arXiv subjects

Arindam Mukherjee

Publications and source records attributed to Arindam Mukherjee.

8 recordsLinked to original sources

arXiv2026

Doping-controlled topological superconducting transition in misfit layer compounds

Achieving topological superconductivity is a key goal in quantum physics, offering a path to fault-tolerant quantum computers. A central challenge in this field is to continuously drive a material through a topological quantum phase transition to directly observe the evolution from trivial to topological superconductivity. However, finding a robust platform that allows such extreme and precise tuning remains a challenge. Here, we demonstrate a doping-controlled phase transition from a conventional to a topological superconducting state in the bulk misfit layer compound (LaxPb1-xSe)1.14(NbSe2)2. We reveal a non-monotonic phase diagram characterized by two distinct superconducting regimes separated by a non-superconducting phase at a precise doping. In the highly doped regime, the superconducting phase becomes remarkably sensitive to non-magnetic disorder, and orientation-selective in-gap modes emerge at atomic step edges. Supported by Bogoliubov-de Gennes calculations, these emergent spatial signatures are consistent with a time-reversal-symmetric crystalline-topological order parameter. Our results establish misfit compounds as a platform to engineering topological superconductivity.

cond-mat.supr-con↗

arXiv2026

Doping tunable charge density waves in misfit layer compounds

The ability to tune charge density waves (CDWs) through external control knobs, such as doping, pressure or strain is crucial for exploring the phase diagram of two dimensional (2D) or quasi-2D materials. Yet, controlling CDWs critical temperature and ordering vector remains a challenge for current experimental techniques. In this work, we establish misfit layer compound heterostructures as a reliable platform to manipulate CDWs in transition metal dichalcogenides. By combining ab initio calculations with low-temperature scanning tunneling microscopy, we show how to achieve doping tunable control over NbSe2 CDW by chemically alloying in the rocksalt subunit. Crucially, we prove that tuning the La Pb ratio in the misfit family (LaxPb1xSe)1.14(NbSe2)2 enables stabilization of different CDW orders, such as 2x2 or 3x3 patterns, and even coexisting phases. This work paves the way for engineering transition metal dichalcogenides with tailored charge density waves within misfit heterostructures.

cond-mat.mtrl-sci↗

arXiv2025

Moir\'e pattern multiplicity driven by electronic effects in two-dimensional CrCl3/Au heterostructures

Moir\'e patterns are a central motif in van der Waals heterostructures arising from the superposition of two-dimensional (2D) incommensurate lattices. These patterns reveal a wealth of correlated effects, influencing electronic, magnetic, and structural phenomena. While diffraction techniques typically resolve multiple moir\'e wave-vectors corresponding to the incommensurate nature of the underlying lattices, Scanning Tunneling Microscopy (STM) often reveals only a dominant superperiod. In this work, we address this apparent discrepancy through an STM study of a twisted monolayer of CrCl3 on Au(111). We observe the coexistence of several moir\'e patterns at a fixed twist angle, whose relative intensity depends on the tunneling bias. Fourier analysis of STM data uncovers hidden higher-order moir\'e components not visible in STM topographic images, while spectroscopy maps reveal that the spectral weight of each pattern varies with electron energy. Our results establish that STM selectively probes on the same area distinct moir\'e modulations depending on electronic confinement, providing a unified framework that reconciles real space and reciprocal space observations of complex moir\'e superstructures.

cond-mat.other↗

arXiv2025

Shortest Paths in a Weighted Simplicial Complex

Simplicial complexes are extensively studied in the field of algebraic topology. They have gained attention in recent time due to their applications in fields like theoretical distributed computing and simplicial neural networks. Graphs are mono-dimensional simplicial complex. Graph theory has application in topics like theoretical computer science, operations research, bioinformatics and social sciences. This makes it natural to try to adapt graph-theoretic results for simplicial complexes, which can model more intricate and detailed structures appearing in real-world systems. Though seemingly obvious, we did not find any previous work that looked into this prospect of simplicial complexes. In this article, we define the concept of weighted simplicial complex and $d$-path in a simplicial complex. Both these concepts have the potential to have numerous real-life applications. We start by adapting the Depth-First Search and Breadth-First Search algorithms for our setup. Next, we provide two novel algorithms to find the shortest paths in a weighted simplicial complex. The core principles of our algorithms align with those of Dijkstra$^\prime$s algorithm and Bellman-Ford algorithm for graphs. Hence, this work lays a building block for the sake of integrating graph-theoretic concepts with abstract simplicial complexes.

