({B\to K^{*}ν\barν})$, we constrain the new physics interactions. We study the correlation of $\mathcal{B}({B\to Kν\barν})$ with $\mathcal{B}({B\to K^{*}ν\barν})$, $\mathcal{B}({B_s\toϕν\barν})$, $\mathcal{B}({Λ_b\toΛν\barν})$, and the longitudinal polarization fraction $F_L^{K^*}$. We find sizeable enhancements in all branching fractions studied, while $F_L^{K^*}$ offers complementary sensitivity to the underlying RHN interaction structure. These results provide testable signatures of right-handed neutrino interactions at Belle~II and LHCb, and at the future FCC-ee experiment.

hep-ph↗
arXiv2026

Flexible PDMS/La$_{0.7}$Sr$_{0.3}$MnO$_3$/MWCNT Composite Thin Films for Multifunctional Temperature and Magnetic Sensing Electronic Skin

The development of multifunctional electronic skin (e-skin) requires materials that combine mechanical flexibility with responsiveness to multiple stimuli. In this work, a flexible PDMS/La0.7Sr0.3MnO3 (LSMO)/MWCNT composite thin film was fabricated via solution casting, using LSMO powder synthesized by a solid-state reaction method. Structural and spectroscopic analyses confirm the formation of single-phase rhombohedral LSMO and successful incorporation of PDMS, LSMO, and MWCNT components. The composite exhibits a smooth and uniform surface morphology, along with significantly enhanced thermal stability, retaining ~70% mass at elevated temperatures. Electrical measurements reveal thermally activated resistivity behavior, enabling temperature sensing functionality. Additionally, the composite shows a notable decrease in resistance under an applied magnetic field, exhibiting magnetoresistance due to spin-dependent transport in the LSMO phase. Mechanical testing indicates elastomeric behavior with a maximum load of ~0.49 N and stretchability of ~26%, along with ductile deformation characteristics. The multifunctional sensing properties arise from the synergistic interaction between the conductive MWCNT network and magnetically active LSMO within the flexible PDMS matrix. Overall, the composite demonstrates a unique combination of thermal stability, mechanical flexibility, and dual sensing capability, making it a promising material for next-generation e-skin applications.

cond-mat.mtrl-sci↗
arXiv2026

Intercomparison of Machine Learning Algorithms for Remote Sensing-based In-season Crop Mapping

In-season crop type mapping is critical for food security in the face of increasingly extreme climate-related threats to crops. Currently, the USDA Cropland Data Layer provides crop type labels at 30m resolution and is available the February after harvest, but no product exists that maps crop types before harvest with satisfactory accuracy that would allow emergency managers to respond to crop threats in near real time. Furthermore, the relative advantages of a wide range of algorithms have not been evaluated in a way that accounts for interannual variability, until this study. Here, Harmonized Landsat-Sentinel surface reflectance imagery time series and crop rotation history information are combined to map corn in Iowa and almonds in California at 30m resolution accurately by early June in unseen years, with robust quantification of uncertainty due to phenology and crop distribution. Thousands of model configurations across ten machine learning algorithms were compared using a year-wise cross-validation and a suite of metrics. Hyperparameter search revealed Support Vector Machines to be the most successful algorithm overall, with a mean F1 score of 0.74 (0.59) across five unseen validation years for almonds by early June in California (corn by early June in Iowa). Interannual variation was a large source of uncertainty, but patterns showed the potential to further improve performance with ensemble approaches or ancillary data. Future work may extend these methods to include multiclass maps of all crop types, CONUS-wide maps, and in-season crop yield forecasting.

cs.LG↗
arXiv2026

Indian Wedding System Optimization (IWSO): A Novel Socially Inspired Metaheuristic with Operational Design and Analysis

This paper presents a novel population-based metaheuristic, Indian Wedding System Optimization (IWSO), inspired by the socio-cultural dynamics of traditional Indian weddings. IWSO models the matchmaking process driven by collaboration among families, candidates, and matchmakers as a guided, selective search framework for solving complex optimization problems. The algorithm introduces two key innovations: (i) a matchmaker-guided influence strategy, where elite solutions direct the evolution of weaker candidates, enhancing convergence without external parameters; and (ii) an adaptive elimination and reinitialization mechanism that maintains diversity and prevents premature convergence by replacing underperforming individuals. IWSO employs a weighted multi-objective fitness function and analytically derived time and space complexity, benchmarked against existing optimization approaches such as Genetic Algorithm (GA), Partical Swarm Optimization (PSO), Differential Evolution (DE), Cuckoo Search (CS), etc. Extensive experiments on benchmark high-dimensional and multimodal test functions demonstrate superior performance of IWSO in terms of convergence speed, solution quality, and robustness.