cs.DM↗

arXiv2025

Lightweight Object Detection Using Quantized YOLOv4-Tiny for Emergency Response in Aerial Imagery

This paper presents a lightweight and energy-efficient object detection solution for aerial imagery captured during emergency response situations. We focus on deploying the YOLOv4-Tiny model, a compact convolutional neural network, optimized through post-training quantization to INT8 precision. The model is trained on a custom-curated aerial emergency dataset, consisting of 10,820 annotated images covering critical emergency scenarios. Unlike prior works that rely on publicly available datasets, we created this dataset ourselves due to the lack of publicly available drone-view emergency imagery, making the dataset itself a key contribution of this work. The quantized model is evaluated against YOLOv5-small across multiple metrics, including mean Average Precision (mAP), F1 score, inference time, and model size. Experimental results demonstrate that the quantized YOLOv4-Tiny achieves comparable detection performance while reducing the model size from 22.5 MB to 6.4 MB and improving inference speed by 44\%. With a 71\% reduction in model size and a 44\% increase in inference speed, the quantized YOLOv4-Tiny model proves highly suitable for real-time emergency detection on low-power edge devices.

cs.CV↗

arXiv2025

Efficient Edge Deployment of Quantized YOLOv4-Tiny for Aerial Emergency Object Detection on Raspberry Pi 5

This paper presents the deployment and performance evaluation of a quantized YOLOv4-Tiny model for real-time object detection in aerial emergency imagery on a resource-constrained edge device the Raspberry Pi 5. The YOLOv4-Tiny model was quantized to INT8 precision using TensorFlow Lite post-training quantization techniques and evaluated for detection speed, power consumption, and thermal feasibility under embedded deployment conditions. The quantized model achieved an inference time of 28.2 ms per image with an average power consumption of 13.85 W, demonstrating a significant reduction in power usage compared to its FP32 counterpart. Detection accuracy remained robust across key emergency classes such as Ambulance, Police, Fire Engine, and Car Crash. These results highlight the potential of low-power embedded AI systems for real-time deployment in safety-critical emergency response applications.

cs.CV↗

arXiv2025

YOLOv5-Based Object Detection for Emergency Response in Aerial Imagery

This paper presents a robust approach for object detection in aerial imagery using the YOLOv5 model. We focus on identifying critical objects such as ambulances, car crashes, police vehicles, tow trucks, fire engines, overturned cars, and vehicles on fire. By leveraging a custom dataset, we outline the complete pipeline from data collection and annotation to model training and evaluation. Our results demonstrate that YOLOv5 effectively balances speed and accuracy, making it suitable for real-time emergency response applications. This work addresses key challenges in aerial imagery, including small object detection and complex backgrounds, and provides insights for future research in automated emergency response systems.

cs.CV↗

arXiv2009

Study of low energy hadronic interaction models based on BESS observed cosmic ray proton and antiproton spectra at medium high altitude

We study low energy hadronic interaction models based on BESS observed cosmic ray proton and antiproton spectra at medium high altitude. Among the three popular low energy interaction models, we find that FLUKA reproduces results of BESS observations on secondary proton spectrum reasonably well over the whole observed energy range, the model UrQMD works well at relatively higher energies whereas spectrum obtained with GHEISHA differs significantly from the measured spectrum. Simulated antiproton spectrum with FLUKA, however, exhibits significant deviations from the BESS observation wheras UrQMD and GHEISHA reproduce the BESS observations within the experimental error.

astro-ph.HE↗