cs.NE↗
arXiv2025

Configurational Entropy-Driven Phase Stability and Thermal Transport in Rock-Salt High-Entropy Oxides

High-entropy oxides (HEOs) offer a unique platform for exploring the thermodynamic interaction between configurational entropy and enthalpy in stabilizing complex solid solutions. In this study, a series of rock-salt structured oxides with varying configurational entropy, ranging from binary to multi-cation systems, to elucidate the competing roles of enthalpy and entropy in phase stabilization is investigated. Compositions including (Ni$_{0.8}$Cu$_{0.2}$)O to(NiCuZnCoMg)$_{0.9}$A$_{0.1}$O (A = Li, Na, K) were synthesized and their stuctural, microstructural and thermal properties have been discussed. X-ray diffraction combined with thermal cycling confirms that even a medium configurational entropy ($\sim$ 0.95R) can induce single-phase behavior stabilized by configurational entropy ($ΔS_{conf}$), challenging the traditional threshold of $1.5\,R$. High-resolution TEM and EDS mapping reveal nanocrytalline features and homogeneous elemental distribution respectively, while XPS analysis confirms divalent oxidation states. A strong coupling between high configurational entropy with thermal conductivity ($κ$) has been observed. First, a sharp decrease in $κ$ with increasing $ΔS_{conf}$ is seen and then decomposed samples (while cooling) show high $κ$, demonstrating the role of $ΔS_{conf}$ on $κ$. Furthermore, Li-doped compositions exhibit improved thermoelectric performance, with a maximum figure of merit ($zT$) of $\sim$0.15 at 1173K\, driven by low thermal conductivity and favorable carrier transport. The results highlight that configurational entropy, even at intermediate values, plays a significant role in stabilizing disordered single-phase oxides and tailoring phonon transport.

cond-mat.mtrl-sci↗
arXiv2025

Multiplication operators on amalgam of $l^{q), θ}$ and $L^{p}$

We define the grand amalgam Lebesgue function space $l^{q), θ}(L^p),$ and study the fundamental structural properties of the space, including completeness. Then we define the small Lebesgue sequence space and study its function space properties. Furthermore, we prove a version of the Hölders inequality on the frame work of these spaces. Finally, we study the multiplication operator in the setting of these spaces.

math.FA↗
arXiv2025

A network-based approach to measure granule size distribution for discrete element modeling of granulation

Drum granulation is a size enlargement process where granular material is agitated with a liquid binder to form larger size granules. Discrete element modeling is increasingly being used to better understand and investigate the granulation process. However, unlike experiments the measurement of granule size within a DEM framework often necessitates an explicit quantitative definition of a granule and a corresponding granule identification method. In this work, we show that the existing definitions and the associated methods in literature are ineffective at identifying granules for dense flows such as during drum granulation. We propose an improved definition and granule identification method based on community-detection used in network science literature. The proposed method better identifies granules in a drum granulator as benchmarked against liquid-settling. We also vary granulation process parameters like liquid content and fill level and study their effect on the cumulative granule size distribution attained after drum granulation. We find that the existing granule-identification methods fail to reproduce the well-known effects of process parameters on the cumulative granule size distribution. The proposed method, based on community detection, reproduces the effects with better accuracy.

cond-mat.soft↗
arXiv2025

Variational Autoencoders-based Detection of Extremes in Plant Productivity in an Earth System Model

Climate anomalies significantly impact terrestrial carbon cycle dynamics, necessitating robust methods for detecting and analyzing anomalous behavior in plant productivity. This study presents a novel application of variational autoencoders (VAE) for identifying extreme events in gross primary productivity (GPP) from Community Earth System Model version 2 simulations across four AR6 regions in the Continental United States. We compare VAE-based anomaly detection with traditional singular spectral analysis (SSA) methods across three time periods: 1850-80, 1950-80, and 2050-80 under the SSP585 scenario. The VAE architecture employs three dense layers and a latent space with an input sequence length of 12 months, trained on a normalized GPP time series to reconstruct the GPP and identifying anomalies based on reconstruction errors. Extreme events are defined using 5th percentile thresholds applied to both VAE and SSA anomalies. Results demonstrate strong regional agreement between VAE and SSA methods in spatial patterns of extreme event frequencies, despite VAE producing higher threshold values (179-756 GgC for VAE vs. 100-784 GgC for SSA across regions and periods). Both methods reveal increasing magnitudes and frequencies of negative carbon cycle extremes toward 2050-80, particularly in Western and Central North America. The VAE approach shows comparable performance to established SSA techniques, while offering computational advantages and enhanced capability for capturing non-linear temporal dependencies in carbon cycle variability. Unlike SSA, the VAE method does not require one to define the periodicity of the signals in the data; it discovers them from the data.

cs.LG↗
arXiv2025

Investigating 1-Bit Quantization in Transformer-Based Top Tagging

The increasing scale of deep learning models in high-energy physics (HEP) has posed challenges to their deployment on low-power, latency-sensitive platforms, such as FPGAs and ASICs used in trigger systems, as well as in offline data reconstruction and processing pipelines. In this work, we introduce BitParT, a 1-bit Transformer-based architecture designed specifically for the top-quark tagging method. Building upon recent advances in ultra-low-bit large language models (LLMs), we extended these ideas to the HEP domain by developing a binary-weight variant (BitParT) of the Particle Transformer (ParT) model. Our findings indicate a potential for substantial reduction in model size and computational complexity, while maintaining high tagging performance. We benchmark BitParT on the public Top Quark Tagging Reference Dataset and show that it achieves competitive performance relative to its full-precision counterpart. This work demonstrates the design of extreme quantized models for physics applications, paving the way for real-time inference in collider experiments with minimal and optimized resource usage.

hep-ph↗
arXiv2025

Land Cover Changes Cause Increased Losses during Photosynthetic Extremes

Human-induced carbon dioxide (CO2) emissions, primarily from fossil fuel combustion and changes in land use and land cover (LULCC), are a key contributor to climate change. As the climate warms, extreme events such as heatwaves, droughts, and wildfires have become more frequent and are projected to intensify throughout the 21st century. These escalating extremes are likely to further disrupt vegetation productivity, known as gross primary production (GPP), and reduce the ecosystem's capacity to absorb carbon. In this study, we employ a global Earth system model to assess how (a) CO2 emissions alone and (b) CO2 combined with LULCC influence the severity, frequency, and duration of GPP extremes. Our results show that negative GPP extremes periods of unexpectedly low carbon uptake are increasing more rapidly than positive extremes, especially under LULCC scenarios. The primary climate factor driving these extremes is soil moisture variability, which is influenced by fluctuations in both precipitation and temperature. The delayed responses of GPP to different climate drivers depend on the specific driver and geographical region. Overall, the highest incidence of GPP extremes arises from the combined influence of water stress, temperature anomalies, and fire-related disturbances.

physics.ao-ph↗
arXiv2025

A Comprehensively Adaptive Architectural Optimization-Ingrained Quantum Neural Network Model for Cloud Workloads Prediction

Accurate workload prediction and advanced resource reservation are indispensably crucial for managing dynamic cloud services. Traditional neural networks and deep learning models frequently encounter challenges with diverse, high-dimensional workloads, especially during sudden resource demand changes, leading to inefficiencies. This issue arises from their limited optimization during training, relying only on parametric (inter-connection weights) adjustments using conventional algorithms. To address this issue, this work proposes a novel Comprehensively Adaptive Architectural Optimization-based Variable Quantum Neural Network (CA-QNN), which combines the efficiency of quantum computing with complete structural and qubit vector parametric learning. The model converts workload data into qubits, processed through qubit neurons with Controlled NOT-gated activation functions for intuitive pattern recognition. In addition, a comprehensive architecture optimization algorithm for networks is introduced to facilitate the learning and propagation of the structure and parametric values in variable-sized QNNs. This algorithm incorporates quantum adaptive modulation and size-adaptive recombination during training process. The performance of CA-QNN model is thoroughly investigated against seven state-of-the-art methods across four benchmark datasets of heterogeneous cloud workloads. The proposed model demonstrates superior prediction accuracy, reducing prediction errors by up to 93.40% and 91.27% compared to existing deep learning and QNN-based approaches.

cs.LG↗